NYC Data Science Academy
NYC Data Science Academy offers 12 week data science bootcamps. In these programs, students learn beginner and intermediate levels of Data Science with R, Python, Hadoop & Spark, Github, and SQL as well as the most popular and useful R and Python packages like XgBoost, Caret, dplyr, ggplot2, Pandas, scikit-learn, and more. Once the learning foundation has been set, students work on multiple projects through the bootcamp. Along the way, students are assisted in preparing for employment process through resume review and interview preparation. The program distinguishes itself by balancing intensive lectures with real world project work, and by the breadth of its curriculum. Throughout the program students work alone and in teams to create at least four projects that are showcased to employers through multiple channels; private on-campus hiring partner events, student blogs, meetups, and filmed presentations.
NYC Data Science Academy works closely with hiring partners and recruiting firms to create a pipeline of interest for its students. Ideal applicants should have a Masters or PhD degree in Science, Technology, Engineering or Math or equivalent experience in quantitative science or programming. Candidates with BA’s who have appropriate experience are also considered.
Recent NYC Data Science Academy News
- December 2016 Coding Bootcamp News Roundup
- Alumni Spotlight: Kelly Mejia Breton of NYC Data Science Academy
- Alumni Spotlight: Arda Kosar of NYC Data Science Academy
Recent NYC Data Science Academy Reviews: Rating 4.71
New York City
12 Week Full Time Hadoop & Spark Bootcamp
Students entering with some programming knowledge will become adept at using the Hadoop system to solve “big data” problems. Starting with MapReduce using both Python and Java (which we will teach in week 2), we will explore the structure and components of the Hadoop ecosystem. The course will particularly emphasize the use of Hadoop tools to analyze large volumes of data. Student work will include in-class exercises and a class project designed in concert with the student.
- Contingency Fee
- Financing available through Pave
- Minimum Skill Level
- The applicant should be technically inclined, have some programming experience, be familiar with Linux/Unix, and have an interest in problems involving large datasets.
- Placement Test
- Prep Work
- Data Science with R - Data Analytics and Visualization level; Data Science with Python - Data Analytics and Visualization level
Part Time - Data Science with R: Machine Learning (Weekends)
The class is 35 hours of classroom guidance with an optional 3-week showcase project of students’ own choices and optional presentation of their projects. This class introduces a number of statistical models for supervised and unsupervised learning using R programming language. The goal is to understand the concepts, methods, and applications of the general predictive modeling and unsupervised learning and how they are implemented in the R language environment. A selection of important models (e.g. tree-based models, support vector machines) will be introduced in an intuitive manner to illustrate the process of training and evaluating models.
Part Time - Data Science with R: Data Analysis and Visualization (Weekends)
This intensive Data Science with R – Beginner Level course being offered by NYC Data Science Academy is a five week course that will introduce you to the wonderful wold of R and provide you with an excellent understanding of the language that leaves you with a firm foundation to build upon.Why R is important: R is a powerful, comprehensive, and dynamic…
- Minimum Skill Level
12 Week Full Time Data Science Bootcamp
In this program students will learn the modern data analytic techniques and mastered the requisite skills, such as Python and R programming languages as well as Hadoop, to address real-world data science problems. Throughout the program students work alone and in teams to create at least five projects that are showcased to employers. Finally, students spend two weeks on a Capstone Project, with assistance from instructors. During the final weeks, students will have assistance in finding jobs through resume review, interview preparation, and connection to our hiring partners.
- Contingency Fee
- Payment Plan
- Payment plans available to qualified candidates
- Limited number of scholarships available to qualified candidates
- Minimum Skill Level
- Ideal applicants should have a Masters or PhD degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
- Prep Work
Part Time - Big Data with Hadoop and Spark (Evenings)
This class is a 6-week evening program with hands-on introduction to the Hadoop and Spark ecosystem of Big Data technologies. The emphasis in this course is on learning several of the major components of Apache Hadoop – HDFS, MapReduce, Hive, Pig, Streaming – by doing exercises of increasing complexity. Programming will be done in Python. Students are expected to be familiar with using an operating system from the command line; knowledge of Python is helpful; the material in Learn Python the Hard Way is sufficient background knowledge. The course format is mixed lecture/lab. Students will need to bring their own laptops to connect to our server; instructions will be provided ahead of time as to how to install any required software.
- Minimum Skill Level
- Students are expected to be familiar with using an operating system from the command line; knowledge of Python is helpful
Part Time - Data Science with Python: Machine Learning (Weekends)
This class will introduce you a wide range of machine learning tools in Python. The main focus is on the concepts, methods, and applications of the general predictive modeling and unsupervised learning and how they are implemented in the Python language environment. The goal is to understand how to use these tools to solve real world problems. After this course you will be able to carry out your experiments with the public available algorithms or develop your own algorithm.
- Minimum Skill Level
- Knowledge of Python programming Able to munge, analyze, and visualize data in Python
Introductory Python (Evenings)
This is a class for computer-literate people with no programming background wishing to learn basic Python programming. The course is aimed at those needing to do “data wrangling” – manipulating downloaded files to make them amenable to analysis. We concentrate on language basics such as list and string manipulation, control structures, and simple I/O, and introduce modules for downloading data from the web.
- Minimum Skill Level
- No experience required.
Machine Learning in Finance
This course is a dense presentation of machine learning (ML) tools used in financial risk management, portfolio management, and trading. Ten classes are offered: two on risk management, two on loan portfolio management, three on portfolio optimization, and three on high-frequency trading. The risk classes cover the risk measurement of financial assets using distribution fitting, copulas, PCA, and splines. The loan portfolio management classes cover risk estimation and backtesting using logistic regression, regularization, clustering methods, and the applied statistics concepts such as parameter and process risk. Kaggle competitions for loan portfolios which used tree-based algorithms for predictions are also reviewed. The classes on portfolio optimization introduce classic theories for asset return estimation and their extensions (multi-factor models) while using unsupervised & supervised ML methods to verify & derive new factors; modern portfolio theory using constrained optimization & robust methods; and Black-Litterman model portfolios where asset-specific, ML-derived models are integrated. The classes on trading introduce the limit order book and market microstructure and then move on to tour the winning strategies of to Kaggle competitions on trading. The feature engineering and code of the winning solutions are reviewed in depth.
- Minimum Skill Level
- Intermediate Level Course Other prerequisites at https://nycdatascience.com/courses/machine-learning-in-finance/
R for Business Analysts
This class will be an introduction to the statistical programming language R for business analysts. We’ll explore data science use cases in the business realm and use R for data wrangling, data mining, visualization and prediction. Throughout the class we will be approaching business problems analytically and we’ll use R to explore data, make better business decisions and identify areas for improving performance. The combination of data analytics, R and the data science process will provide the foundation for using R for data science business problems. Students should come prepared with an understanding of computer programming and a curiosity for data science.
- Minimum Skill Level
- Students should have some experience with: Programming Basic statistical and linear algebraic concepts In R, it will be helpful to know basic data structures such as data frames and how to use R Studio.
- Prep Work
- R Programming – https://www.rstudio.com/online-learning/#R R Studio Essentials Programming 1: Writing Code https://www.rstudio.com/resources/webinars/rstudio-essentials-webinar-series-part-1/
Data Science with Tableau
This course offers an accelerated intensive learning experience with Tableau – the growing standard in business intelligence for data visualization and dashboard creation. Without prior experience, students will learn to work with multiple data sources, create compelling visualizations, and roll out their data science products for continuous, scalable outputs to key stakeholders. By building insight and weaving narrative, students will be empowered to harness data in a striking way that provides value to organizations large and small.
- Minimum Skill Level
- Know how to use Mac, Windows. Familiarity with relational databases is preferred but not required: gain a greater appreciation for the logic underlying Tableau’s features utilize their capstone project to visualize ‘big data’
- Prep Work
- Before the course begins, pre-work will be available for students interested in strengthening their ability to access and extract data from relational databases (e.g. SQL-based servers).
Via analogy to biological neurons and human perception, this course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, the most popular open-source Deep Learning library. Essential theory will be covered in a manner that provides students with an intuitive understanding of Deep Learning’s underlying foundations. Paired with hands-on code run-throughs in Jupyter notebooks as well as strategies for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across the major contemporary families: Convolutional Nets for machine vision; Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis; Generative Adversarial Networks for producing realistic images; and Reinforcement Learning for playing video games.
- Minimum Skill Level
- Object-oriented programming, ideally Python (introductory course: https://nycdatascience.com/courses/introductory-python/) Simple shell commands, e.g., in Bash (tutorial of the fundamentals: https://learnpythonthehardway.org/book/appendixa.html)
Part Time - Data Science with Python: Data Analysis and Visualization (Weekends)
This five week course is an introduction to data analysis with the Python programming language, and is aimed at beginners. We introduce how to work with different data structure in Python. We covered the most popular modules, including Numpy, Scipy, Pandas, matplotlib, and Seaborn, to do data analytics and visualization. We use ipython notebook to demonstrate the results of codes and change codes interactively during the class. Our past students include people with no programming experience or those who have minimal exposure to Python. Students told us our classes are very informative, engaging, and hands-on.
- Minimum Skill Level
- Knowledge of basic data types (e.g. string, numeric), data structures (e.g. list, tuple, dictionary) Familiarity with concepts of list comprehension and for/while loop
NYC Data Science Academy Reviews
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The introductionary course in Python (Data Analysis and Visualization) was an invaluable first-step in the Data Science journey. The course provides hands-on experience with the core analytical packages of the Python language. Tony makes great use of class-time; he is extremely effective in delivering instructional content and fielding questions related to the languages applicability.
It's important when learning anything to get the fundamentals right. If you build bad habits, it can become difficult to fix them later on, especially if you have also built many dependencies on those bad habits. This is why when I wanted to start learning about data science, I chose to take this course to help me make the right choices from the very beginning.
I would say that I got exactly what I came for. Tony is a very good instructor. He is able to express complicated concepts in an understandable way, and I would definitely say that now I understand enough about the Python ecosystem that I could start learning on my own if I wanted.
I spent my undergraduate years focusing on the life sciences without much formal educational background in programming and advanced statistics. While working as a data analyst my first few years out of college, I gained practical coding experience in R, picking up general programming and modeling experience - even still, I lacked the underlying foundation needed to understand and implement more complex machine learning for my projects at work.
The NYCDSA online bootcamp was the perfect blend of machine learning theory and practical, hands-on projects helping to solidify the lecture concepts. The overall experience was intense: I worked full-time at my day job and spent most of my free time (~30 hours/week) keeping up with lectures, course projects and career development - but got out an incredible learning experience, which helped me to perform more advanced projects at my current job and ultimately to find a new full-time data science role. My TA, meeting with me at least weekly, along with my online cohort of 4 other students, held us all accountable for staying on track with course deadlines and project work. This accountability was a crucial component in keeping us motivated throughout the 5 months; other online programs fail to do this and suffer student dropout as a result.
Another invaluable outcome of the program is the portfolio of projects (~5), which NYCDSA greatly emphasizes and helps groom. I used these as a demonstration of my experience (both from a coding standpoint on GitHub, and data storytelling standpoint, on the NYCDSA blog) in almost all job applications. While one does not need to attend a bootcamp in order to create a project portfolio, NYCDSA makes sure to curate and grade the assignments so as to demonstrate in the portfolio an important mix of technical skillsets sought in the job market, and holds its students to higher standards of work quality than they might hold themselves.
I came to NYCDSA immediately after graduating from college with a bachelors in math and finance. I had been struggling with passing quantitative interviews due to my lack of programming skills. The New York City Data Science Academy helped out sooo much on that end by constantly keeping us busy with lectures, homework, and projects. I was very surprised at how quickly I was able to learn programming in Python and pass assessments and interviews for a number of top hedge funds. There is no way I would've been able to learn as much as I did in as short amount of a time anywhere else. On top of that, having instructors, TAs, and a team devoted to helping me develop the skils and connections I needed was invaluable. After graduating from the bootcamp, I completed a project in Python for a quantitative investment firm, impressed them, and got the job. I'm so glad I came to NYCDSA to build the skills I needed for a career I'm truly passionate about.
I came into NYC Data Science with experience working as a data analyst at several companies and active data consulting work.
At NYDSA, I wanted to spend three months solidifying my existing skillset as data analyst and learning areas that I did not know much about like machine learning and big data. In my jobs, I had previously worked with both Python and R, and appreciated the value of both languages. I also liked that NYDSA wasn't part of some big cookie cutter data science bootcamp chain.
For the first month of NYCDSA when we covered topics like data wrangling, visualization, shiny, and web scraping, it was mostly review for me. That being said, I learned some new tools and tricks, and was able to work on some interesting projects with some help with from our great teachers. I also got to meet and learn from a lot of interesting classmates. The students at NYDSA come from a wide variety of backgrounds, from PHDs to straight out of bachelors programs, and are definitiely part of the value of the program.
After watching me instruct classmates on web scraping, which I had a lot of experience with before the bootcamp, NYCDSA asked me if I wanted to record web scraping lectures for their online data science bootcamp. I agreed to record them and got to have some experience teaching while I was still a student in the bootcamp, which both solidied my skills and helped me earn back some of tuition.
During the machine learning and big data portion of the bootcamp, I was exposed to lots of new material and learned a lot. While I still have a lot to learn, I now have a good understanding of different machine learning models and techniques, and some of the big data technologies.
One month after the bootcamp ended, I started working full-time at a consulting company that I was consulting for during the bootcamp. With my experience in the program, I was able to negotiate better terms on my contract with the company. In addition, I have been doing some additional data science consulting on the side (one project referred by NYCDSA) and the skills learned at NYDSA have benefited me in all my work.
If you invest your time in the program, you will get three months of data science learning with awesome teachers who know their stuff and are willing to help you through any learning hurdles. As someone who has and continues to learn most of this stuff on my own, three months with expert teachers definitely accelerated my learning pace. So if you want to spend three months learning a lot of data science, I would recommend you sign up
I have had a very positive experience during and after completion of the NYC Data Science Academy's Full-Time Data Science BootCamp. I chose this particular school because they were the most transparent (the full - and very rigirous ciriculum is available on their website) and the depth and breadth of interview questions from the Data Science Academy convinced me that they take this very seriously - and that this would be a very difficult but rewarding 12 weeks.
To start from the beginning. the application process of the time was rather straightforward - a web application, a short programming assignment, and an interview. Although I didn't have much familiarity with Python or R and had been working in the retail wealth management industry for the past 3 years, my educational background is a B.S. in Mathematics and a minor in Computer Science and was considered sufficicient for me to be accepted into the program. In hindsight, I think the most important prerequisite (outside of the base math/computer knowledge) is having passion for data/programming/AI/statistics/etc then the potential difficultiy of the content becomes secondary to your desire to learn and improve.
The Data Science Academy did a terrific job selecting the studens - the main commonality being the passion and desire to learn a lot of difficult content in a short amount of time.
The program is project oriented - both individual and group projects. There is a good deal of flexibility about the project topic/content so each student ends up with a unique portfolio of ~4 projects (and corresponding presentations/blog posts).
For example, I did an EDA (Exploratory Data Analysis) project and created an interactive webapp using R and Shiny based on data for a particular anti-poverty initiative from a large mulitinational charity. This webapp visualized the breakdown in demographics, geography, and other metrics to help that organization identify trends in the data and better allocate their resources going forward.
The other projects I worked on utilized a wide array of popular datascience tools and techniques, my personal favorites: web scraping (big fan of Selenium + BeautifulSoup ), natural language processing, visualizations, and machine learning. There are a few technical resources available that not all students take adavantage of, those are the various linux servers and hadoop/spark clusters available. During the machine learning project, each time I ran my ensemble of models it woud incapacitate my laptop for ~30hours. I soon discovered the value of using a high powered remote server for number crunching - and polished my linux/BASH scripting skills in the process.
The staff and instructuors are very thoughtful about keeping an environment condusive to learning. Every Friday there would be a Q&A session with the staff where they would ask the students their thoughts on the pace of this week's ciriculum along with other questions/issues. I mentioned that the hand sanitizer dispensar was empty, they listened and had it filled the next week.
The instructors are great. The lecture sessions can run on a bit long, but they are finely tuned machines, packed with a ton of great content and example code. They are accessible on slack and answer frequently answered my coding questions at odd hours of the evening. As the content grew in difficulty, it was very obvious that each instructor has mastered the topic of their lecture - I never felt dissapointed with their answers to my questions.
The atmosphere was condusive to learning, everyone was open to helping each with homework/project coding questions if asked. I was pleased with the sense of comradary among the students as opposed to the toxic competitive environments I've seen at some traditional higher education institutions.
The Raspberry Pi computers have been a personal interes of mine, and one of my projects usilng a Pi 3 caught the attention of the managent staff at the Academy. He saw some good potential, and put me in touch with their PR contact, two months later my project gets publised online and in print - https://www.raspberrypi.org/magpi/issues/61/ (page 13).
Towrds the end of the BootCamp their staff put me directly in contact with several managers/exeutives at business hiring data science personell. I had much better job application results when I leveraged their network as opposed to a cold application. Althought it took a few months after graduating, the Data Science Academy put me in contact with my future employer.
Currently I'm working with the title Data Science Contractor for a Data Science Consultancy. I am extremely happy with the work I'm doing. Every day I get to work on interesting conceptual puzzles and work with Amazon and Google's cloud computing and big data tools. Our work is primarily done in Python, BASH, and SQL, and the NYC Data Science Academy really helped me prepare for the various technical and statistiacal challenges in this profession.
I couldn't be happier with my decision.
I attended the Spring 2017 Cohort of the data science bootcamp at the NYC Data Science Academy. Originally I had read up about the program in 2015 but did not feel prepared to quit my job and take a leap into a full-time bootcamp. I spent about 2 years preparing myself by taking courses online (mostly through coursera) to build up my Python and R skills. Part of me was hoping that coursera courses would be enough to land a job as a Data Scientist or even as a Data Analyst, however that was not the case. Finally in January of 2017 I felt a bit more confident to apply to the bootcamp, and once accepted I decided to officially make the switch into a career in data science.
The bootcamp curriculum is very intensive and cannot be taken lightly. A majority of my fellow students were in a similar situation where they quit their jobs, and invested a great deal of time and money considering that this program is not cheap. Keeping that in mind it's very important to understand that this is not a golden ticket to a better paying job. Simply showing up every day will not be enough to get the most out of your investment while attending this program.
Before the Bootcamp:
The pre-work which is part-time and can be completed online or in person is very important as it serves as a preview of the first couple of weeks of the program. If you complete all of the pre-work (which I highly suggest) the first few weeks of the bootcamp might seem to be full of redundant information and make you question your decision to pay for a very expensive course which is teaching you things you already know. Don't worry too much about that because that is just the calm before the storm as things pick up very quickly as you approach the end of the first month. It's also much better to spend the first few weeks mastering the basics by going over things you know as compared to jumping into the deep end by learning everything for the first time on Day 1 of the bootcamp. Take advantage of this "down time" to master the basics and prepare yourself for what's to come because once you get to machine learning things start to get very serious.
The pre-work mostly covers Python and R programming which are vital skills to have in order to make it through the program. If you can have a basic understand of Python and R before the bootcamp it will only help you because just like anything else the more you do it the better you get at it. The pre-work doesn't include SQL which I believe is very important to have at least a general understanding of the basics. It also doesn't unclude linear algebra or statistics which are also extremely important especially if you have been out of school for a while. You do not have to master any of these (Python, R, SQL, linear algebra, or basic statistics) before the bootcamp but having a basic understanding will help a great deal.
During the Bootcamp:
During the 12 weeks every day involves learning new information, and that is why it is extremely important to keep up with the homework to review what you have learned. The projects are also very helpful to link everything together. The projects are a great opportunity to showcase your data science skills and you should pick topics which are relevant to industries you are interested in. Make sure you can explain every aspect of your project because you will be asked questions about them during interviews.
The staff is extremely helpful and will take the time needed to help you with anything you need to keep you moving forward so do not hesitate to pull an instructor or TA aside to ask questions. The program is not perfect and the staff is constantly working to improve the curriculum. Pulse check on Friday afternoons is a great opportunity to voice your concerns (which my cohort took full advantage of) and speak your mind of what you find helpful and what can be improved. This is not only helpful to you and your cohort, but also for future cohorts.
After the Bootcamp:
Once you make it through the 12 weeks the learning does not stop, because you will feel that there is much you still do not feel confident about. This is an unfortunately do to the fact that during the limited amount of time (12 weeks) there is a great deal of breadth of materials covered but it's impossible to go into great depth on each topic. This is when your show of commitment comes into play where you have to review concepts which you do not feel confident about. Reviewing the material is helpful, and completing coding challenges through HackerRank also helps. The recommended textbook is another great resource (ISLR) which should ideally be read before or during the bootamp. I unfortunately waited until after to read the text which is great for reviewing the machine learning concepts. There are many resources available to help review. I found some very helpful YouTube videos other other textbooks to review machine learning concepts I did not feel confident about. This will obviously vary from person to person depending on your learning style. You can choose to spend time reviewing everything, or focus on mastering concepts which are relevant to jobs your are applying to.
The NYC Data Science Academy is there for you during your job hunt. They will help you fix your resume which helped me go from getting no replies for jobs that I would apply to before the bootcamp, to having 10 phone interviews lined up within 2-3 weeks of finishing the program. The hiring partner event is a great opportunity to connect with prospective employers. Mock interviews are available to help work on interviewing skills. I had my mock interview 2 days before my actual interview, and it was a previous graduate who is currently working as a data scientist. His advice helped to boost my confidence and within a few days of my actual interview I received an offer.
Take Home Message:
Overall it was a great experience for me and I do not regret the sacrifices I made to attend the program. If you are interested in this program please give it your best and keep in mind that at the end of the day the burden is on you. You are the one who has the most to lose if you do not take full advantage of this opportunity. There is a great deal of self-studying involved before, during, and after the program. The more dedication and effort you put into your journey to become a data scientist, the more likely it will result in a positive outcome. You might hear back from some of the jobs that you apply to, you might not hear back from most of them. Not all interviews will go well but with each failure you will learn what needs to be improved. My advice is that if you are motivated and dedicated to start a career in data science, and willing to put in the required work then this 12 week data science bootcamp at the NYC Data Science Academy is the right choice for you.
It is one of my best decisions to attend NYC Data Science Academy. During the bootcamp, I gained more confidence in programming. The coding skills taught are always on trend. Followed by the schedule of the bootcamp, I smoothly transferred my career path into data science. I do not only learned R, Python with corresponding practical, popular libraries in industrial applications but also acquired the ability in quickly learning new skills. NYC Data Science Academy provides students a good platform to communicate computer programming, machine learning algorithms, and its applications. Instructors and TAs are always helpful and patient for students to overcome difficulties in the study and provide supports. At the same time, students could have a blog platform with editing help to present projects. Interview skill development and career advice from Chris and Vivian are powerful to develop students as strong competitors in the job market. I really appreciate the knowledge, skills, and support I acquired from NYC Data Science Academy. I highly recommend NYC Data Science Academy to anyone who is interested in this career.
Long story short --
I have a PhD in computational geoscience and worked as a geophysicist in Houston for five years. I joined NYCDSA for the 12-week bootcamp, and worked as hard as I could. I was hired after my first interview, with an offer in hand within two weeks post graduation. NYCDSA has helped me achieve this smooth transition into a brand new field in just 3.5 months.
How I made the decision to join --
1) The time commitment is right: I was willing to put in a few months of my time through well-designed highly-intensive training, rather than spending a year or so to learn on my own. I do not want to go through a one-to-two-year data science master's program, considering a) I have a computational PhD degree, and b) although many data science theories have been long established, data science platforms and tools are evolving fast.
2) Word-of-Mouth: I have friends in New York working in the data domain recommending this academy over other data science training offerings. "richer content", "up-to-date material", "good instructors" are among the key words that I recall.
3) A balanced focus on teaching and job service: I have interviewed with a few different data science bootcamps. Many of them gave me a feeling that they want me to be 90% ready for a data scientist role coming in, and they are only willing to do the 10% polishing to get me "sold". NYCDSA convinced me with their road map that they will first focus on teaching the content that they are proud of, then switch gear near the end to the job search part. They shared their online pre-work content with me, so I could get ready. I was impressed by the quality of the recorded lectures and coding platform, which further boosted my faith in the academy.
Experience at the bootcamp --
1) The content
The teaching material is well developed and feels fresh. They keep polishing the core content and introduce many newly developed "jump start" sessions along the way. You are well informed about what's new out there while learning all the fundamentals.
2) The instructors
They have a stable teaching team here. Unlike many other camps which keep losing instructors and hiring recently graduated trainees as instructors, NYCDSA has a stable team. The majority of them started working here from years ago when the 12-week bootcamp was initiated.
They are a knowledgeable, friendly and hardworking group of people, with finance, math, computer science, physics background. When they are not teaching, they either help the students or work together on their side projects. It is smooth to learn from people you respect and admire.
3) The fellow campers
A vast majority of the students here have or are working on a graduate STEM degree, with a solid quantitative background. Many also bring in years of experience from finance, health care, software engineering, marketing or other fields. What they all share is a strong will to perform and succeed in data science.
I feel honored to have worked with a few of them on the group projects. We helped each other not just during the bootcamp, but also during the job search period. I am convinced that it is a great professional and personal network to be in, for the long future after our time at the academy.
4) The career service
NYCDSA organizes hiring events for each cohort. You will see quite a few Fortune 500 companies coming to the event, as well as promising start-ups. The NYSDSA career team verify the job vacancies, collect details about the hiring teams, and prepare cohort members individually for a successful outcome (resume, LinkedIn, GitHub, blog posts, interview skills, and many other aspects.) They also utilize their own personal network to get interview opportunities when they see a great match.
They keep supporting and motivating the students during the course of job search. There are rooms set aside for graduates to come back to and work on things. Here you get daily check-in's from the instructing team and helpful discussion with fellow cohort members. I have been enjoying this cozy and welcoming space often, and plan to keep gaining knowledge and energy from this ideally located data science hub.
Advice for future students --
1) Complete the pre-work, have an initial plan for the projects coming in.
2) Work hard during the bootcamp, be curious and independent. Treat it as a 3-month internship.
3) Plan to jump right into job hunting effort right after.
4) When working with wonderful teammates, make sure to deliver your parts; after achieving your goals, remind yourself that you have been kindly helped along the way.
Closing comments --
It has been a great investment. With the guidance, help, and support from NYCDSA, my job preparation and search time frame has been shortened by at least 3-6 months. For people with solid STEM background and strong desire to work in Data Science, this bootcamp should be a challenging and rewarding journey. I would continue to cherish the relationship I have built with my mentors and friends met during Cohort 9 at the academy. I wish them well.
Going to NYC Data Science Academy is a decision I don’t regret for a second. These were ones of the most challenging 3 months but well worth it. I learnt a lot and got a lot of support that I would not have gotten anywhere else.
As long as you are ready to put in a lot of sweat, hours and effort, you will be successful and do extremely well because you will always get the support of the TAs, staff and fellow students. You are surrounded by a bunch of smart people and TAs who are here to support you and help you grow.
The fact that NYC DSA selects students with a Masters or PhD degree is a big plus because you end up working with people from whom you can learn tremendously. Their experience and background make the bootcamp that much more interesting.
The curriculum is solid and half of it is dedicated to machine learning. Some bootcamps only dedicate a few weeks to machine learning which does not make sense to me given that it is the core of a data science position. The curriculum keeps evolving based on the feedback the students give every week during the pulse check.
I believe that you won’t have any difficulty finding a data science position after attending the bootcamp as long as you have the drive and treat the bootcamp and your job hunting as a full time job.
Also, NYC DSA offers a lot of help in your job hunting. The last 3 weeks of the bootcamp are dedicated to helping you with your job hunt (don’t worry you’ll still be working on your data science skillset in the meantime with probably the toughest classes of the bootcamp happening at that time too…). You’ll receive a lot of support to find a job from the staff and they will prepare you for interviews.
All in all, get ready to work hard and if you do, this will be one of the best decisions you will ever make to advance your career in data science
I had been in the 12 week data science bootcamp last year summer, which changed my view to myself about leaning data science. It was a big challenge for me at that time as I had little programming experience. Although the course was extremely intensive, the tutors and TAs were very helpful and encourage us to find a way to achieve the goal. Also, I believe that the course are very comprehansive and good for someone who really wanna find a related job in Data Science industry. Now I am studying Master of Data science in University of Rochester. My experience in the 3 month bootcamp, defenitely increased my chance to be admiited. I would like to say thanks to all of them who helped me at that time and make me ever stronger.
100% YES!!! I wholeheartedly recommend NYC Data Science Academy! If you want to switch into data science, the bootcamp will help you land your dream job. I got an internship offer shortly after the end of bootcamp(~2.5 weeks) through the bootcamp’s hiring partner event, and recently became a full-time data scientist at the same company. All of this would not have been possible without the help of NYCDSA!
When I was reading through bootcamp reviews, I personally thought it was more helpful to find people of similar background as mine and see how well they fared. For instance, knowing that people with only bachelors degree attended NYCDSA and got data scientist jobs helped to not only inspire me, but also to set realistic expectations on what type of jobs I could get and how long it could take. So here is a blurb about myself:
- Bachelors degree In math and economics
- <1 yr of work experience
- Limited exposure to R/SQL/Big Data tools prior to the bootcamp(Does using a select statement in SQL count?)
- Prior experience coding in C and basic Python through an intro level computer science course in college. No exposure to data packages in Python.
Prior to coming into the bootcamp, I asked myself: “Is the bootcamp worth the $16,000 investment?” Is it going to give me enough skills to find a job as a data scientist?”. If you read my introduction, the ultimate answer is an obvious YES! Below, I am listing the top 7 reasons why I think NYDSCA was a worth investment for me:
- Top-notch job assistance: From teaching how to craft your resume to preparing for technical interviews, Vivian and Chris did an excellent job at explaining what interviewing for data scientist positions was like. This “soft skill” portion was really important for me since I wasn’t accustomed to interviewing for technical roles. Thank you Vivian for always pushing me to become a better data scientist! Thank you Chris for all the earnest advices on job hunting!
- Connections of the bootcamp: As I mentioned earlier, I got my job through the bootcamp’s hiring partner event. Being able to take advantage of the wide network of employers definitely made “getting the foot in the door” much easier
- Learning both R and Python: I wanted to learn both as I knew it would help me cover my bases and be prepared for most data science-related jobs.
- Transforming me into a confident coder: having learned coding through a class in college and a bit of self-learning, I knew I needed to improve my skills if I ever wanted to land a serious job as a data scientist. The pre-work material along with projects were really helpful in that sense
- Structured curriculum: There is a lot of thought put into the structure of each class. It was very nice to have all materials that I needed to learn organized for me so that my only worry was to learn.
- Instructors: They deserve their own section as most staff have been teaching for quite a few cohorts. They are all very knowledgeable and approachable. Special shoutout to Zeyu for being an amazing TA and always offering helpful guidance through my projects!
- Projects: Each project covers an essential area of data science-data visualization, web scraping, machine learning - and I learned so much through them. The projects were also essential to build my data science portfolio and showcase my skillset to employers.
If you made it all the way to the end, thank you reading this review! All in all, NYCDSA was great!! It worked perfectly for me as it gave me the skills (both technical and soft) I needed to land a data scientist job. BE PREPARED TO WORK HARD. Treat both the bootcamp and job hunting as a full-time job and you will be rewarded. :)
I attended the January-March 2017 boot camp of the New York Data Science Academy. It was the most densely packed and learning filled 3 month period of my life. NYCDSA has the right balance of theory and practise built into their curriculum.
Projects were fun and challenging. Instructors and TA's with expertise in both coding and statistics were available round the clock. I personally asked for assistance on many topics and was more than satisfied with the help. Staff's knowledge about theory and real world applications blew my mind.
Perhaps, the best part about NYCDSA is working with fellow students who are as passionate, knowledgeable and hard working as you are. Highly recommended.
Honestly one of the best decisions I’ve ever made. Yes it’s a reasonably difficult course, but if you are truly interested in data science you enjoy every second of it. Like anything, you get out what you put in. If you’re ready to work as hard as you need to in order to master this wealth of knowledge in 12 weeks, this course is 100% for you.
The instructors and TAs are excellent, all accomplished data scientists with a wealth of skill and knowledge. The resources, from slides to code examples and practice questions, are things I will continue to use throughout my career as a data scientist. There is ALWAYS more to learn in the field of data science.
If you’re thinking about going because you simply want a pay raise, then don’t. The course is relatively difficult, and if you aren’t willing to put in the work to master everything you need to land a job, then you won’t get a job. Simple as that.
However, if you are committed to becoming an expert data science, the job support here is immense. There are mock interviews, code interview practice questions, linkedin workshops, presentations from hiring companies and data scientists etc…I myself recently accepted a dream offer from a company I was connected with through the bootcamp.
You likely won’t get a job immediately, it’ll take awhile and a lot of interview practice. It took me about 3 months. If you haven’t mastered the skills you need to be a data scientist, then you don’t have the skills to pass through the interview process. But again, if you are committed there is no shortage of resources made available to you. If you do not succeed here, it is because you did not put as much effort into them as they did into you.
Finally, an underrated part of the experience is the other students. Some of my best friends in the city I met through the bootcamp, and we still go out for drinks all the time. The course not only provides you with knowledge, but connections. It’s a room full of intelligent, driven and entrepreneurial people. You could expect nothing less.
If you want to be a data scientist, and more importantly you have the drive to learn and succeed, you’ll thrive here. Simple as that.
This course was the best thing to ever happen to me. In 20 weeks (4: pre work, 12: course, 4 job hunt) I went from someone who couldn't write 'Hello World' in python to a full blown Data Scientist, making six figures, with multiple companies vying for my interest.
What you should know:
You will get as much out of this course as you put in. I had many, many days where I was working well past midnight and back in class by 9:30am. You learn how to learn, which is THE skill required for any coding job. The curriculum is intensive, and a lot of times I couldn't totally complete the homework without checking for answers from my peers, and that's okay! In the real world, much of your job will be interacting and working with a team.
Go every day, work hard, finish the projects on time, and hold yourself accountable. The lecturers do a great job, but ultimately when you're 24+ years old, nobody is going to spoon feed you. The homework is great, but when you try to put everything you've learned together into a well rounded project (there are 4-5 projects), that is when you really understand what is going on. Throw yourself full bore into the projects, and take pride in your work. 90% of what I learned, no exaggeration, was in the 3-5 days before projects were due. Its one thing to figure out homework by looking at the example sets, and a different thing entirely to apply those concepts to a data set with different structure and goals. If you are proud of your projects at the end, you will get a job. Period.
The job is the ultimate goal for 99% of people entering the camp. Unfortunately, there is some confusion about how the search will work. For one, you will not be "given" a job. For most people, the job search will take 1.5-3 months. Vivian has excellent contacts but she also has 40+ students. In order to guarantee yourself a job, you need to approach the process like a data science project. For me, I did "easy apply"s on LinkedIn, 50 a day. These take literally 15 seconds each. I then selected 15 companies a day with a more formal interview process, and sent them a variation of a pre-written cover letter. For my top picks, I tried to find a hiring manager or data scientist on the team, and add them on LinkedIn. I put my name on AngelList, and got many companies reaching out. I humbled myself and told everyone I was more interested in a great learning position, not a great salary. I iteratively changed my own interview methods, including voice tone, inflections, negotiations, honesty levels, until I found a balance that worked for me. You cannot just apply and hope. That is not a method.
Basically, the bootcamp is the first big step. The second big step is learning how to apply and interview. Many people send out 5-10 applications to their top picks (who are often everyone else's top picks as well) and then sit on their hands and wonder why they haven't gotten a job. When entering a new field, you have to make concessions about your salary and place of work, in order to reap the rewards down the line. Also, without multiple options, you will not be able to negotiate because you'll feel this is your only chance. BROADEN YOUR HORIZONS!
The camp was the best decision I ever made. I read a book called Design Your Life, which basically said take how you want your life to be, then decide what is necessary to get it there.
I wanted to live in NYC, with a six figure job, working in an office with low stress, and love what I do. NYCDSA made all of that possible. If you have gotten a degree that isn't taking you where you want to be, but you know you're smart and can work hard, I strongly urge you to apply to NYCDSA today.
WHY Data Science and WHY NYCDSA: I had no idea about data science until 2016 Feb when Alphago defeated Li Shishi and it was the first time for me to get to know what is Artificial Intelligence and what is machine learning. Bringing my huge curiosity, I self-learnt an online Machine Learning course on Coursera and was able utilize the skill , for the first time, in my working project and had a positive outcome. Being proud of my achievement, I also realized there was still a long way down the road. This is actually the reason why I decided to join bootcamp - fully armed myself with comprehensive data science skillset and then shifting my career towards a new page. NYCDSA is a perfect choice for me as it teaches anything you need to know to work as junior data scientist and allows you to keep full time job as the same time.
Experience: Even though it is an part-time online bootcamp, I was investing 40+ hours / week on studying slides, having office hour with TA and doing projects with current full time students. The bootcamp starts with an introduction Toolkits (Unix,Git,SQL,etc.), followed by introduction to R/Python, then followed by statistics and machine learning immediately applied using R and Python. The comprehensive curriculum is completed by an introduction to Big Data tools. I was able to finish a strong portfolio with 4 projects in Shiny App, Web Scraping, Kaggle and Capstone. I learned a lot especially by collaborating with full time students and TAs, so I would strongly recommend those online students who are physically in NYC, walk in the classroom and collaborate with full-time students on your last two projects.
In terms of job assistance, NYCDSA can provide tremendous assistance on your job hunting after your graduation. Vivian and Chris know what you need to have on your resume to catch HR’s eye and get an interview, and they have a strong network which can expose you to much more opportunities.
Outcome: I finally received offer from 2 Top insurance companies, 2 Top hospitals, and 2 boutique consulting companies. I feel my investment of 7 months of study and $16K totally worth as it allows me to finally launch my dream job. Thank you New York City Data Science Academy and I would recommend it to anyone who has same dream to be a data scientist.
I came to New York City Data Science Academy because I wanted to become a better coder, to become more knowledgeable about machine learning, and to get a better job. Having completed the bootcamp in the Spring of 2017, I can say that through the Data Science Academy, I was able to accomplish all three.
Before the bootcamp
Previous to the bootcamp, I had a job as a data analyst which gave me the exposure SQL, Linux, Hadoop, and some Python - all tools that are taught in the academy. I knew I wanted to improve my overall problem solving approach, specifically using Python and R. After a few years as an analyst, and many months of debating if enrolling in a machine learning bootcamp was worth the time and money, I decided to go for it. Although I do not have a masters degree like many of my fellow cohort members, I knew that I could use my work experience to my advantage in preparing for the bootcamp. Like many others have stated, giving yourself enough time to go over the 100+ hours of prep work before the bootcamp is highly advised - being able to perform the basics of Python and R will set you up for success.
Preparing before the bootcamp is also crucial in another way. As you spend more time studying, you spend less time doing all the other normal things you’re used to doing in your life. In order to make the most out of the bootcamp, sacrifices must be made, from your social life, to your eating and sleeping habits, and to the amount of coffee you normally drink. If you don’t get used to it before, adjusting to these changes midway through the bootcamp can be a challenge.
During the bootcamp.
If you spend enough time preparing before the start of the bootcamp, then the first month or so should not be too challenging (but still very useful). Many of my fellow cohorts actually became nervous, thinking that our investment in the bootcamp might not have been worth it. Don’t fret. After going over the basics again, the fun truly begins.
After the first month, you will spend every day learning machine learning concepts, applications, statistics, and then applying these techniques in both Python and R. This is no easy task in a few short months, which is why the instructors, teaching assistants, and Vivian, deserve so much credit in churning out so many qualified data scientists in such short time. The instructors are always, at all times, helping and guiding you in the right direction. On top of that, you have the additional resource of working with your fellow cohort members, all who have unique backgrounds and always willing to help.
In the end, the journey would not be worth it without days of extreme struggle and frustration. Some days I felt really confident in the material, other days I did not think I had what it takes to be successful. What I believe the instructors are best at is instilling the confidence in each and every student, spending as much time with you as needed to successfully complete the projects.
The last few weeks are spent on tidying up your resume, github, blog posts, and interview skills. Aside from learning both R and Python in the bootcamp, one of the reasons why I chose the Data Science Academy was because of the strong professional connections that Vivian and the team have developed over time. The final day is dedicated to a networking event, where the ratio of companies to students is almost 1 to 1. Although it can be a bit nerve wracking, Vivian and the team do a good job of preparing you on what to expect.
After the bootcamp
I was lucky enough to land an internship at a startup as a data science intern from one of the participating companies at our networking event. I have to give my experience to the bootcamp all the credit for this. Had I not had relevant experience and projects to speak of, I would not have been able to land the job. As my internship was coming to an end, I spent more time with Vivian and the team doing mock interviews, going over practice questions, asking for help on take home assignments, and constantly reviewing. Without a doubt, I can say that the three months of the bootcamp was the second hardest thing I’ve ever done - the first hardest thing was getting a job afterwards.
Vivian and the instructors have the uncanny ability of knowing what specific skills you need to improve on, based on constant back and forth communication based off of past interviews, as well as the interviews you eventually take. You will fail, and fail a lot. Most data science interviews are designed to test you on the very limit of your knowledge on data science subjects. With practice, you will answer the questions confidently, and even if you are unsure of a question, you will be able to communicate a thorough data science process on how you think the question could be answered. If you fail an interview, it’s another lesson on how to improve for your next interview, which Vivian will most likely have helped you set up already.
After months and months of dedicating my life to all data science related activities, I have landed a job as a data lead at a media company, and have the entire NYC Data Science Academy program to thank for it. If you are seriously considering a future career in data science, then I can 100% vouch for the academy, so long as you are ready to work harder than you have ever worked in your entire life. At the end of the day, it’s all worth it.
Machine Learning is transforming the world at an incredible pace and I felt it was imperative for me to acquire new skills in order to be professionally relevant. The summer bootcamp fit my schedule perfectly and I decided to enroll to better understand this exciting new field. With a PhD in Engineering from a top-five ranked school and significant background in Computational Materials Science, I felt that I had sufficient background to be successful in the bootcamp. Due to my hectic schedule, I was unable to complete all the pre-work prior to the bootcamp and I believe that this had an impact in my learning experience at the later stages of the bootcamp. In my opinion, pre-work is analogous to binary decision trees—you are trained to be independent weak learners ahead of the bootcamp. The actual bootcamp is more like a random forest where individual students work alongside other students as well as Teaching Assistants and Course Instructors to significantly contribute on a variety of real world projects related to Data Visualization, Web Scraping, Machine Learning and Big Data. The course is fast paced and students are exposed to a variety of technologies relevant to Data Science. The instructors are knowledgeable and fellow students in the cohort are sharp. It is not surprising that NYC Data Science Academy is one of SwitchUp’s Top Bootcamps of 2017. I strongly recommend this bootcamp to individuals who are seriously interested to pursue a full-time career in Data Science.
Going to NYCDSA is one of the best decisions I’ve ever made. Data science was completely new to me and I didn’t have a vey good programming background. At NYCDSA, i was able to master both my data science and programming skills with the help of ever-present instructors, TA’s and friendly classmates. The curriculum was well balanced with all important data science topics, lab practices and mandatory individual projects included. In addition, they also had weekly coding challenges and professional development courses which teaches you how to deal with interviews and present your self in the real world. The three months program was intense but is doable if you put in effort and dedication. After the course, the academy also help you with your resumes and get interviews with companies within their connection. I highly recommend NYCDSA to all aspiring Data Scientist as this program helped me achieve my dream of becoming Data Scientist within 3 months after graduating.
I am a recent NYCDSA graduate. Before the retrospect. The outcomes first: Know myself better, made great friends, landed a great job within 3 months, came back to the dream land……
My story is a little different. The pre-DSA career is not bad at all. I have had worked for a couple of top companies in the pharma and health industry for several years after getting my PhD. For some reasons, I am always thinking of expanding and tuning my fields. Life is quite dynamic that I recently moved to the east coast and happened to get stresses from various parts of the life. Rather than hanging there, I decided to challenge myself and make changes. I have thought of schools but it is not realistic for my situation. I have studied some bootcamps and visited NYCDSA last year. The people there were very kind and down to the earth. I was not too confident at first but I would like to give a try. Therefore I gave up what I was doing, which was pretty risky and got a lot of doubts from people around me.
But I always know very well and anyone should keep in mind that 3 months can hardly make one totally expert in data science, otherwise I will be willing to pay 10 times more. We should always keep learning; the effort will pay back.
The course designs are up to date. They focus on practical data science skills. The first month is about coding and analysis in R and python, which I found helpful in my case. After that, machine learnings have been emphasized on both python and R, then the big data part. Most of us are not directly from cs or math backgrounds. We had to work very hard for the classes in the morning, the homework at night and most importantly, the 4 projects with different emphases. We were pushed by the deadlines and harsh schedules, like the real world, have to come out the results before knowing everything. I was the humblest guy during the 3 months in my entire life J. The instructors are very kind, helpful and always there to help. During the process, I have made some great friends, knowing people from such diverse backgrounds and also know myself better. Although I got my job on my own search according to my specialized interests and desires. The NYCDSA tried its very best to connect alumni and companies for the hiring. They have great connections. There are many alumni get jobs from the network.
Suggestions and lessons: Once you have made the decision to attend the bootcamp, forget the past and dedicate yourself. Always focus, do not doubt your decision for one second or look back.
Skill is very important, but that’s not everything. Try to communicate, make friends and find out your strength well during the bootcamp. Design at least 2 of your 4 projects well, you know yourself the best, interact with the instructors frequently but do not solely rely on their ideas. Do not live far, I burned myself out on the long trip every day. For the projects, try to team up with members most accountable, fair and those with high integrity and work ethics. If you share the common interest, that’s even better. Do not judge others only by the technical or coding skills, it’s a teamwork, cultural fit is critical. Last but not the least, we do pay a lot and might give up what we already have for the bootcamp. But when you get the quick reward in life, these pains with hope are abosultely worth it.
I came to the NYCDSA after having spent several fruitless years on the academic job market with a Ph.D. in English. I was depressed and didn't really know where to redirect my career. I had a background in math and a strong enough interest in data, but I knew that I needed to skill up, because that no one was going to take me seriously without another credential.
Despite my unusual background, the NYCDSA was very welcoming and helped me get unstuck. I'm in a new, fulfilling career, and I couldn't have done it without this program. By the end of the first year in my current job, I'll recoup the investment with respect to my prior earning power.
1. The cohort my term (and, I suspect, for most) is a really excellent mix of people. You have ex-academics, mid-career folks who are trying to reskill, fresh B.A.s who need to weaponize their math/CS skills; you have people from the sciences, from finance, from advertising; you have representatives of over half a dozen countries. They bring a variety of talents, and you learn a lot from seeing what problems they want to approach and how to approach them.
3. The instructors are all very committed. There's a lot to learn, and they work hard to see that you get it absorbed. I'm not wild about the setup pedagogically--three-hour lecture blocks make it easy to lose focus--and sometimes the instructors are not the easiest to follow as lecturers, but they put tons of work in, and it's appreciated.
Could Be Improved:
4. I know they try on job assistance--there's some work with resumes and interview prep, and they set up some interiews for you with their assorted hiring partners--but they don't seem to have the staff they need (at least as of my job run) to supervise it as well as they could. To give an example, they didn't have a dedicated placement officer when I was there. Job hunting is always terrible and unpredictable, but my impression is that some of the other camps (e.g., Insight) do a better job of minimizing the aimlessness and frustration it can incur.
Still, after three months in the program and three and half on the market, I got a job, and I wouldn't have done it without the academy. It was an important stage in my life, and I'm happy I made the decision to go in. It's let me move on with my life.
What impressed me most about my experience at NYCDSA was that it exceeded all of the expectations I had from speaking with the instructors and researching the program online. The entire team truly went above and beyond and I have only positive things to say about the instructors, the curriculum, and the way the experience changed me personally.
I found the instructors at NYCDSA to be not only incredibly knowledgeable, but approachable, thoughtful teachers. They seemed to really care about each student’s development and regularly stayed late in the evenings to offer help. If they could not be present in person, the instructors were always an e-mail/Slack message away and made it a point to check in with students and offer additional resources. I also liked that they not only taught the theory behind machine learning algorithms, but explained their most common applications and pitfalls to watch out for.
The curriculum at NYCDSA is constantly updated to reflect the most valuable skills for the real world. I found during interviews that whenever I was asked whether I had experience with a certain data science technique or language, I could either say “yes” or show a project to demonstrate my skills directly. What I was taught always matched up with what was requested of me in the interviewing or working world. Even after the end of the bootcamp, I kept my slides and materials for review, and was provided with hundreds of interview questions to help me succeed going forward.
Most importantly, the NYCDSA provided an amazing support group and helped me transform myself during a critical point in time. The other students were dedicated, kind, and came from all different backgrounds. I learned a huge amount from them and the instructors about the process of learning a skill like data science/programming and collaborating successfully. Apart from teaching the curriculum, instructors also provided resume reviews, listened to elevator pitches, and made themselves available to discuss interview experiences. I felt as though I had a whole village behind me, rooting for my success.
I would without a doubt recommend NYCDSA to any friends or colleagues looking to learn data science. It was an exceptional experience and I feel grateful to have found it.
I would highly recommend anyone who wants to switch career to data science or strengthen data science knowledge to apply NYC Data Science Academy.
Before I joined the Boot camp, two of my close friends already graduated from the program and landed their dream jobs. So unlike most of people who don’t know too much about this program and have to do some research before applying it, I applied the program without a hesitation and also had a high expectation as well.
The curriculum design was excellent and really taught you how to learn new tech skills, frameworks quickly. It could be hard for people who are not exposed to programming or statistics to keep up the pace. Make sure to go through all the pre work and learn basic statistics before attending the boot camp. Once you started to work on your final project, you will notice that you’ve learned so much.
All the instructors are very talented and very patient to students.
Vivian and Chris work hard to help you to find a good job once you’ve graduated. After you graduate, NYCDSA sticks with you. Vivian and Claire emailed us frequently with new job opportunities and openings.
It’s not going to be easy. You will have nights you have to stay up to finish the project, missed parties that you don’t have time to attend to. But it will be worth it! The knowledge that I’ve learned in 3 months are way more than my two years master degree and I got my dream job too.
I’ve never regretted to attend NYC Data Science Academy. I’ve met so many amazing friends in the boot camp. It’s a very valuable experience to me in terms of career development and personal growth as well.
If you are passionate about data science and big data and you are willing to put hard work to achieve the goal in a short time of period, there is no better place than NYC Data Science Academy to learn data science skills.
I went into the bootcamp with little more than a liberal arts B.A. and some online self-training. Within 6 months of finishing the bootcamp, I received multiple job offers and landed a dream job on the strength of my data science skills. The employer found me through the NYC Data Sceince Academy job network-- in that sense, the Academy continued paying dividends well after I had finished.
It is hard. When I read reviews that speak negatively the program, I suspect that they are from students who didn't put in the work themselves to take the many learning and job opportunities that NYC DSA provides. If you are willing to devote your time and effort to developing a new skill, you will be rewarded by this program.
The NYC Data Science Academy 12 week in-person DS bootcamp will give you quality instructional resources, project experience, and beneficial job support. In 12 weeks I was able to learn a vast amount about the field, and gain the momentum necessary to secure a position 2 months after I finished. I greatly enjoyed the intense intellectual environment, as well as the comradery with my fellow students. The instructors, TA’s, and management are approachable and receptive to your needs, as long as you pester them. The bootcamp was not perfect, but I was highly satisfied with my experience, and it was worth the cost. It definitely accelerated my skill set and allowed me to gain experience, while making friends in the process.
The curriculum has great breadth ranging from fundamental statistics, to carrying out end-to-end analyses in both Python and R, to covering big data tools. The breadth and 12 week time constraint meant that depth was sometimes lacking, but this was reasonable. It just means that you pay more attention to the subjects you're interested in or where your time would be well spent. For example, I am not very interested in NLP, so I didn't devote much energy there, and instead focused on time-series analyses.
The instructors are all very knowledgeable, and passionate in their respective sub-areas of expertise. They were the real deal. The only drawback was that some of the lectures were hard to follow, but it was not hard to get clarification from the instructor or a fellow student.
The bootcamp structure is open and approachable. Everyone is on the same floor in midtown Manhattan. Feedback is encouraged, and I really appreciated the opportunity to talk to the people who were in control of things, and who could help guide me in the right direction. This openness was a huge plus in my experience.
The job support was pretty solid and consistent. I got some interviews from the hiring event, and the door was open for any job-seeking advice I had along the way. New opportunities were pushed in my direction, and I was frequently reminded to stay sharp with my skills. To help with this post-program training material was provided.
A drawback of the program is that seeking out the help you need was harder than it should have been. As a student you were expected to seek out the help you need. This is fair, but it would have been nice to be challenged a bit more in the curriculum, for example with more mini-DS case studies. Another issue was that It was difficult to get feedback on projects. I think support with projects over the whole process could have been better. On the other hand, you get to choose your own project topics, and you will learn by doing. Also, there is continuing support after the bootcamp has ended until you find a job at the least. You also have a place to come sutdy even after the bootcamp is done.
All in all, go down and talk to them if you have any doubts. Talk to the students, teachers, and anyone else you can find. I think if you love to learn, and you are proactive about seeking out the help you need, then this bootcamp will help you learn a ton in a short amount of time.
Our latest on NYC Data Science Academy
Welcome to our last monthly coding bootcamp news roundup of 2016! Each month, we look at all the happenings from the coding bootcamp world from new bootcamps to fundraising announcements, to interesting trends we’re talking about in the office. This December, we heard about a bootcamp scholarship from Uber, employers who are happily hiring bootcamp grads, investments from New York State and a Tokyo-based staffing firm, diversity in tech, and as usual, new coding schools, courses, and campuses!Continue Reading →
Kelly Mejia Breton has a background in mathematics and statistics, and had worked as a senior energy analyst for five years when she quit her job to enroll at NYC Data Science Academy. Kelly had enjoyed learning about machine learning in grad school, and wanted to learn more about how to apply it. Now Kelly has an exciting new job as a Marketing Science Associate Director at Mindshare, where she is using both her old and new skills. Kelly tells us why she chose NYC Data Science Academy over other data science bootcamps, how much she appreciated having other women in her cohort, and why she enjoyed all of the projects she worked on there.
What is your pre-NYC Data Science Academy story? Describe your educational and career background.
I graduated with a bachelor’s of arts degree in mathematics from the University of Rochester in 2006, and started working in finance at Morgan Stanley. I was there for three years in their private wealth management department where I opened accounts, and traded. Soon after that, I decided to pursue a masters in statistics from the City University of New York at Hunter College. I graduated in May 2011 and started working at Pira Energy Group, where I was an analyst. A year and a half later I was promoted to be a senior analyst, forecasting crude prices, and product prices.
After five years in that job, I decided I wanted to go back to doing what I learned from my statistics degree. I loved Pira and I learned a lot there, but my role was based more around fundamentals and economics, and I wanted to be working in statistics. I had tried learning data science on my own using Coursera, but it was difficult to find time alongside my busy work schedule. So I decided I needed to study full-time. I did some research and found that NYC Data Science Academy was the best fit for me.
Why did you want to specialize in Data Science? How different was that from your previous analyst role?
What I was doing before was more economics and fundamentals– we weren’t using much machine learning. I had done machine learning in my graduate program, and I really wanted to learn more about how to apply it. I had done a little bit with R in my graduate program, but I wanted to do more. So after five years, I thought I needed a refresher, plus I could learn some new algorithms that I hadn’t known.
Why did you choose NYC Data Science Academy over other coding bootcamps? Was the curriculum important?
I did some research and NYC Data Science Academy was highly ranked. At the time when I was looking for a data science program, I found that most of them were on the West Coast, and the only one that compared to that level on the East Coast was the NYC Data Science Academy. I met with them, they were really nice and helpful, and that sealed the deal.
The curriculum was important too. If they hadn’t offered R and Python as a base, I would not have been interested. Also, as a perk they also taught big data topics, like Spark, Hive, and Hadoop.
What was the application and interview process like for you?
The application was an online application, and you had to submit some documents, and do some coding. Then there was an in person interview. The coding challenge was medium difficulty as I hadn’t coded before. Although I had used R as a statistician, I didn’t really code as much. I couldn’t do it on my own so I was googling a lot. I don’t think I did that well on the coding, but NYCDSA looked at my background and skills as a package deal. I don’t have coding, but I do have experience in statistics, analyzing data, and math. Those things probably outweighed the fact that I didn’t know coding. Whereas somebody else with strong coding skills, and not so much statistics, may also be a package deal and be accepted to the program. But if you don’t have a statistics, math, or coding background, then maybe it’s not a great fit. They saw how I was thinking and they saw my background, so they probably thought that I would be a good fit and could pick up whatever I was lacking.
How many people were in your cohort? Was your class diverse in terms of gender, race, life and career backgrounds?
Yes, there were people from literally everywhere, and from all age groups. We were a group of 21 students, and there were five girls. For me, that’s high, because studying math and statistics my whole life, I’ve always been the only girl in the room. They all had similar backgrounds, either physics, math, engineering or computer science.
What was the learning experience like at NYC Data Science Academy? Describe a typical day and teaching style.
I like to wake up early because that’s when I learn better. Whereas some students would stay late, I would get in around 7am and start my day early. I’d go over my homework, and my notes before class started at 9:30am. At 12:30pm we had a lunch break, then class started again at 2pm. In the afternoon we would either be doing some kind of topic that’s not part of the main curriculum, continuing the lectures, reviewing homework, presenting a project, or working with the TAs on our projects. Classes normally ended at 4pm or 5pm, then afterwards you stay for a few hours going over homework. I would stay there until 7pm on a daily basis and some people stayed there until midnight every day.
What was your favorite project that you worked on at NYC Data Science Academy?
I honestly liked them all. What’s cool about the Academy is they let you pick the data set you that you want to work with. There are five projects, and every 2 weeks you have a new project due that you have to present. In my cohort there was one project the instructors selected for you, which was a kaggle project. We had to work in a team and it was pretty fun. Even though the kaggle project was all about physics, I still liked it because at the end of the day, it helped me realize data is data, and I was able to still find a story, still analyze, and still forecast, whether I had any background in physics or not, so that was pretty cool.
How did your learning experience at NYC Data Science Academy compare to learning at college?
What I liked about the teaching style was it was more one-on-one, you really felt like you could reach out to instructors at any point. It wasn’t only in TA or office hours, you were able to reach out to them throughout the whole day. A lot of instructors stayed there until late– there were always at least two TAs there until 10pm at the earliest.
How did the bootcamp prepare you for job hunting?
They had professionals come and help with our resumes and interview skills. They also have a career fair, which is like a networking event, where employers in the industry who are looking for employees would come in and network with us. A lot of interviews came out of that, and a lot of people got positions from those events.
What are you doing now? Tell us about your new job!
The position is Marketing Science Associate Director at Mindshare, and I got it through the Academy. Mindshare was going to attend the networking event, but didn’t end up coming. I was looking for them and couldn’t find them, so the following day I reached out and sent them my resume. About a week later, I started interviewing with them. Then later on, they contacted the Academy again and we actually have another NYC Data Science grad who is working at Mindshare, so that’s pretty cool.
What does your role involve?
My position is working with data – sometimes large datasets – using Python, R, Hive, and some Spark to find data insights for clients. We analyze client advertisements, and marketing campaigns to see how they performed, and use machine learning to forecast how they can improve their advertising.
Are you using the technologies you learned at NYC Data Science Academy or have you had to learn new skills?
The Academy gave me a great foundation so that the new skills I am learning just build on what I already learned. Without the Academy, I think I would’ve had a more difficult time understanding and using these new skills that I have learned. We pretty much use everything I learned at the academy including SQL.
How has your previous background been useful in your new job?
Because I worked a lot with data before, data is data, so my previous experience is always useful. Everywhere I go, I feel like I’m building on what I’ve learned.
How do you stay involved with NYC Data Science Academy? Have you kept in touch with staff and other alumni?
We have a Slack chat group, so we stay in touch through that, and a lot of us stay in contact through Linkedin. Every now and then the Academy has a meetup or some event. I went back to speak at a meetup once, before I started my position at Mindshare. I was really nervous to start NYC Data Science Academy because I didn’t know anyone who had attended, and I wasn’t sure what to expect. So I like to give other people that comfort, that it actually was a great experience, and for me it was a dream come true. So if they reach out to me I always make the time to go.
What advice do you have for people thinking about changing careers by going through a data science bootcamp?
I would say, if you really do love data, and you like coding, machine learning, and finding the inspect of the data, and the story and all that, then I would say go for it. I was very nervous and had a really stable job that I was really good at it, so it was a hard decision to leave. It was a tough decision, but I do not regret it. So if you really love it, do it.
It’s hard work, it’s not an easy thing, so if you’re not committed 100%, then don’t waste your time. But if you do like it, then it’s worth a lot, and I would say do it. It’s hard work, but it can be done.
Originally from Turkey, Arda Kosar studied mechatronics engineering, got an MBA and worked as a Business and Sales Consultant before moving to the U.S. in 2015. He had dabbled in data science, and wanted a career as a data scientist, but found his self-training was not enough to land him the roles he wanted. So Arda enrolled at NYC Data Science Academy for their 12-week program to learn R, Python, and machine learning. He tells us why he chose NYC Data Science Academy, how much he learned from his cohort mates, and all about the Kaggle data science competition he entered. Arda graduated in July 2016, and is now a Senior Data Scientist at Publicis North America (and the bass player for a band)!
What is your pre-bootcamp story? Your educational background? Your last career path?
I moved to the U.S. from Turkey in October 2015 because my wife got a job here. Back in Turkey I studied mechanical engineering for my bachelor's degree, and I worked for two years as business and sales consultant. Then I decided to pursue an M.B.A., and graduated Jan 2015.
When I first arrived in the U.S., I applied to jobs for three months. I was applying to data analyst and data scientist jobs, but I only received rejections. So I started looking at the job descriptions, to see what skills they required. I saw that the most common requirements were R, Python, SQL, and machine learning. So I decided to make an investment in myself. I didn’t want to do a masters program because they are too long – I needed something short, but efficient. So I found two bootcamps, NYC Data Science Academy and Metis. I decided to start my research on NYCDSA. I talked to a couple of alumni, and they sounded so excited about the bootcamp, and said it was really good. I didn’t need to talk to the other bootcamp, I just decided to enroll in this one. I graduated July 1, and got a job in October.
Why did you want to change career paths and become a data scientist?
In my MBA, I did a course about advanced Excel, and in that we analyzed data, and did some basic machine learning, but at that time I didn’t know it was machine learning. I found that I really liked playing with data and coming up with insights from numbers. So I started looking at data analysis, and data visualization. When I look at a data set, I get so excited about it, and what insights will it give to me. In the projects I did, I found really exciting insights from my data.
Did you try to learn on your own before you thought about a data science bootcamp? What types of resources did you use?
I took some courses from Coursera, but never face-to-face. I knew a bit of Python before I started bootcamp, and I had heard about R but not SQL or machine learning. And I had some basic statistics knowledge, but not that much.
What factors made you choose NYC Data Science Academy?
Their website was so detailed, I saw a lot of things on their curriculum included in the job descriptions I was looking at, and I read reviews on Course Report about NYCDSA. I found it’s really helpful when people leave their name and title on the reviews, so I contacted some of the alumni to ask them about the program.
How did you pay for the NYC Data Science Academy cost?
When I moved here, I had a car in Turkey, so I sold it. I invested the car money into this bootcamp.
What was the NYC Data Science Academy application and interview process like for you?
There was an online application, which asks about your background, and your work experience, plus two coding questions at the end. I don’t know if they are using the coding challenge to eliminate people, but I think they want to see your skills. Then I called NYC Data Science Academy for an interview. I interviewed with Janet and an instructor. They didn’t ask any technical questions, they just asked about my passion about the field. They want to see how dedicated you are, because it’s a huge investment. It’s good because it increases trust, they don’t just say “ok come to the bootcamp”, they are really picky about who they enroll to the bootcamp, which is good.
What was the coding challenge like? What did you have to do and how hard was it?
It was two basic Python questions about palindromes. It had a medium-level difficulty. I had to search a little bit, read about it in some forums, and then I tried to come up with a solution.
How many people were in your cohort? Was your class diverse in terms of gender, race, age, life, and career backgrounds?
We were 20 people and it was a good mix of men and women. Some of them were managers as I have a friend who is a manager at PWC. One guy was in his 50s and had a son who was our age. It was really great having so many people from all different backgrounds. We could easily ask each other questions, about projects and homework. We were like one huge group that worked together all the time.
NYCDSA usually only accepts people with a master’s degree or Ph.D. Is that something that was important to you? Did you want to learn alongside people with STEM backgrounds?
Yes. I learned a lot from the curriculum, my instructors, and TAs, but I also learned a lot from my cohort mates. It’s really nice being in such a diverse group of people. Some had Ph.D.’s in physics, Ph.D.’s in math, or computer science. I was a little bit scared at the beginning because my bachelor’s degree didn’t include that much statistics – mechanical engineering is just numbers and formulas. I knew some computer science, but I improved my skills a lot with the help of my cohort mates.
What was the learning experience like at your bootcamp — a typical day and teaching style?
When I arrived, before the actual bootcamp started, they put me into an introductory Python class for four weeks, two days a week. So when the bootcamp started it was really nice, because they started from scratch. You know what programming is, but we began with the basics. For example we started with R, we learned how to create variables, write syntax, and other basic stuff. It then steps up really fast, and you have a lot of homework to practice and improve your skills.
On a typical day, the lectures usually start at 9:30am. In the morning there is a three-hour lecture until 12:30pm. Then we have a lunch break. We mostly eat with our cohort, and talk about nondata science stuff. In the afternoon there are no lectures usually, but if they can’t finish the topic in the morning, they can allocate an hour more in the afternoon. Usually in the afternoon, there are homework reviews, coding reviews, or some introductions to different tools to use for our projects, which are useful.
You can stay on campus as long as you want, and you can come in anytime you want. It’s not restricted – you can even sleep there! I didn’t sleep there, I live in the Bronx, so it took me an hour and 15 minutes to commute. I was, however, the first person in, and the last person out most of the time. At the end of the bootcamp, most of us stayed there til 7pm or 8pm, but it really depends. If you’re more efficient working from home, you can go home, but generally all the material the instructors and TAs taught ended around 3:30pm or 4pm. There were also sometimes guest speakers from the industry, which was really cool. Usually the day ends at 4pm or 5pm.
What were your Instructors like?
We had an instructor teaching R, machine learning, and statistics who has a really powerful background. We also had another instructor who is teaching Unix, Git and GitHub, and creating Shiny dashboards in R. Another instructor was teaching Python, and machine learning in Python. We also had three or four TAs. So there were a lot of people to turn to whom we could ask questions. They were super helpful. You also have a Slack channel so you can Slack them if you’re not on campus.
What is your favorite project that you worked on at NYCDSA?
I’d have to say my capstone project. We worked on a Kaggle competition with a group of three. It was an open Kaggle competition, so every day there were neo-calls and neo improvements, but we got 30th place. It was about predicting demand from historical sales data. It was a nice project and really business-like. It was for a Mexican bakery company, and was a really common business problem, about reducing the amount of leftovers. It was really nice to see that machine learning can be applied to real business problems like this. It was a real bakery called Groupo Bimbo.
Kaggle is a platform for open data science challenges, so they are real business problems, but with simulated data sets, because they don’t want to share their actual numbers. But the problems are real, the size of data sets are real, only the numbers are simulated. It’s open to everyone around the world. One cool thing about Kaggle is there are some really experienced data scientists competing, so even by reading the forums and looking at their solutions, you can learn so much.
How did the bootcamp prepare you for job hunting (interviews, hiring events, whiteboarding)?
NYCDSA works with a resume review company, and each student gets a one-on-one resume review session. Vivian, Founder and CTO of NYCDSA, and two other people on the job hunting team, are helping students a lot, they are continuously watching your application process, and looking in their networks to see if they know people at the companies you are applying to. NYCDSA also does mock interviews, so they are doing their best to get you a job. To actually get a job depends 30% on their effort, and 70% on your effort. They are not magicians but they are doing their job really well.
What are you doing now? Tell us about your new job!
I work as a Senior Data Scientist at Publicis North America, a marketing agency. For now, I mainly build tools to help other departments. It’s only my second week, so I haven’t used many machine learning techniques I used at the bootcamp, but I’m using all the information I learned about R and Python, so now mostly it’s about programming. I think in the future there will be a lot of new stuff. The tools are mostly visualizations for other departments if they have to prove something. So if you have to prove something to someone, you have to come up with data, or visualizations. Some of it is to simplify their workload.
How did you get the job?
I applied to the job through LinkedIn, but actually when I got the onsite interview, I contacted Vivian to tell her I got an interview at Publicis, and I discovered that just a week ago my director and my manager gave a speech at NYCDSA. So they already knew Vivian and NYC Data Science Academy which made my life easier.
What was that interview process like for Publicis?
I had a phone interview first, to check if I’m a good fit for the job. Then I had an onsite interview which took about half an hour, and I talked about my projects and my MBA. They asked a lot of questions about my projects. I didn’t have to do a technical interview with coding challenges or data sets, but they asked technical questions about my projects.
Has your previous background been useful in your new job?
Yes. In my MBA I mainly focused on marketing, which helps a lot because Publicis is a marketing agency. So I know the terms and the jargon, so if you combine it with a data science bootcamp like this, I think it’s a good match for marketing agencies.
I also took some programming classes in my engineering background, and some basic statistics. But other than that the only thing I’m using is how to think mathematically, how to troubleshoot, how to think like an engineer. So I’m not using the mechanical engineering classes.
What’s been the biggest challenge or roadblock in your journey to becoming a data scientist?
I didn’t get that many math classes in my bachelor’s degree, so the most challenging thing was catching up with that stuff, because machine learning is on a basis of mathematics. So I had to catch up with work and homework, doing the projects. So that’s why I was the first person in, and the last person out most of the time. It was the most challenging thing for me just to keep up with everyone, because you also have to get some sleep and take care of yourself.
You have to really dedicate three months of your life just to this, and nothing else. I was still able to do rehearsals and play shows with my band, so that was my only social stuff. I’ve been in a band called Tacoma Narrows since February. We have an album on Spotify. It’s Folk Americana, and a little bit of funk. It’s a mix of genres. I’m playing bass. You have to have something to clear your mind from your huge workload.
How do you stay involved with NYCDSA? Have you kept in touch with other alumni?
We have a Slack channel for all of our alumni. We are going to alumni events, and we are also organizing reunions among ourselves. We talk to each other all the time. It was only 20 people, so we still keep in touch, meet, catch up, have some drinks.
What advice do you have for people making a career change through a data science bootcamp?
You should only expect 30% from the bootcamp, it’s all about hard work and dedication. You really have to dedicate three months of your life to this and most of the time you won’t be able to do anything else. That’s the key I think. If you are doing a career change like me, you really have to make sure your resume and LinkedIn are polished, because if you are applying online, they are the only things that represent you. These things have to be really solid, and they have to show you can do data science. Vivian is helping a lot in that process! We also post all of our projects online in a blog like a portfolio. You can see my portfolio here.
Find out more and read NYC Data Science Academy reviews on Course Report. Check out the NYC Data Science Academy website.
Welcome to the September 2016 Course Report monthly coding bootcamp news roundup! Each month, we look at all the happenings from the coding bootcamp world from new bootcamps to big fundraising announcements, to interesting trends. Of course, we cover our 2016 Outcomes and Demographics Report (we spent a ton of time on this one and hope everyone gets a chance to read it)! Other trends include growth of the industry, increasing diversity in tech through bootcamps, plus news about successful bootcamp alumni, and new schools and campuses. Read below or listen to our latest Coding Bootcamp News Roundup Podcast!Continue Reading →
Denis was a biomedical engineer on the pre-med track when he realized he wasn’t on the right career path. He had always loved tech and keeping up with the latest tech trends, so he researched coding bootcamps. Denis came across data science and realized it was the perfect combination of his tech and STEM interests, so he enrolled at NYC Data Science Academy. Now Denis is a data scientist at Ameritas Life Insurance Corp. and it’s exactly the job he was looking for. He tells us why he chose NYC Data Science Academy over other data science bootcamps, how dedicated his instructors were, and about his Donald Trump web scraper project!
What is your career and education background before you started at NYC Data Science Academy?
I graduated with a bachelor's in biomedical engineering. I then started medical school, but I soon felt it wasn’t for me and I lost interest. So I turned to technology because I knew I liked it. I have always been a tech person, trying to find out what’s new, what's hot, and all the trends.
I was also interested in math and worked for a while as a tutor for high school math and standardized tests like the ACTs and SATs. Then I realized, what better way to combine technology and math, than with data science! I felt it was a great move into data science because, like in tutoring, you’re telling stories and figuring out how to break down this information to make it more deliverable and easier to understand.
How did you become aware of Data Science as a career?
I was interested in technology and I’d heard of web development courses and bootcamps, so I looked into doing one of those. But in one of my searches, I came across a Data Science bootcamp and that’s when I first started learning about it.
Did you have any exposure to data science in your work or previous studies?
I didn’t necessarily have exposure to Data Science, but more so the storytelling and the research aspect. I did research for two years in a biomedical engineering lab.
Did you research other data science bootcamps in NYC?
I looked at Galvanize and a few others in the northeast. I wanted to stay local. I’m from Westchester County, NY, so I wanted something close. When looking at other schools, it looked like NYC Data Science Academy was a little more personalized, and I liked how they scheduled their curriculum. It would be standardized, you’d have planned coursework and classes, and pre-prepared notes as well. I thought that would be really useful because, let’s say you learn something in the morning, but you don’t really remember the exact code for it, the notes are always great to refer to.
Did you want to learn a specific data science language?
No. Prior to the bootcamp, with my interest in technology, I was dabbling in web development, HTML, CSS, a bit of Ruby, and Python, so I was actually open to learning more languages. I thought that would be beneficial, because if one language wasn’t great then you could always turn to another.
Did you think about studying data science at college?
Possibly, but bootcamps are a lot faster, and they get you work experience which is what I feel is very important. Going back to school would take longer, and I already had a degree. I was actually looking at masters degrees if anything, but I thought a bootcamp was the best way to get experience and get into a new career.
How did you pay for the NYC Data Science Academy tuition?
I don’t think NYCDSA offers scholarships, so I financed it on my own. During bootcamp, I was also tutoring 15 hours every weekend so that helped pay for it.
How many people were in your cohort? Was your class diverse in terms of gender, race, life and career backgrounds?
There were about 22 people. I think it was diverse. Even though we had more guys, about a quarter were females. People came from different backgrounds, there were some people coming out of academia with PhDs, and some people who had work experience. Most people had a masters and work experience, and were looking for a change in career.
NYCDSA usually only accepts people with masters or PhDs. Was it unusual for you to have a bachelor's and not a master's or PhD?
There were four or five of us with bachelor's degrees. Maybe I got in because I got into a doctorate program, even though I didn’t finish.
What was the applications and admissions process like?
There was an online application with one or two coding challenges at the end. We could use any coding language, I think I stuck with Python because I had experience with that at the time. And then we had a phone screening interview, where I could ask questions, they could ask me questions, and get to know me. Then I got called in for an onsite interview, and that’s where I had interviewed with an instructor to talk about my past experiences, what I hope to get from the program, and my future direction, to see if I’m a fit. I was nervous throughout the whole process because the bootcamp was something I really wanted.
How difficult was the coding challenge?
Not exactly difficult. I had done previous problems like that, using online resources. When I was learning how to program in Python, they would give me basic math problems, and say “how would you put this into code,” and I thought that was pretty helpful in figuring out the application challenge.
What was the learning experience like at your bootcamp — typical day and teaching style?
A typical day started at 9:30am with lecture for three hours, that ended at 12:30pm, then lunch until 2pm. Usually people would go to grab food and come back to do work and ask questions while they ate. The afternoon was for homework review, help with projects, and sometimes there were extra learning sessions, like workshops, with topics that would be useful. Some of us stayed until 11pm or 12am. I’ve done that quite a few times, it’s not abnormal. The instructors are also there up until 10pm or 11pm, so they are really helpful. That was something I really liked and was one of the reasons I think I made the right choice going there.
How did it compare to learning at college?
In college, professors have office hours but I never really utilized them because I didn’t feel I needed them. However with this, because you’re learning at a fast rate, and most of the learning is done through practice, having the TAs and instructors around for questions is very helpful, so that’s the main difference. The learning pace at NYC Data Science Academy was comparable to some of my past experiences with education.
What is your favorite project that you worked on at NYC Data Science Academy?
My favorite would have to be the data visualization projects and the web scraping. Web scraping was interesting because it made you think, how could you write a script to pull data off a web page? And sometimes in real life you’re not going to have data in a table for you, neatly laid out. So that was a helpful project to learn how to tackle those problems later on.
I scraped a Twitter page, it was Donald Trump’s page. I was looking for word counts, so seeing which words he used a lot, and the conclusion was, he used the phrase “make America great again”, very often. I also analyzed and looked at who he tweeted at the most, which I found were social media personalities and media outlets. Looking at the number of retweets and likes, you could tell that before he started to run he around 50 for each tweet, but a year later he is getting a couple of thousand likes and retweets for every single tweet.
What was your Data visualization project?
This was the first project and the topic was regarding water quality in NYC. It was interesting to see the number of water complaints and the type of complaints in each borough. I drink water straight from the tap, so I like to know where I should be careful about drinking unfiltered water. I remember Staten Island had the least number of complaints, however, they also have a smaller population. I didn’t have a chance to compare the number of complaints with population size.
How did NYCDSA prepare you for job hunting?
Besides the knowledge and education, they have a hiring party in the second to last week of bootcamp. That’s where we learned to network and talk to people who were hiring, and that’s actually where I met my current manager. Then after the bootcamp, the school opened up an extra room where grads could come and continue to learn and ask questions. The job placement manager also held mock interviews to make sure we were prepared and I found that really useful. They also helped refer us to potential employers.
What are you doing now? Tell us about your new job!
I am at a life insurance company called Ameritas, and I’m a data scientist in the marketing department.
What is the company like and what do you do there?
The company is headed in a new direction and I’m the first data scientist here. So far, I’m in the process of learning about the products – I didn’t know there were so many insurance products. I’m trying to learn more about the industry, and figure out how we go about learning about our customers, which is basically the aim of every marketing organization. Once we do that, we can cater our products to the customers a lot better, and also improve the customer experience.
How big is your team? How many people do you work with?
I interact more with the IT department for now, but I do report directly to my manager in marketing, and she reports directly to the CMO (chief marketing officer). So I feel like my voice and my suggestions are being heard, and that’s good because I wasn’t looking for an organization where I would join and do things that would be disregarded.
How have the first three weeks of your job been so far?
They’ve been really fast and busy. For the first two weeks, I was meeting everyone in the marketing department. But there’s another office in another state, and I work with people in both offices. I think I’ve met with 20+ people, ranging from graphic designers to videographers, to people in charge of each product that the company has.
So far I’ve been exploring the data, gathering what we have, and talking to other people in marketing to figure out what would be useful to know. We are just starting this, so it feels like a startup environment in an established company. I like that.
How did you find your job and when did you start?
I talked to my manager at the end of hiring party. A couple of weeks later, I followed up, and my hiring manager set up a phone interview with me – she was in Ohio. So we set up a phone call, and she gave me a problem, and asked how I’d tackle it. I got called back for more video interviews with a director in IT, two marketing managers, and the CMO. After that, I got good news, and I moved to Cincinnati, Ohio.
Are you using the technologies and skills you learned at NYCDSA?
I actually just started using them because I finally got my hands on some of the data. Right now, I’m starting off with R since there are a lot more more packages available, so that’s what I prefer to use. If Python is needed later on, I’ll use that, but for now I’m just going to stick to R. I think the company is also going to use some other applications which I’ll learn as well.
Is this the job you wanted? Do you feel like you reached your goal?
This is actually the job I was looking for. I wanted to do marketing, because it was interesting. I’m coming from a background in health, I like to understand people. Being able to analyze people’s buying behavior or catering services to make them feel more involved with the company, and keeping them around, is something that really interests me. I also like that I can explore any avenue I want, instead of going into a company and being told, “you’re the data scientist, you’re only going to be looking into email clicks or just websites.”
I know you’ve moved states, but how do you stay involved with NYCDSA? Have you kept in touch with other alumni?
Yes, when I was still in NY, I was coming back and using the extra room at NYCSDA so I often saw my classmates and instructors. And since I’ve moved, if I know of an opportunity on LinkedIn, I’ll send it over to them. We also still stay in contact in our cohort Slack group.
What was the best thing about NYCDSA?
The helpfulness of the instructors, their knowledge and willingness to help us- they dedicated a lot of time during their lunchtime, and after class. Some of them stayed to help until 9pm or 10 pm. Having flexible access to the building was great because we could always come and study if we wanted and I think that was really useful.
What was the most challenging thing about studying data science?
The most challenging thing is the amount of information given in that short period of time, because you can’t slack off and hope to catch up the next day. You have to actually stay on top of it every single day. I think that was the most challenging thing because sometimes you’re just a little tired, so you’ve got to make sure you get enough sleep as well.
What advice do you have for people who are thinking about doing a data science bootcamp?
The projects are very very important. Anyone can say “I know how to do this” on their resume, but a portfolio of projects actually proves it. The fact that we do five projects gives you a lot of opportunities to showcase different skills.
Wendy was a biologist studying the sense of smell when she started using machine learning in her research. She liked it and wanted to learn more about algorithms and statistics, so she enrolled at NYC Data Science Academy (NYCDSA). Now Wendy is working as a Data Scientist at ASCAP, predicting trends in the music industry! Wendy tells us why she wanted to learn both R and Python, how much she enjoyed learning with her main instructor, and how NYC Data Science Academy was instrumental in helping her get her new job.
What were you doing before you started at NYC Data Science Academy?
I got my masters of biotechnology at the University of Pennsylvania; so that’s very different from data science. When I graduated, I got a job at a private research lab studying olfaction, the human sense of smell. While I was at that job, even though it was biology, I used machine learning algorithms to make predictions on whether a compound will have a smell or not. So that was my first exposure to data science.
Wow, I’ve never heard of someone studying smell! Can you tell me about it?
My study was to define the space of smells. So for example, for colors, we know there are three primary colors, red, blue and yellow, and if you mix them you can create all the colors in the world. But we don’t know how smells are created, or what are the primary smells. More importantly, we don’t know the space of the smell. I was looking for what makes a compound smell, and the reason behind it.
So why did you want to do a data science bootcamp?
At my last job, I was practicing machine learning on my own. I could write code but I didn’t really know how algorithms actually worked, and what was going on behind the screen. So the main reason for me to go to a bootcamp was to understand the statistics that go into an algorithm. In terms of field, my entire educational background is in biology, but I didn’t really want to limit myself to just biology. Machine learning is a technique that I can apply to different fields, so another reason I went to bootcamp was to open up my job options.
Did you try to learn on your own before you thought about a coding bootcamp?
My boss at my old job sent me to a few workshops, and I also learned from him along with teaching myself. So before I joined the bootcamp, I had been practicing for two to three years. Yet, I needed the hands-on experience.
Did you research other data science bootcamps in NYC?
I wanted to go to one on the East Coast because I’m from Philly so it’s closer. I looked into a bunch and I applied to three – NYC Data Science Academy, Galvanize, and Data Insight.
What attracted you to NYC Data Science Academy?
There are two reasons. First, I really liked their syllabus because it is more thorough than other bootcamps in NYC. NYCDSA teaches both R and Python. I had been using R for many years and I think R is pretty important, so I wanted a bootcamp that would teach both R and Python. (A lot of NYC bootcamps just focus on Python.) The second reason was the opportunity to do four or five projects throughout the bootcamp. Other bootcamps I researched had fewer projects. I wanted to do more so that when I applied to jobs, I’d have something to show employers.
Did you think about studying data science at a college?
I already have a masters degree, I didn’t I want to go through that again, it’s pretty expensive. Data science bootcamps are quick, relatively cheaper, and teach all the skills that you need. The amount of time you put in is equivalent to a whole semester.
How did you pay for the NYC Data Science Academy tuition?
I paid out of pocket because I could afford it. For students who are looking for a bootcamp, something to consider is that NYCDSA doesn’t provide any loans, and they don’t have scholarships. A few bootcamps I looked at, like Galvanize, do have scholarships. Overall, the other aspects of NYCDSA outweighed the need for scholarship, so I decided to go there.
What was the NYCDSA application and interview process like for you?
The first step is an online application. Then NYCDSA gives you a coding challenge where you can use whichever programming language you want to answer the questions. It wasn’t terribly hard as they want to make sure you have some basic coding experience. After the coding challenge, NYCDSA contacts you to schedule a call. The purpose of the call is for you to ask questions, and for them to assess if you’re a good fit. If you’re chosen to move forward, you’ll have an onsite interview with an instructor. For the onsite interview, they asked about my background, and my goals after bootcamp.
How many people were in your cohort? Was your class diverse in terms of gender, race, life and career backgrounds?
There were 20 people in my NYCDSA cohort. Out of 20 people, we had four girls, so not too bad. It was pretty diverse where half were white, about a quarter Asian, and a quarter other races. Our cohort was a really smart group of people. About a third of them had a PhD and had just finished school. Another third were probably in their 40s or 50s, and already had pretty successful careers. I was really surprised because a few of them owned their own companies, and weren’t looking for jobs, but just wanting to learn new skills. The last third of the group were people like me with Masters degrees. Plus, there were two people fresh out of college. The majority of people had a graduate degree and a few years of work experience.
Who were your instructors and what were they like?
We had three instructors at NYC Data Science Academy. Our main instructor is responsible for teaching machine learning, statistics, and the coding in R. He is very knowledgeable and had plenty of work experience before he came here. He was an actual data scientist, and his educational background is very heavy in statistics. I really like him, he’s very knowledgable and very well rounded in statistics and in machine learning. Then we had another instructor who was responsible for coding in Python; his background is a PhD in math. The last instructor taught Hadoop and Spark, the big data tools. He is a bit older and worked at Google for about 10 years.
What was the learning experience like at NYC Data Science Academy – what’s a typical day and teaching style?
So the bootcamp starts at 9am every morning. From 9am to 12pm, we have a lesson, with one break. The course is pretty intense and interactive. For the teaching style, every instructor is slightly different. We spent most of our time with Chris and he makes everything pretty fun, the way he teaches us. He’s very fluent, and has a great personality so you’re never bored. If we have questions, he encourages us to just ask when it comes to mind as opposed to waiting until the end of class. Sometimes we’d have a competition, where we’d do a small project in groups, and present it to the class.
In the afternoon, it’s a bit more flexible. If we didn’t finish a lesson in the morning, sometimes it runs into the afternoon, or we do homework review. For every lesson we have homework, and then we have four TAs to help us review the code. Sometimes, we have guest speakers from the industry to help us prepare for our career.
What hours did you and the other students usually put in?
Every student is different. I’d get there 9am, some people would get there 7am or 8am. In the afternoon, our homework reviews generally went until 4pm or 5pm. A lot of students would stay and work on homework or projects as the TAs stay until 9pm or 10pm. So you could spend an entire day (or 16 hours) there, and some students do. I usually went home after homework reviews because I would get tired and I need a break.
What was your favorite project that you worked on at NYC Data Science Academy?
So we had a few different projects. The first project was data visualization, which was really fun. All the data I plotted in my last job was static, but during the data visualization project, we learned how to use Shiny, which is an interactive app you can use to build interactive graphs.
The other project I really liked was the web scraping project, which was in Python. It was great for boosting my Python skills! With web scraping, you can pretty much scrape any website you want. One of the challenges I found in doing all the NYCDSA projects was finding a suitable data set. It can be hard to find good data sets, and hard to validate the source. But with web scraping you create the data set, so you know the data is good and the structure is what you want. So I did a web scraping project to see if I were to buy a house in NYC, where should I buy it, if I want to rent it out as an investment. It was down to a few different boroughs, but I think the top area was somewhere near Williamsburg in Brooklyn.
How did NYCDSA prepare you for job hunting?
The entire bootcamp is 12 weeks, and we started the career help in week 8. We had a resume coach look at our resumes and help fix them, then we also learned how to fix our Linkedin profiles. We had a workshop on interview techniques- how to dress and how to speak. In the last two weeks, potential employers came in to interview us. Two or three companies came and each student got at least two or three interviews – which NYCDSA guarantees.
Lastly, we had the hiring party where we spent three hours meeting potential employers. We met around 25 employers. The hiring party is very effective because all the employers who came out are obviously looking for data scientists. When I followed up with them, my return rate was almost 100%, and a lot of those turned into interviews.
What are you doing now? Tell us about your new job!
I’m a data scientist at ASCAP, the American Society for Composers and Publishers. We are a music company; we handle the performance rights of songs, and we represent the songwriters and publishers. So for example, you write a song, and then when your song gets played you need to get paid, so we are the middle man between you and how you get paid. So think you and the radio station, or you and the TV. We will represent your rights and if someone wants to play your song, we charge a fee, then we give you your money.
How did you find your job and when did you start?
I found my new position thanks to the NYCDSA hiring event. ASCAP’s HR contacted me before they came out to the event. The bootcamp sent our resumes out to potential employers to contact us before the hiring event. So that’s how I got connected.
The bootcamp ended April 1st, and I started interviewing about a week before that. The interview process was quick because I received the offer a week after the bootcamp, then started May 1st.
What’s your specific day-to-day role?
We have a few different projects going on. The main one I’m working on is to predict music trends. So for example, say you want to predict the trends for the song Hello by Adele. We know the song is a really big hit right now, so we know it probably will get played 1000 times a day on radio stations, and we want to predict what the song is going to look like three months from now, if it’s going to play 2000 times, or if it’s already in its declining phase.
What is the company like and how big is your team?
The company is not new, we just celebrated 100 years, so it’s definitely a company with some history. Our data strategy team is brand new, it’s been around less than a year. My team is still growing but we are pretty well rounded. We’re part of a bigger team, but I work with five or six people every day. I’m the first data scientist here, and we’re looking for more. That’s a very good thing about my job because I have a lot of flexibility to do the job the way I want to. And I can report the insights I find directly to my manager and to all the senior management, so my voice is heard in the company.
Are you using the technologies you learned at NYCDSA?
Yes. My day to day job is pretty much data analysis and machine learning all day long, so I use both R and Python as well as Spark.
Is this the job you wanted? Do you feel like you reached your goal?
Yes! I’m pretty happy here. I like the job I’m doing every day, because it really is a data science job.
How do you stay involved with NYCDSA? Have you kept in touch with other alumni?
Yes. Since you spend eight hours a day with all the students, you become friends. I’m still pretty close with two girls from my cohort. And since our instructors were really nice to us, I still go back and visit every now and then. Everyone becomes this family within your cohort so people still stay in touch and occasionally get dinner together.
What were the best and the most challenging things about studying at NYCDSA?
I definitely learned a lot there, but the best thing was the job connection.
The biggest challenge for me was time management because we had a lot of homework and projects were due every two and a half weeks. You spend a lot of time doing both, and sometimes if don’t have enough time, it’s hard to pick and choose how to fit everything in within that finite time frame.
What advice do you have for people who are thinking about doing a data science bootcamp?
Two things. First, I encourage everybody to apply early, because after you get accepted, you can spend that time before the bootcamp to start improving your skills. They do teach from the very beginning in terms of coding, but if you don’t know any languages it will be pretty hard for you to follow. So you’ll want to spend some time studying both R and Python.
The second thing is, trust the system. NYCDSA had us do four or five different projects, then we fixed our resumes and LinkedIn, and started applying to Jobs. One thing I did, that I wouldn’t do again, was that I started to apply for jobs fairly early, about a month after the bootcamp started. Back then, I only had two projects to show, plus my resume and LinkedIn weren’t fixed. I know I missed a few good opportunities because I wasn’t fully prepared. My advice would be to wait until towards the end of bootcamp, until you’re at least 75 percent prepared, before you start sending out your resume.
Ho Fai has an impressive career in IT Infrastructure for consulting powerhouse PWC. When he realized that he clearly loved gleaning insights from data to solve his clients’ problems, Ho Fai decided to invest more heavily in his Data Science skill set. His company agreed to let him take a sabbatical and enroll in NYC Data Science Academy, with the aim to make his skills even more valuable to the business. Here, Ho Fai answers all of our questions about his experience at NYC Data Science Academy!
What were you up to before you went to NYC Data Science?
I've been an IT infrastructure consultant since 2007, originally for BearingPoint, whose North American Commercial Services practice was acquired by PWC in 2009, and I became a Manager in 2013. I consult for different industries - primarily financial services but also airlines, hotels, pharmaceuticals, automotives - but always around IT and usually IT infrastructure.
What’s an example of an IT infrastructure project you’ve worked on in the past?
In the world of consulting, every project is different, but to give you an idea - in one project, I worked with a company to assess their current state of technology in terms of compute, storage, network, etc, and helped define their vision for the next 5-10 years. That’s a high-level strategic project, but I’ve also worked on more tactical projects to help clients actually migrate data centers or separate from their parent companies.
Do you have a CS degree? What type of education do you need in order to get that job?
I studied in France at the Ecole Nationale Supérieure des Télécommunications. The French “Grande Ecole” system is a bit different from what we're accustomed to in the US, but I essentially obtained a Master’s degree with an emphasis on Computer Science and Networking with dashes of Economics and Macroeconomics.
Do you have a background in programming at all?
Only basic Java and C++ from my academic studies in my Master’s degree. Over the course of my consulting years, I didn't really do much coding per se, because I was more of a strategist in technology consulting, not a developer. The only coding I did on the job was in VBA in Excel, and some SQL.
What inspired you to start looking at data science bootcamps?
Throughout my years of consulting in IT infrastructure, I’ve always solved clients' issues using bits and pieces of data science, gleaning insights from data to solve the client's problems.
After taking stock of everything I've done so far - I realized that's the piece I enjoy the most. After talking with friends in Silicon Valley who went into data science, it confirmed that the combination of programming, math, data visualization and communication to end users is my passion and my forte.
How did you find out about NYC Data Science Academy in particular? Did you research other data science bootcamps?
Like any good consultant, I did my due diligence! I made a spreadsheet and compared curricula, price, time/length and so on. I also talked to some of my data science acquaintances to get their perspectives on these data science bootcamps. The curriculum and timing at NYC Data Science Academy made it the most appropriate choice.
Your story is unique because you didn’t quit your job to do a data science bootcamp. Instead, you approached your company about taking a sabbatical- what was their reaction when you pitched it to them?
In a nutshell, overwhelmingly positive. I've been working for PWC for quite a while, and I have developed great relationships. I am tremendously appreciative of all the mentors in the company who support my career decisions and development. I'm planning on returning to PWC after graduating from the NYC Data Science Academy, where I have the flexibility to reorient myself within the company into different groups or even help develop a practice using these data science skills that I am acquiring.
PWC definitely sees the value of these data science skill sets. It was a no-brainer for me to ask my management, and it was a quick decision for them to agree.
Does your company offer education benefits? Did they actually pay for the bootcamp tuition?
PWC is huge on personal development. In consulting, the people are the product, so the skill sets and experience of the consultants is what our clients are paying for. PWC is huge on providing learning and development- both internal and external. And beyond that, there's actually a budget devoted for each employee to invest in learning that may only have a tangential relationship with your current position.
Tell us about the application process for NYC Data Science Academy. Did you have to learn any Python or R in order to do the coding challenge?
There were two coding exercises, which I found relatively simple, but that simplicity depends on your background. Having had some level of programming education or experience definitely helps, but it didn't have to be in Python or R. My Java and C++ skills were rusty so I actually answered those coding questions using VBA, which isn't necessarily a popular development language, but they accepted it.
NYC Data Science Academy didn’t mind the choice of language as much as seeing that you can think in terms of programming logic. The point of the bootcamp is to teach you skills that you don't know in a really short and aggressive timeframe.
What is your cohort like? Did everyone come from a similar background as you?
Oh, it’s definitely an extremely diverse group. I am the only management technology consultant. Folks come from academia, research, some just graduated, math PhDs, architecture, law, etc. Data Science as a field probably skews a bit male, but we do have quite a few women in my class.
What's been the biggest challenge for you in the first six weeks?
Personally, my biggest challenge is in statistics. Even though I was rusty in coding, I can pick up computer science concepts and languages pretty quickly. My strong suit is in data visualization and storytelling; the actual analytical process of sifting through data to reach findings and presenting them clearly and succinctly.
Statistics - especially theory - is the area I’m putting most of my emphasis and focus on. That's only my personal story, though. All the people here have such different backgrounds;some might be familiar with statistics but face challenges learning R or Python, and vice versa.
After graduating from a Master’s program, what do you think about this immersive 12-week education style? Is it working for you?
It is definitely working for me. I’m not in university anymore; I don’t have the freedom to spend years slowly learning, and figuring out my life plan. I need an aggressive timeframe and the fact that a bootcamp is able to condense so much into such a short timeframe, but still do a really good job of covering theory to practice, is honestly phenomenal.
If you're in a career-oriented mindset, bootcamps are the way to go. If you have the leeway, flexibility and the luxury to be a student for a couple of years, then university may be a good option for you.
What’s the teaching style? Does it work with your learning style?
It's an awesome mix of lectures and projects. The structure of the bootcamp is one of the things I like most about being here. We always have a couple of hours of lectures in the morning. In the early afternoon we usually have homework review followed by either guest lecturers or project presentations and the rest of the time we work on homework, projects, Kaggle competitions, third party vendor and recruiter visits, and resume reviews. All of that mixes together so that you don't get bored or feel overwhelmed by one specific topic. It keeps things fresh. On the flipside, you need to learn how to juggle.
After six weeks at NYC Data Science, tell us a bit about the projects that you’ve done.
All of our projects thus far have been based on our own ideas. That’s fun and paradoxically stressful at the same time, because the world is your oyster. The choice and selection of the project is up to you, but so are all the downstream impacts, challenges, delays, etc. That approach is great, because you're more likely to be invested in the actual topic of your project if you choose it yourself.
At the six week mark, we are assigned a project that’s based on a Kaggle competition. The teachers want everyone to use the same dataset with the same objective to assess where everyone is halfway through the program.
What's your favorite project so far? Can you tell us about it?
My favorite project was a Shiny web-based application because I like the visualization aspect of it. Shiny is a web-based application development framework. In our case, we tied it to R, and essentially from R we could create visualizations that are easily transposed into a web-based application.
I chose to analyze World University rankings. I studied in France in this system that is quite prestigious but that most have not heard of outside of France. I wondered what determines if a school is “good” and prestigious or not. Kaggle actually had past datasets from world university rankings so I visualized the rankings of all the universities in the world by these three organizations. A user can play around and visualize by country, by university, and more importantly compare how different ranking organizations tend to rank drastically differently.
NYC Data Science Academy, like most bootcamps, is focused on job placement after you graduate. Since you already have a job, are you planning to skip the job prep section of the course?
I’ll probably still participate, just to polish my skills. For example, the code review sessions (i.e. which are basically interview coding exercise prep simulations) are useful to sharpen coding skills under time constraints. I have found it really interesting to solve problems under a time restriction - that’s great preparation for a career as a data scientist.
What are your plans after you return to PWC? Will you move into a pure data science role, or use your new programming skills and machine learning and data visualization in your current role?
Figuring that out is on my to-do list! So far I have had conversations, done some research and discussed with colleagues at PWC. There are several teams where I could use these skill sets but figuring out precisely which one is still up in the air!
Aravind is no stranger to statistics and analytics- he has a Master’s degree in Statistics from Columbia and has been working as an Analyst at a global investment firm building statistical models. But Aravind wanted to strengthen his programming and machine learning skills, so he considered his options and chose NYC Data Science Academy to take his skillset to the next level. Aravind candidly answers all of our questions about why he chose a data science bootcamp over a second Master’s degree, his final projects, and how data science skills have made him a better analyst.
What were you up to before you decided to go to NYC Data Science Academy?
I didn’t attend NYC Data Science as a typical “career switcher.” Instead, I was mostly interested in gaining new skillsets quickly. Bootcamps offer an intense curriculum, but at the same time, are shorter than traditional options.
I already have a background in statistics and have been working for an investment firm as an analyst. I worked with different groups at the firm doing statistical modeling, but I didn’t have as much machine learning and programming experience. That’s what drove me to NYC Data Science Academy.
So you wanted to move up in your career, not change your career?
Yes. I could have continued as an analyst, but data science is a skillset that is designed to solve real world problems using data driven methods. It requires a strong understanding and domain knowledge of programming and statistics, and that was my goal.
One you decided that you wanted to learn those programming and machine learning skills, how did you research your options?
I had two other options, which weren’t bad, but had their downfalls in that they were time-consuming and expensive. First, I could use online courses. The content in machine learning courses on Coursera is very good, but it can take over 8 months to complete a set of courses.
I also considered doing another Master’s degree, but I would be out of work for a long time, and about 30 to 40% of the coursework would overlap with my Statistics Master’s degree. So I decided that a bootcamp was the best option.
How did you decide between NYC Data Science Academy and other data science bootcamps in New York?
I was already familiar with Vivian Zhang’s teaching from her meetup groups, even before she started NYC Data Science Academy. I had applied for the first cohort, but it started in early 2015 and I decided to postpone it for work commitments.
I looked at both The Data Incubator and Metis. The Data Incubator didn’t have a class that started immediately, which I needed. I looked at the coursework at Metis, but they primarily teach Python, and I wanted to learn both R and Python.
I chose the Data Science Academy because of the variety of coursework they offer. We used both R and Python in great detail. Both languages are useful for a data scientist; neither is “better” than the other. I feel that R, for example, may be a great data visualization tool, while Python could be used for analytics and machine learning. At the same time, the latest machine learning packages in R have been promising. Getting exposed to both R and Python was appealing.
At NYC Data Science Academy, were you satisfied with the emphasis on those programming and machine learning skills that you wanted to learn?
There was plenty of material in the curriculum, but we also had a lot of coding sessions where we could sharpen our coding skills. If you really want to become a better programmer, then there is a lot of work that you have to do on your own.
Tell us about the projects that you created while you were at NYC Data Science Academy.
We worked on five projects throughout the camp. We had to complete projects and do presentations, then start on the next project immediately. We were always able to complete those projects in the designated amount of time, but it was very intense.
The projects that we worked on for data visualization were individual projects. The machine learning and capstone projects were group projects.
Can you tell us about your Capstone Project?
My capstone project involved the classification of musical scales. Earlier studies show that songs in different genres can be classified based on signal information. We used classification algorithms to decide whether a particular scale is rock, hip hop, etc. Sometimes you can even classify based on characteristics like whether it’s a minor or major key. Or even more specifically, the mood of the music. For example, there’s a concept called “raga” in Indian classical music with a specific frequency pattern. We fed the computer existing data with what we know about raga, then built a system that automatically classifies music. Companies like Soundhound do a lot of this fingerprinting, which involves a lot of machine learning and digital signal processing.
My Python project was to build a web scraper to collect and analyse rental listings on Streeteasy.
Who was your instructor at NYC Data Science?
Our primary instructor was Christopher, who came from a statistics background. I thought he did an excellent job teaching and communicating each of the algorithms and statistical concepts. He was clear, concise, and effective.
You have a Masters degree in Statistics and have been working with Statistics for the last few years- do you feel like you still learned a lot from Christopher?
I wouldn’t call myself a statistics expert! Even the way Christopher approached simple concepts was interesting. Often with stats, people approach a problem without understanding the conceptual underpinnings behind a particular idea. Chris was able to explain both the mathematical concepts and the conceptual underpinnings.
For example, conceptually, we may say that a t Distribution is “fat tailed” compared to a normal distribution, but Chris would explain why this is so instead of making those basic assumptions.
Did the rest of your cohort have the same background as you? Were there people with different levels of education?
One thing I learned is that at a bootcamp, everyone comes from varied backgrounds. Some students had a Master’s degree in a non-quantitative subject, others had Bachelor’s degrees. Some even had math and physics PhDs- and among those PhDs, some had a theoretical background, while others had programming experience. Those with a computer science background had a small advantage because they had less catch up to do for programming prework.
Everyone had an area that they wanted to improve on. I came from a statistics background, so I was able to focus on topics that I hadn’t had a chance to work on before, like Python.
What was the biggest challenge you faced at NYC Data Science Academy?
During the bootcamp, a bout of flu went around! I had to miss a couple of classes, and then quickly complete a project and present it. I wanted to ensure that the quality of my work didn’t suffer, so I had to work extra hard. I wasn’t sure I could do it, but the support of the TAs was so helpful. Chris made the lessons that I missed available on video. All of those things helped me bounce back and complete two projects really quickly.
What are you up to after graduating in March?
I am with the Asian Markets strategy group that tries to use both qualitative and quantitative strategies for Indian and Chinese Equities. My idea is to contribute to quantitative groups at the company in a better way through machine learning and automation of processes.
Have you gotten to put your new skills to work?
With my programming skills, I’ve been building a tool that takes information from the web about particular news articles about stock. I’m using natural language processing to use that news information in a more seamless way. Plus, my supervisor also feels like those quantitative skills are helping the group.
Were you impressed with the feedback loop at NYC Data Science Academy?
One of the things I have to mention is that Vivian is doing a great job keeping the best aspects of each cohort, and at the same time making sure that each cohort is better than the one before. The feedback mechanism that exists at NYC Data Science Academy is really impressive. I was surprised at the extent to which Vivian valued my opinion as a graduate.
In this new cohort, I made a couple of suggestions, and they have additional hours dedicated to MongoDB and they’re working on a machine learning “defense exam,” which would go with the final project and would be very useful for someone who wanted to prepare for a job. They would get experience with theory and thesis defense, which would give them a better grasp on the subject matter.
What’s your advice to future data scientists who are considering a coding bootcamp?
At the end of the day, you cannot become a data scientist in 12 weeks, so you should learn the most relevant and important concepts. The most important thing is to keep learning after the bootcamp is over. NYC Data Science Academy has made me feel like I can maintain a lifelong commitment to learning.
If you're a college student, an incoming freshman, or a teacher with a summer break, you have tons of summer coding bootcamp options, as well as several code schools that continue their normal offerings in the summer months.
Wondering what a college student or a school teacher can do with coding skills?Continue Reading →
Shin Chin was already working as a data scientist when he decided to take NYC Data Science Academy’s online Data Science Bootcamp. Although he had studied math, engineering and physics at college, he felt he needed more specific practical skills in Python and R in order to move his career in data science forward. He started in October 2015, and talks to us about strengthening his data science skillset, and how learning online with NYC Data Science Academy is already making him a better employee!
What were you up to before you started at NYC Data Science Academy?
My educational background is in electrical engineering. I got my BSc in electrical engineering, an MSc in electrical engineering, and another MSc in physics at the University of Michigan. Then I got my PhD from Penn State in signal processing and pattern recognition. My PhD thesis title was “Anomaly detection in complex dynamical systems” so I implemented an algorithm that I researched and developed to detect anomalies in complex dynamical systems. I didn’t really do machine learning like the kind we do at NYC Data Science Academy.
Right now I’m a data scientist on an Air Force contract. I’m part of a web development team that tries to integrate analytics into the application we’re building. I have knowledge of data science, which is my value-add to the team, but I’m not actually writing code or analyzing large data sets right now. In my previous two jobs, I was a data scientist but I felt I needed to brush up more on my skills in order to succeed.
With that kind of background, why do you need NYC Data Science Academy? What drove you to do a bootcamp style program?
After college I did start interviewing for data science positions. But I felt like my skill level was not up to the degree needed to succeed at big companies like Facebook or LinkedIn, because my background is in electrical engineering, not computer science. My software development and programming skills were not as proficient as someone who is a computer scientist.
Over the last three years I picked up R and Python, but I was not very good. I’m not sure how to use machine-learning algorithms in Python and R to analyze sets, define patterns, and find anomalies. So I thought NYCDSA would help me brush up those skills, improve my understanding of these wonderful machine learning algorithms, and help me implement them practically in a work environment. I’m more of a research scientist and I want to be data scientist in the real world industry, rather than just being a theoretician.
Did you look at other data science bootcamps before you made your decision on NYC Data Science Academy?
I did look at the Python course at General Assembly.
How did you find out about NYC Data Science Academy?
I was looking through news articles about data science bootcamps and NYC Data Science Academy had great reviews. I heard they had a more rigorous curriculum in Python and R than other data science bootcamps.
Why did you decide to do the online version of NYC Data Science Academy?
I didn’t want to quit my job and move to New York City from Washington, DC. It would be too expensive. I talked to NYC Data Science Academy founder Vivian Zhang and told her I wasn’t interested in moving to New York City, and she told me about the online version.
Did you have to be convinced of the bootcamp model or the online bootcamp model, because you had done so much traditional education?
I know I have a strong background in math, engineering and physics, but I felt I was lacking practical skills. My traditional academic education gave me around 85% to 90% of the skills I needed to work as a data scientist for a big company, but the bootcamp will give me that last 10% to 15% to learn other practical programming skills. With these skills I’ll be able to hit the ground running in my first year at a big company.
What have you learned so far at NYC Data Science Academy?
We started with R, then moved on to Python. I haven’t got into Spark, Hive and Hadoop yet, but those are the next tools I’ll learn.
For beginners who are not totally sure, what is the difference between R and Python?
R is a great statistical computing package that a lot of statisticians use. They’re great libraries and great packages that can be used to perform machine learning visualizations. Python is more of a programming language used for a wide variety of purposes like web development. But Python is catching up very quickly because people have developed modules that implement a lot of the same stuff that R implements. A lot of companies use Python. It’s also very good for integrating into web applications. R is also a little more complex to learn than Python. It’s good to learn both because different companies use one or the other.
Do you like using one or the other more?
I’ve been using R more often, but I started to learn Python in the last year or so. I think both have their uses.
In the online version of the class, is job placement important?
Vivian has always been emphasizing that NYC Data Science can help you find a job after you graduate. She always gives me encouraging news about students or hiring companies coming to NYC Data Science to interview students, and tells me about students getting jobs at various companies. Hiring companies are invited to come meet students towards the end of the program, and she is encouraging me to go out to New York City to be present at hiring events. She also sent my resumes out to hiring partners such as BlackRock. I just started the interview procedure.
What is it like to take the online version of NYC Data Science?
It’s 25 to 30 hours a week. They record all the lectures and put them online for me to view them. They also put all the lecture notes and lecture slides on the website. I think it’s better than actually being in the classroom because I can stop the video and rewind. I meticulously listen to the videos, and go through the slides, to make sure I understand everything. There are also homework and projects you have to complete.
I have a TA who’s assigned to me. He helped me setup my environment for Git, Python, R and SQL. He reviews my homework and when I have finished a project, we have a Google hangout where he goes through it, suggests improvements then grades it. If I have any questions, I can call him anytime and he will give me the answer.
Do you get to talk to other people in the class ever or other people doing the online course?
Not really. I think I’m one of the few people doing the online course.
Who is the instructor who is delivering the lectures?
The main instructor, who is very good, is Chris. I’ve never met him personally, but he has a master's in statistics and he’s a great statistician. When he lectures, he gives very good explanations on all the concepts, and includes instructions on how to perform the machine learning.
What types of projects are you working on? Have you done a big project yet?
There’s a final project but I haven’t started working on that yet. I’m still in week 9 and I still have the machine-learning project to finish before I work on my capstone project. I’ve worked on three projects so far, and I’m working on the fourth project now, then the capstone project will be the biggest project.
Do you feel there are things they are teaching in the class that you already know or has everything been new?
Everything is familiar to me, except they go more in depth and I learn more about the algorithms, R, and Python and all the parameters and things that you can do. I learned more and I find myself thinking, “Oh! I never knew this about R.” So they helped me understand it more and gave me new insights into what is going on.
Is there a good feedback loop when a problem comes up?
Yes. Sometimes when I click on the online classroom and the links don’t work, I immediately communicate with the TA and he gets it fixed within a day.
Do you think somebody should have a PhD in order to do well as a data scientist?
I think it really helps to have at least a master’s in a quantitative subject because it’s not about knowing and knowledge, it’s about the method of thinking and analytical skills. The skills you have as a scientist are very helpful as a data scientist.
How are you balancing your studies with a full time job?
On my job, the last 10 months I’ve been working remotely, and my entire team work remotely. I work on NYC Data Science right after I finish my work in the late afternoon, and evenings. I’ve not been going out on the weekends. When my friends ask me to go out, I say I have to work on my studies.
You’re working on a data science team now for the Air Force. Have you noticed that what you’re learning at NYCDSA has made you better at your job already?
Yes, yes. I’m not working in a data science team in my job, I’m the only data scientist on my team. Most of the people on my team are analysts or web developers.
And also the reason why it’s taken a longer time – I signed up five months ago – is because I’ve taken a couple of vacations in between. I can take six months to finish the course.
What’s your dream scenario when you graduate?
To work as a data scientist with the skill sets I have learned, applying what I’ve learned on a day to day basis, and creating value for the company. I like where I’m currently working, so my goal right now is to help them improve their bottom line.
Do you have any advice for people thinking about doing a data science bootcamp?
I think it helps if you have a basic knowledge of statistics and programming skills. Also, be prepared to work hard, because it’s a lot of work. You need to work hard to get the most out of it.
Ben Reid is the founder of Elasticiti, a tech services company that builds advanced advertising analytics SAAS systems for online web publishers. His team uses data to help companies make informed decisions, so Ben sees NYC Data Science Academy graduates as a fantastic talent pool. We chat about ramping up bootcamp grads, his experience with their first bootcamp hire, Sara, and why Elasticiti will continue to hire from NYC Data Science Academy!
Tell us about Elasticiti. Who are your customers and what does your team do?
Elasticiti is a tech services company and we’re focused on helping digital media companies develop really top tier analytic solutions, using a mix of open source tools and their own choice of enterprise-grade technology. We work like a design or architecture firm would to take a raw idea to the next level of focus and strategy. We work in a very rapid, iterative fashion so that people can quickly incorporate information and turn it around in a new draft; that’s key to our process.
Are most of Elasticiti’s employees data scientists?
It’s a mix. We are thrown all sorts of different tasks, some are more in the data engineering realm, some are predictive in nature, a lot of them are visual and design driven. Part of the attraction of the data science background is that versatility in that broad skill set.
How did you get connected with NYC Data Science?
I’d been to three or four meetups hosted by NYC Data Science Academy before I realized we should be working together. Vivian and Janet are really talented and impressive so the conversation progressed from there.
We were looking to expand our hiring profile to include career changers. That fits the profile of someone coming out of NYC Data Science Academy. Our team has a lot of people who are much more senior in their career so this is an interesting complement to it.
What was your impression of the NYC Data Science graduates? Were you impressed?
The other motivating factor for hiring from NYC Data Science was the caliber of the candidates. We went to a couple of their showcases and saw some of the projects that they did, and most importantly, how they thought about the project. The end result is important but equally important is how they worked through all the challenges, what they personally thought was interesting about those questions, what they included/excluded, etc.
It definitely feels like the students there are of a pretty high caliber even before they come into the program. The school has only done a couple of cohorts so far, so the fact that they’ve been relatively exclusive in who they accept is a good sign.
Other than NYC Data Science Academy, how do you usually hire for the analyst roles on your team?
The most effective hiring method I’ve seen is through meetups and networking. You can go to a bunch of database meetups or Python meetups and after a while, you’ll meet the types of people who you need to hire.
What does the relationship between NYC Data Science Academy and Elasticiti look like? Are you paying to hire their graduates or is it just a mutually beneficial relationship?
Right now, there’s no referral fee or money changing hands. We seem to have a mutually vested interest in people graduating from the academy and finding careers.
Tell us about your first hire from NYC Data Science Academy.
We hired Sara Zeid for a couple of reasons. Firstly, she had relatively strong domain experience and had a good foundation in media. The other reason is that she had two degrees in social sciences. A lot of the way we look at the world in media relies on knowledge of sociology and economics so the fact that she had formal training in that was definitely attractive. Prior to the Academy, Sara didn’t have much technical experience, which really for us was neither a pro or a con. We felt that NYC Data Science would provide the broad foundation and we would fill in specific applications after that.
I love that, because I hear a lot of skeptics say; “How can an English or Econ major transition into a technical role?” In reality, those applicants are really strong because their past lives intersect with these new technical skills.
Absolutely. For what we do at Elasticiti, which is very prototype and idea driven, the way we think about problems is central. That willingness and that competence to tackle new things is really what we’re looking for. We can throw Sara a 19-gig file and she’ll tear through it in any number of applications to get down to another data set, and start quickly moving to interesting cuts of the data or finding trends within it to start the conversation with the business.
New perspectives are a great complement to the existing team. A lot of us have been in media for a long time and having someone come to the table and say “why is it done that way” or “that’s similar to something we tackled in week 13” can often bring new thinking to a project.
What kind of mentoring or onboarding is important for a bootcamp graduate?
We do a media 101/102/103, a lot if which probably doesn’t stick because it’s pretty vast, but you want to get an overview of the cosmos. Your project is going to dictate which part of the media universe is really important. We certainly don’t want our new hires to be overly spoon-fed. We want people to be a little self-motivated, too. Media is a large and interesting animal; understanding the habits and traditions of this industry is definitely critical. In addition, for those newer to the workforce, we teach some ‘soft’ skills too which can play a role in progressing a project. These can range from presentation skills, to running effective meetings, and asking questions in a way that gets the most useful response.
Do your other employees have CS degrees?
We actually haven’t hired anybody with a formal CS background. Everybody comes to the table with some other social science or even liberal arts background but along the way has acquired the necessary technical skills. Some have MBAs, a number of folks have other social science backgrounds like Economics. The common thread is genuine interest in problem solving and the tools used (R/Shiny, Python, Postgres etc).
Do you have a feedback loop with NYC Data Science at all? Are you able to influence the curriculum based on your own needs?
I would say that feedback loop is nascent, but we have had some conversations along that line. We’ve also talked about giving NYC Data Science cohorts some sample projects along common industry challenges and mocked-up data sets but we haven’t done anything yet. Client privacy is absolutely critical so their data is off-limits. That said, media is a big hiring industry and we’d love to expose some of the canonical media problems and data science applications. There are a ton of great Machine Learning and Time Series Forecasting examples.
How early on do you get to start interacting with the students? Are you meeting them midway through or at the very end?
NYC Data Science Academy makes them available to us so we talk to them midway through. Not in the sense of “Hey, I’ve got a job for you” but I view everything as relationship building and credibility is so key. We get to talk a little bit about what motivates them and what they’re after and vice versa, and that will grow or not grow as is natural. Those types of things tend to evolve over weeks, if not months.
Will you hire from NYC Data Science Academy in the future?
Yes, we’re already talking to a number of students from the cohort after Sara’s for potential candidates on potential roles. So far, I’m very happy with Sara. There are a lot of interesting people coming out of that group and we’re definitely interested.
NYC Data Science also has a Data Engineering class, and I’m interested in those graduates as well. It’s great to be conversant and capable in both back-end and business analysis but we definitely have a need for people who are really good at what I call “beating up the data” in service of multiple data scientists and analysts.
Would you recommend hiring data scientists out of a bootcamp? Are there types of companies that you would not recommend to hire coding bootcampers?
It’s hard for me to answer the negative side of that for other industries or other types of companies. For us, the attraction was and probably will remain that bootcamp grads come to the table with a wide range of foundational skills and they may come to the table with more advanced niche skills that they want to build upon. That’s what really works for us.
After one year of medical school, Samara Bliss realized that her true passion was in health technology, and that data science skills would be vital to her career goals. Now just days away from graduation at NYC Data Science Academy, Samara tells us how she landed a job at IBM Watson before graduation and shares the most important ingredient to a successful bootcamp experience.
Tell us what you were up to before attending NYC Data Science Academy.
I was pre-med during undergrad and graduated from Columbia with a Bachelor’s Degree in Neuroscience. After graduating, I did a year of research with neurosurgeons and then started medical school in fall 2014. I’ve been interested in health technology for a very long time and planned to work as both a practitioner and entrepreneur. I wanted to be one of those MDs that embraces technology. But in medical school, I realized that clinical practice was not for me. I was so passionate about technology and data that I ended up spending all of my time focusing on that. After asking a lot of people for advice, I decided to go after the type of job I wanted so I left after completing one year.
When you were in med school and doing research at Columbia, did you find that you were able to see the intersection between health and technology?
There could be two parts to this question. One is the intersection of health and data and the other is the intersection of health and technology.
I briefly audited a bioinformatics and health data course in college and I was aware of the importance of data to medicine but wasn’t able to really focus on it. I did quite a bit of clinical research before and during medical school and looking back it’s funny to think how bizarre some of those statistics were. The numbers are so small and the values are often insignificant. Sometimes it felt like we were using small data to make grand, sweeping arguments that don’t necessarily hold up all the time.
There’s another side, health and technology. There were people willing to teach me about it, but I had to work hard to foster those relationships. Medicine is notorious for being very slow to pick up new technology and I found that to be very true throughout my educational experience. In my first year I did this clinical skills class that teaches you how to be a doctor—how to use a stethoscope and related skills. I often felt like we were being trained to teach and practice medicine in the 1950s and not 2030, when we’ll be working in our own practice.
You left medical school after your first year. What did you do after that?
That summer I had a grant from the National Institute of Health to do research with the general surgery team for three months. The grant ended at the end of August and I started the data science bootcamp mid-September.
How did you find out about NYC Data Science Academy?
One of my best friends from college did a Master’s degree in statistics and is currently working as a data scientist. I asked him about it, and he suggested not to do the Master’s degree. He is very connected in the data science world and recommended two bootcamps. One of them was Insight, which I was not qualified for because I don’t have a PhD. The other was NYC Data Science Academy. Technically, I’m not qualified for this program either because it’s supposed to be post-masters or post-doctorate, but maybe one year of med school counts.
There are people without Master’s degrees and PhDs enrolled at NYC Data Science Academy, correct?
Not that many. Almost everyone had some form of advanced degree which is a real selling point for the course. I enrolled in a General Assembly data science course while going through the application process at NYC Data Science Academy and it was great because I was able to compare the two experiences. At General Assembly, my interview was a 10-minute phone call with an interviewer who asked pretty basic questions.
Was that for the GA immersive data science course?
No, this was a part-time class that met for three hours twice a week. I think the course was 60 total hours of instruction. I got the sense that most people were accepted, which works fine for a twice a week, six hour course.
One thing I liked about NYC Data Science Academy is that they tried to get a good group of people together to meet the expectations of their industry partners.
Did you have programming experience or exposure to Python or R?
I took a statistics course in college and we used R in that course. I used it a couple of times, but not when I was doing clinical research. I didn’t have any experience with Python. A background in Python would have been extremely helpful.
Did you need that experience to get through the interview at NYC Data Science?
There’s a coding challenge during the interview process. The application includes two algorithm-type problems that involve writing code in R or Python, whichever one you prefer.
Were you able to get through the coding challenge with your background in R or did you have to teach yourself how to do it?
The challenge was something along the lines of “what is the sum of every third Fibonacci number from one to 1000?”
I thought about it for a while, and I wasn’t able to figure it out how without the help of the internet and friends with a background in data science. On my application, I wrote, “Full disclosure: I did not come up with this answer on my own, but I could explain it.” During my in-person interview they asked me to whiteboard the answer and I was able to explain it. I’d say you don’t necessarily need to know how to do it on your own, but you should be able to write the code and explain what it means.
You mentioned that NYC Data Science did a good job forming a strong cohort. Was your class strong, was it diverse, and was it a good group of people?
My favorite part about the bootcamp is the other students. I was super intimidated by the group in the beginning. It was the most impressive collection of people I’ve ever been in a room with, and everyone had varied backgrounds. There are two people with PhDs in Math, people with PhDs in Computer Science, a person who worked in a hedge fund for 15 or so years and has published numerous papers, a biodynamics PhD—it just goes on and on. It makes it easy to stay late working with these people because they’re so smart and great to be around.
I’d say our professional diversity was stronger than our demographic diversity. When I’m describing the bootcamp to my friends, I like to say the average person is a young, newly married white male pivoting their career. There’s 21 people in the class and only one other female besides myself.
Did you feel that you were on the same level in terms of programing and quantitative expertise. Did you feel that you could keep up?
I was certainly not on the same level and it was definitely hard to keep up. But one thing that’s great about having individuals with varied backgrounds is that a PhD in math may easily grasp the statistics material, but they might not grasp Python as easily and vice-versa. No one in the room knows everything or else they wouldn’t be there.
There was enough support and infrastructure to ensure that I didn’t get completely lost.
Coming from the medical field to technology, does it seem more male-dominated than medicine?
I spent most of my time and did all of my research in the general surgery department and that was mostly male aside from the Chief of Surgery.
Being one of two women in a bootcamp, do you have advice for the many other women who find themselves in that position?
It’s funny because I never thought about it. I rarely think about being the only female in the room until someone reminds me. It was a very mature, focused group of people.
I will say that I’ve never been aware of any difference in experience or problems. If anything, at networking events it felt like people were maybe even more interested in talking to me because they were consciously and subconsciously interested in hiring a female data scientist.
There are also very well-established data science groups for women, like Py Ladies or Women In Machine Learning, for example. There is a lot of support available.
Have you been to meetups for data science or Python specifically?
A few students in my cohort and I went to a three-day conference called PyData recently. There were lectures about cool, exciting things people are doing with Python. It was very applicable to what we were learning at the time.
Have you started a new job yet?
We finish the bootcamp Friday after next. I accepted a position at IBM Watson and sent my acceptance email today.
Who’s teaching the class that you’re taking?
One of the first instructors graduated from Columbia the same year that I did, and he had a background in linguistics. My other instructor had a masters in statistics, I believe. He’s one of the best teachers I’ve ever had.
There’s something to be said for teachers that are recent bootcamp grads because they just learned the information and remember what it’s like to not know anything. They were very good at explaining difficult concepts. There were also a couple other teachers including a math PhD and a guy who was a computer science professor.
There’s a lot of controversy in the coding bootcamp world about hiring TAs who recently graduated from the school. The main argument is that a lot of times, they’re the best teachers.
We have a couple of TAs who were previous students. They all go above and beyond and are there until 11 p.m. every night.
It makes a big difference to have their support round the clock. When the students are working late on a problem set or project there’s always TAs available to help and answer questions. When I think about the cost of the bootcamp and how it went towards paying these people’s salaries so that they were available to me, it was worth it.
Was working late every night the expectation? How many hours a week did you put in, on average?
It depends on the week and the project. It also depends on what you want to get out of the program. What is great is that so many of us in the class have the same mentality—we signed up for this crazy thing for three months, we have to give it our all and if we’re not working hard, we’re not doing something right.
We have lecture every day from 9:30a.m. to 12:30p.m. They give us an hour and a half for lunch during which time people may schedule meetings. In the afternoon we’ll either do a review of the homework, a lab or listen to a guest speaker. The scheduled time usually ends around 5:00 then people start working on homework or projects.
There were probably three to four weeks that I was there until around 11:00 almost every night. This past weekend was the first weekend that I did no work. It was really weird. I woke up on Saturday and said, “this is bizarre. Someone give me a task!” It’s a lot of work, and I think that’s because people are trying to get the most out of it.
Do you take tests that you have to pass?
They have an internal grading system but they don’t show us the grades. It partly has to do with accreditation—they need a way to assess students and attendance. They keep track of attendance, homework, in-class “labs”, and projects. You have to pass each section of the course (there are six or seven). There would be many steps and interventions though before they would finally say you “failed” a section.
Is everyone in your class graduating?
One person left for personal health reasons and he’s actually going to the next bootcamp cohort. I believe one other person left very early on but I don’t know what happened.
Can you tell us about your favorite project that you worked on?
There’s a really large range in terms of the scope of these projects. The coolest project that I worked on was a web scraping project with another classmate. We created a tool that ingests the 20 most recent articles written by any New York Times author and does analysis including word frequency, sentiment analysis, and provides a personality profile of that author. We used the New York Times API and then created a scraper to pull the contents of the articles. Then we did the analysis using some Natural Language Processing Python packages. Lastly, we used an API from IBM Watson called Personality Insights. We fed the twenty most recent articles into the Watson API, and it outputs a numerical, in-depth personality description.
What was the job search process like?
The bootcamp provides a lot of job search resources including making introductions, hosting networking events, and bringing in a resume specialist. I found a couple of companies that were health driven and seemed like a good fit. We were excited to work together, but in the end their data science team was so small that no one would be able to mentor me while I got used to the workflow. They needed someone who could jump in and take on an entire project on their own.
I think that’s typical of any bootcamp grad. They need mentorship in their first months.
Because data science is such a new field a lot of companies have very small data science teams and very little room to take on people who are in the beginning stages. There are places that have much bigger data science teams and have positions like junior data scientist or data analyst. In talking to my classmates, that’s something that they’ve come across as well.
Were you able to get through the technical interviews for those junior data scientist roles?
I just had lunch with one company and both of us acknowledged that they needed someone with a PhD in Molecular Genetics. For the other one, there were four steps to the technical interview and they said, “You were fine on three of them and on one of them you were a little nervous.” I was pretty happy with how I did though. I had just learned Python five weeks ago and I coded out an answer to an algorithm problem in Python that the interviewer seemed happy with. Even though I may not necessarily be able to do a project completely on my own immediately, I was very happy with my increase in knowledge and I think they were as well.
In the midst of that interview with IBM, did you convince them to change you to the technical team?
Not exactly. I think we both realized that my strengths lie in between and that if I was put in a purely technical role, I would be very behind. The role I’m starting in is more along the lines of product management.
What was IBM’s reaction to NYC Data Science? Did you tell them that you were doing it?
They thought it was really really great. In the end, what AI comes down to is machine learning and data science. They were very excited about it.
Did you think that it was worth the tuition? Are there types of people that you would not recommend this bootcamp for?
In order to get out of it what you deserve, you have to put a lot into it. Vivian (founder of NYC Data Science Academy) said that throughout the interview process and when we first started. She was very clear. She said, “We can’t give you results unless you put in the work” which is 100% true. This bootcamp is for anyone who is extremely motivated and sees data science as their future. But at least some previous knowledge of R and Python would be extremely helpful. For someone who wants to casually develop a working knowledge of data science, a General Assembly or Coursera course is ideal.
Is there anything we didn’t cover that you want to make sure we include?
I’ll mention this again—the quality of the students really makes a program. The teachers are great, but the students are the ones you actively work out problems with. I’m hoping that I’ll have this alumni network that I can call upon for a very long time.
Course Report recently caught up with NYC Data Science Academy alumni Sumanth Reddy to discuss how being a poker player relates to data science and his experience searching for a job. Sumanth also shares the interview differences among the most popular data science bootcamps. As one of only two students in his NYC Data Science Academy cohort without a graduate degree, Sumanth proves that at bootcamp, your prior work experience may prove more helpful than advanced studies.
What you were up to before you started at NYC Data Science?
Coming out of college in 2008, I was pre-med before deciding to study physics and pursue a PhD. But, I decided against both for many reasons. I played professional poker for a little while. There were things that were fun about it, but after a couple of years I wanted to transition into something more stable.
I started working at a startup, but I felt pretty stagnant because I was just learning programming and data science but wasn’t able to implement what I’d learned.
I decided I needed to build my portfolio and I started looking up these bootcamps. I liked how short they were because I didn’t feel I needed a year of schooling. I just needed someone experienced in programming to answer my questions and teach me a little bit more about machine learning.
How did your stats experience from college and being a professional poker player factor into this?
I felt like poker was data science at its core. In all my life I have never seen anything like it; it seemed perfect. A lot of jobs are very interested in the fact that I played poker because they see the similarities. So I do believe that poker helped a lot.
Physics, especially quantum mechanics, covered a lot of the core concepts of statistics. It was very similar to data science and the concepts were very important.
I find myself very comfortable thanks to those two things.
Given that you applied to all 3 data science bootcamps, what are some of the differences between Galvanize, Metis and NYC Data Science?
I can’t speak much for Metis because I think their class was full by the time I applied.
At Galvanize, the first thing that I had to do was take a whiteboard coding challenge where I made a function in Python. I also had a question about SQL. It wasn’t extremely complicated. After that, I had another phone call where I had to answer a few statistical questions.
At NYC Data Science, I filled out the application, which contained a couple of coding problems, but I wasn’t under the pressure of a clock or an interviewer. Then I came in for an onsite interview, which just consisted of conversation.
Galvanize and Metis aren’t as lecture-focused as NYC Data Science. NYC Data Science has three hours of lecture and the others only have one. The rest of the time you are supposed to manage on your own. You still have the office hours and everything, but it’s a lot more self-guided. We just always had so much help. I don't know if the other bootcamps had a direct feedback loop with the teachers.
What was your cohort at NYC Data Science like?
We had about 18 students. Compared with the previous and most recent cohorts, we had the most even distribution of girls to guys. I think we were also younger; the oldest person was 35.
I was one of two people out of the 18 that did not have some sort of graduate degree. About half of them have masters and the others PhDs. All very, very accomplished people.
Did you get to work on real world projects?
We started our first project a couple weeks in. We had to do web scraping. We had to pick a website with interesting data and create a question to answer about it. Working alone, we scraped it and created analytics or interesting graphs and visuals.
I’m very sports oriented so I did something about the NBA. The NBA finals had just ended and I wanted to analyze the teams and results. A lot of the star players on the Cavs were injured, and I wanted to see how LeBron James’ numbers fluctuated based on each injury, so I created a visualization.
Did you do a group project?
Yes, we had three more projects. The last two projects were group projects. The first one was a kaggle competition. A kaggle competition is when companies come in and pose problems and allow people to compete. There might be a cash prize, a job opportunity or just an opportunity to practice and learn.
They assigned us groups and we got to pick our own projects and compete. Actually, everyone did pretty well on this. Our group and one other group scored in the top 10%.
We all have our projects on Github.
What was the biggest challenge that you faced during the program?
I thought I was doing pretty well during the first month. Then once we got into machine learning, I started to feel like we were going really fast. I know that other people were already stressed out at that point.
I had to absorb as much as possible and it was all coming a little bit too fast. I didn’t really get to go over things in the detail that I wanted to because I also had to complete my projects at the same time. I didn’t get to catch up on that stuff until the boot camp was over, and I’m still going over it now.
What was the feedback loop like? Were you able to tell instructors that things were going too fast?
Yes, they made it a point to ask us for feedback. They actually gave us incentive to give feedback.On the main page that hosted our lecture slides there was a link that said “Give feedback today.” It could be anonymous or you could post your name. At the end of the week whoever gave the most feedback got a $25 gift certificate. I didn’t get one, but I gave feedback.
On top of that, every couple weeks they had a quick 10-minute forum where everyone could speak freely, because not everyone was giving feedback.
Was there a lot of emphasis on job preparation, interview practice, resume building and things like that?
Yes, we focused on that in the last couple of weeks. They had people come in from a company called 5-Star Resume. They gave us advice about our resume, they helped us touch it up, and they also gave us advice about the interview itself.
The interview tips were very helpful. He showed us how simple social cues can make a huge difference in an interview. For example, don’t bring coffee with you to an interview.
He said, “I know some of this stuff sounds obscure but I know people who have passed every part of the interview and that little thing was what cost them the job.”
Have you been going on interviews since you graduated? Does NYC Data Science have a network of hiring partners?
Yes. I am interviewing and it’s going well.
They helped us set up a lot of interviews through the bootcamp while we were still working on our final projects. They had a bunch of hiring partners come in; most of them were just recruiters but some were higher up. One guy was the principal data scientist and another one, they were building a team. They scheduled a 20 minute interview with each one of us. They also had an employer-specific meetup.
What kinds of positions have you applied for?
I don’t look for anything less than data scientist. I don’t bother with data analyst.
I don’t know that my peers are applying to managerial positions. I think I feel more confident than they do. A lot of them had not even been to a data science interview of any kind before the boot camp, whereas I have been doing it for a few months so I had a good idea of the questions that were coming.
That was another reason why I went to the boot camp, because there were questions in the interviews that I could not answer. I needed to know how to answer them perfectly, and now I do.
Looking back on it, could you have done this on your own without spending the money or were there intangible things that you couldn’t have gotten?
Yes, if you’re smart enough and you have the time and the commitment, anybody can get a graduate degree in computer science just at home doing work on their own.
I can say that, for me, 100% it was worth it – but I will say the boot camp is also about the work you’re putting in and the goals that you have. I really wanted to sponge off of the people around me – not just the teachers, but also all these PhD students around me. It was the most intelligent group of people I’ve been around in such a small space before. I’ve never had such an amazing opportunity.
If you’re doing it on your own it may take you three hours, an entire day to figure out what the problem was because you had never seen it before. But someone with experience will tell you what happened and you’ll figure it out in five minutes.
It’s really about the cost of time; how valuable time is to you. If you have all the time in the world and you’ve got no money, sure, go learn it on your own. But $16,000 for three months of infinite office hours and teaching you from the ground up, absolutely worth it.
What is a Data Science interview like? Do you need a PhD to be a successful data scientist? What types of projects will you be able to build at a data science bootcamp? We asked Jason Liu, a recent graduate of NYC Data Science Academy, all of these questions and more! Read on for his answers, plus advice to other international students hoping to take a bootcamp in the US and how to transition from academia to the fast-paced world of data science.
What were you up to before you started at NYC Data Science Academy?
I finished my PhD in Germany at Ludwig Maximilian university (University of Munich), but I’m originally from Beijing. I was looking for a data scientist position for a while before I started at NYC Data Science Academy. I graduated as a physics Ph.D. last summer (2014). And as a typical physics graduate student, I had no working experience, except research-related job duties for scholarship.
Did you have a technical background before you applied?
Yes, I have worked with programming for many years. Physics is a hard-core field that requires a lot of technical skills. I self-taught most programming skills and took several data science courses from the Coursera.
What programming languages had you used beforehand?
Before the Data Science Academy, I had learned Python and other compiled languages like C and C++. I taught myself Python and learned R for about 1 year so I would have exposure to programming. If you learn one language, it’s easy to switch to other languages; there are a ton of packages that you won’t know how to use, but you’ll be able to pick it up.
What was your goal in doing a bootcamp?
My goal was to get a senior data scientist role. I wanted to leverage the network at NYC Data Science Academy to gain more exposure to high-end jobs.
Why did you choose NYC Data Science Academy? What factors did you consider?
There are couple bootcamps in the market. And NYC Data Science Academy is not the oldest. Actually when I applied, that was the first round. However I was impressed by Vivian’s personal passion and visionary insight about the data science industry.
Did you look at other bootcamps or only NYC Data Science Academy?
I applied to both the Insight program and the Data Incubator program. However, both programs are funded by job placement. As a foreigner, there is no guarantee for those schools that I could get a job immediately after the bootcamp. So I was not admitted by them. I didn’t consider Metis because it is more focused on programming.
What was the application like for you? Did you do a technical interview or a culture-fit interview?
Both. However it was a short and sweet. Basically I went over my CV and answered some questions raised by interviewers.
Did you have to do a coding challenge?
Yes, the coding challenge is one step in the application process. There are two questions, which could be done in a straight-forward way. However I enjoyed the questions because they are also challenging enough to improve the easy solution for better performance.
The coding challenge was a really open question that we could complete in any language that we wanted. Some of my classmates joked that they did the challenge in SQL, which is a database language that isn’t designed for that type of challenge, but they could finish it. It was flexible, but we needed to prove that we had coding abilities; you could have finished it in Excel, but to do the problem quickly and efficiently, you should use a programming language.
Did you get a scholarship to NYC Data Science Academy? How did you pay for the class?
Unfortunately I didn’t get a scholarship. My wife supported me through the bootcamp.
How many people were in your cohort?
I was in the first bootcamp, which had 14 students. There were 5 Ph.D., a couple fresh graduates, and the rest were working professionals. Just few of them had the same technical background/education as me.
Did you feel that your quantitative background made you stronger in the data science course?
Being a PhD student just means that we had more experience and suffered more in our academic lives. It doesn’t mean that we were smarter, just that we pursued academia. Having a quantitative background was very important. There was another student in my cohort who had a Masters degree and seemed to be one of the more qualified students.
If the person is not from a quantitative background but has learned and practiced a lot on their own, they can be just as qualified.
Who were your instructors? What was the teaching style like and how did it work with your learning style?
My instructors are Vivian, Brian, and Jingjing. The class was fast-paced and adaptive. The information was overwhelming at the beginning. However once I got used to the pace, I liked the amount of information and I think the pace helped me to shift from the academia or “research” style (slow) into industry working style.
What technologies did you learn in your course? Were you able to learn it all in the short time you were in your program?
I was exposed to Programming languages, R and Python. I learned a lot of terminologies for machine learning and skills to use them in applied environment. It was definitely hard to learn them all, so I spent a couple more months continue learning them. However if I was looking for a junior position, I would focus on just couple related skills for my targeted job.
Were you satisfied with the curriculum/actual material taught in the courses?
Overall, I like the scope. If there are more time, I would like to learn the current frontier of machine learning.
Were there exams/assessments? What happened if you failed one?
In my round, there was not exams.
Are there things you didn’t expect or that you would change? What was the feedback loop like?
There was a fast feedback loop. Vivian was trying to catch up with comments all the time. I expect to add more Hadoop content with industrial applications.
Can you tell us about a project you worked on?
I worked by myself and spent 4 weeks on it at the same time took the bootcamp. The most challenging part was to build the project from scratch. At each step of development, I needed to decide which functionality I wanted.
Did NYC Data Science Academy do job prep with your class- interview practice, resume building etc?
Yes, the academy accompanied with interviewJet to polish my CV and prepare for interviews.
InterviewJet came in to talk about their platform, went over my CV, and helped me turn it into a short, concise paragraph. Through their platform, a lot of employers have access to my profile.
What are you up to now? Have you gotten a new job?
A recruiter reached out to me when I was in the bootcamp for a job at German company Bosch. It took me a while to get the job, and they are still processing my documentation given that I am international.
What type of job did you get an offer for?
I’ve already got a job offer at Bosch as a Research Scientists specializing in Big Data. It’s a perfect match, because I’m working with Big Data, plus machine learning using Hadoop and Scala.
We learned Hadoop concept at NYC Data Science Academy. While I am waiting for some government document to start my new career, I’m learning Scala on my own. During my onsite interview at Bosch, I had no problem. Once I learned the concepts at the Academy, I could figure anything out.
What is a Data Science interview like?
There were two rounds of interviews. The first was an HR interview- we talked about my background and communication skills. The second round interview was purely programming. We whiteboarded, they asked questions and I had to write scripts. Because this was a research-oriented position, I also presented my peer-reviewed thesis from my PhD. I had to show my presentation skills.
They asked questions about signal processing, which I had worked with in my Physics degree. When I couldn’t answer questions, they gave me hints and we worked through problems together.
Are you prepared to start a job as a Data Scientist?
Yes, I am fully prepared for my new company. There are plenty of researchers around and I am looking forward to work with them. My alumni helped me a lot. I learned from them about how to present myself and engage people about my topics.
Do you have advice for other international students looking for a coding bootcamp?
Typically, you’ll have 1-2 years of working permission after you graduate, so leverage that graduation time in order to take a bootcamp. You don’t want gaps in experience!
Was NYC Data Science Academy worth the money? Would you recommend it?
Yes, I would recommend spending the money in order to boost your career. For the knowledge part, I would say that you could find all the information by yourselves in this Internet era. However there are still some tips and skills that you might not learn until you learn from instructors and classmates. There’s so much value in the connections and opportunities that couldn’t have been learned by myself.
Punam Katariya had a background in data from working as an analyst in market research and her education in math and statistics. Once she learned some programming skills, Punam decided to change careers and enter the world of Data Scientist. After doing her bootcamp research, Punam decided on NYC Data Science Academy because of their syllabus content and the exposure to real experts in the field. Now graduated, Punam tells us about the teaching style at New York City Data Science Academy, her 4 week project, and the biostatistician job offer she received after completing the course.
What were you up to before you started at NYC Data Science Academy?
I had worked as a data and business analyst in market research and staffing industry respectively for total of five years. My education is in mathematics, statistics and business.
Did you have a technical background before you applied?
I didn’t have much professional experience as a coder. However, I was coding in C++ during my Masters program. Because of my interest in data and programming, I was looking for programs on Codecademy and Coursera.
What was your goal in doing a bootcamp?
I wanted to start my big data career as a junior data scientist. My goal from a bootcamp was to achieve hands on experience using software and learn machine-learning techniques on a fundamental level.
Why did you choose NYC Data Science Academy? What factors did you consider?
I chose NYC Data Science Academy because of the syllabus content. I wanted to learn modeling the data and latest modeling techniques. NYC Data Science Bootcamp had good portions of lectures about statistical models using R and Python. Also, they organized industry and field expert workshop and lectures which were very helpful.
What was the application and interview like for you?
There was a coding challenge and a personal interview, I had to go through for application.
Did you get a scholarship to NYC Data Science Academy?
No, I didn’t.
How many people were in your cohort? Did you think it was a diverse cohort in terms of age, gender, and race? Was everyone on the same technical level?
We were 14 people. Yes, it was a diverse group in terms of age and education background. Many of the other students left their industry to advance their career in Data Science. Some people were very good in programming already and some had core knowledge/experience.
Who were your instructors? What was the teaching style like and how did it work with your learning style?
We had two main instructors. One for in R programming and other for Python, D3 JS, Hadoop and Spark. They were always there to help and encourage students. lectures were always followed by hands on examples and homework/In class exercises. Also, all the students were asked to work on their projects during 12 weeks and present in class. Instructors, guest lecturers and guest speakers have lot of experience in their respective fields.
What technologies did you learn in your course? Were you able to learn it all in the short time you were in your program?
The course includes R, Python, D3JS, Hadoop and Spark. It was not possible for me to digest everything in 12 weeks. So, my goal was to understand the materials well and be good in at least one language.
Were you satisfied with the curriculum/actual material taught in the courses?
Yes, I was satisfied, and sometimes overwhelmed, by the material.
Were there exams/assessments?
No, there weren’t exams.
How many hours per week did you spend on NYC Data Science Academy?
I spent more than 60 hours every week on NYC Data Science.
Can you tell us about a project you worked on?
My first project was “Con Edison Hurricane Sandy Outage Data Presentation with R." I worked alone on this project for 4 weeks during bootcamp.
Did NYC Data Science Academy do job prep with your class?
They offered interview practice sessions with professionals in the field.
What are you doing now- did you move up in your career or get a new job?
I have received an offer for a Biostatistician position and paper work is in process. I was applying for jobs after completing the bootcamp. For the most part, I applied on my own. It took me three months to get a job. NYC Data Science prepared me for the interview also. However, initial material and hands on experience on regression helped me with a couple of interview questions.
Was NYC Data Science worth the money? Would you recommend it?
I think NYC Data Science was worth the money for me. I was able get many interview calls and most recruiters were interested in discussing about my experience. I would definitely recommend it. I don’t think that Data Science can be learned so quickly on your own. At bootcamp you are learning the best practices, not only from the instructors and materials but your peers teach you a lot.
With a PhD in Mathematics, Pokman Cheung was no stranger to quantitative analysis, but he wanted to transition into a new career as a Data Scientist, so he enrolled in NYC Data Science Academy to get a grasp on the practical aspects of data science and machine learning. We sat down with Pokman to learn about his experience at the data science bootcamp, the diverse backgrounds of his classmates, and how he landed his new job at Goldman Sachs London!
Pokman also contributes to the NYC Data Science Academy blog- check out his post on Facial Image Analysis.
What were you up to before you started at NYC Data Science?
I had obtained a PhD in mathematics from Stanford, and held academic positions at MIT and Sheffield. However, I decided recently to pursue a new career direction in or related to data science.
Did you have a technical background before you applied? Had you taken a CS/math class, tried Codecademy or another online platform?
I didn't have any relevant technical background from previous work experience. Before applying for the Bootcamp, I had taken several online courses from Coursera and edX on data science and programming. While they provided good overviews of the subjects, I realized that I needed to find another way to gain a deeper understanding and some practical experience.
What was your goal in doing the NYC Data Science Academy bootcamp?
My goal was to get a deeper understanding and some practical experience in data science and machine learning, in order to be able to find a desirable data scientist job.
Why did you choose NYC Data Science? What factors did you consider? Did you look at other bootcamps or only NYC Data Science?
I have looked into similar courses. NYC Data Science Academy appealed to me the most mainly because of their comprehensive and practical curriculum, and their strong industry connections.
What was the NYC Data Science application like for you?
The application consisted of some coding problems and a phone interview. It helped me confirm that I am a good fit for the course, and understand what I am expected for and I can expect from the course.
How many people were in your cohort? Did you think it was a diverse cohort in terms of age, gender, and race?
There were 18 students. This is perhaps the most diverse group of people I have ever studied or worked with, certainly in terms of age, gender and race, but also especially in terms of background and experience. While everyone possessed at least the required technical level, the diversity in background and experience enabled many meaningful and fruitful interactions between the students.
Who were your instructors? What was the teaching style like and how did it work with your learning style?
Vivian, the founder of the Data Science Academy, has vast knowledge of the data science industry and a highly practical perspective. Her ability to share such knowledge and perspective, in the form of class lectures and detailed individual feedback, was in my opinion her greatest value. The other instructors came from such background as healthcare industry, Google and academia. They all share Vivian's practical and interactive style, with particular strengths in various aspects of data science.
What technologies did you learn in your course? Were you able to learn it all in the short time you were in your program?
The course covered various tools and techniques in data extraction (including web scraping), data cleaning, visualization and machine learning. These tools and techniques are mostly implemented in the languages of Python and R. It was a large amount of material, but the instructors made sure that we were able to absorb all of it through well-designed homework assignments, projects and discussion sessions.
Were there exams/assessments? What happened if you failed one?
There were daily homework assignments and four projects throughout the bootcamp, but no exams. We were given extensive feedback on our work.
Are there things you didn’t expect or that you would change?
The bootcamp met, and in some aspects exceeded, my expectations. In retrospect, I would like to have done a little more preparation beforehand in order to even more fully take advantage of the entire experience. The teaching and administrative staff constantly encouraged and responded to students' feedback. In particular, a student-staff meeting -- nicknamed `therapy session' -- was held every Thursday, and any useful ideas and suggestions brought up in the meeting would often be incorporated starting as soon as the following week.
Can you tell us about a project you worked on? What type of data set did you work with, which technologies did you use, what did you find out/discover, did you work on it alone or with a group, is it live now?
After having learned a fair amount of machine learning, the students were divided into teams of four or five and each team started working on a Kaggle competition of their choice. My team chose a competition posed by Ponpare -- Japan's answer to Groupon -- whose goal was to predict the coupons purchased by each user within a one-week period. The provided data included details of the users, details of the coupons, and all the transactions within the previous 51-week period.
Our initial attempt was to train a coupon classifier for each user using some classification method (e.g. neural network, support vector machine). However, poor properties of the resulting models led to the realization that our approach was inadequate in such a situation, where no user would purhase any more than a tiny fraction of all the available coupons.
The approach we eventually adopted was based on quantifying how similar two coupons are using cosine similarity. To achieve an optimal model, we utilized such techniques as feature transformation and cross validation. Our highest score once ranked 7th on the leaderboard.
What was the most challenging part of the course?
I found the projects to be the most challenging but also the most important part of the course, because they required us to not only utilize everything we had learned, but also find or choose our own methods.
Did NYC Data Science do job prep with your class- interview practice, resume building etc?
The NYC Data Science did a great deal to help the students find jobs. Throughout the bootcamp, they invited many industry experts to give talks and provide advice on job application. In the meantime, they also gathered and organized our coursework into personal profiles, which they used to promote us as candidates for suitable openings. Towards the end of the course, there were even more career-oriented activities like 'elevator pitches', meetups with recruiters, interview practice, and resume consultation.
What are you doing now- did you move up in your career or get a new job?
I will start a new job in September, working in the risk management department at Goldman Sachs London. This will be my first job in finance, with a significantly higher salary than my previous jobs in academia.
I started applying for jobs some time before starting the bootcamp, but only received an offer in the middle of it. The bootcamp was useful for my interview preparation, and also gave a positive impression of me to employers.
How long did it take to get the job at Goldman Sachs?
My job application started in February and last until July, a month after the bootcamp started.
Did you feel prepared for the interview with your current company? What is a Data Science role interview like?
I felt well-prepared for the interview. The final interview lasted for a whole day. It consisted of a presentation of a past project and meetings with five people. Besides the typical motivational questions, I was also asked a variety of technical questions, covering such topics as statistical inference, regression models, algorithms and codes, as well as some actual problems arising in my interviewers' work.
Was NYC Data Science worth the money? Would you recommend it? Could you learn that on your own?
I think so. The main values of the bootcamp are: (i) the instructors' knowledge and perspective in the industry, e.g. concerning which ones of the vast number of available tools and techniques are more important than others, (ii) the opportunity to interact with many established data scientists, and (iii) the experience of working on real-world data science projects with guidance from the instructors and collaboration with fellow aspiring data scientists.
With 30 years of experience in the University of Illinois Computer Science department and a stint at Google, Sam Kamin is making the transition into bootcamps. He’s currently designing the curriculum for NYC Data Science Academy’s Data Engineering course. We chat with Sam about the differences between traditional education and coding bootcamps, the world of Data Engineering, and how the NYC Data Science team is preparing for the first day of class on August 24th.
Do you have experience with education or in data engineering?
I was a professor for 30 years at the University of Illinois; I was in charge of the undergraduate Computer Science program for a long time, so my main experience is in education. I also did research and publishing, mostly on programming languages and some parallelism. I went to work at Google in New York, which is all about big data- everything is running on gigantic clusters.
My sister took a class at NYC Data Science Academy and now works here; when they needed someone to teach this new Data Engineering program, it seemed like a great opportunity.
As a professor at a pretty huge research university, did you have to be convinced of this bootcamp model at all?
I was mostly convinced. In the CS program at Illinois, we do have some balance between practical learning and theoretical or more fundamental education. Within the faculty – I’m sure this is true in every department- there’s a range. Some people think on one extreme that programming is just the details that you learn once you know the theory. Other professors think students need to have practical skills to get jobs and that it’s really hard to understand the theory without practice.
I tended to side with a more practical education. So for me, the tension wasn’t that great. I talk to a lot of people who work on Hadoop, for example, who have been in computer science for years; in 6 weeks, you’re not going to train someone to that level. But on the other hand, I meet people who programmed for 6 weeks and have great jobs. The industry is big enough that it can support a wide variety and depth of knowledge.
What is the difference between Data Engineering and Data Science?
Data Engineering focuses on handling big data whereas Data Science focuses on analysis of that data, machine learning, and statistics. Data Science consists of a lot more visualization, whereas Data Engineering is about handling large amounts of data using Hadoop and clusters.
Which programming languages and frameworks will you be teaching in the Data Engineering class?
Python is used everywhere and we’re going to be relying on it heavily, so the students will become expert Python programmers. Python is a general purpose language that we use to grab and massage data, and Hadoop is the framework that stores the data and allows us to process it. So we will teach Python and Hadoop components like MapReduce, Hive, Pig, Sqoop, and others.
Spark is a tool that allows for straightforward analysis on large amounts of data. It’s calling card is that it’s considered to be more efficient than MapReduce. In Data Engineering, there are a lot of different ways of getting at the same thing and analyzing big data. Different companies use different tools; Spark is the latest, so we will cover Spark.
In contrast to the Data Science bootcamp, we won’t be teaching R. R is used by statisticians, but not much by Data Engineers.
Should applicants for the data engineering class have some experience in programming with Python already?
Yes and no; we’re not requiring it but the applicants we’ve seen so far do have some experience. We’re not getting students who are complete newbies – but there’s a big difference between having some experience and being an expert, which is what we intend them to be when they graduate.
We’re going to be teaching people who have some programming experience but probably don’t have PhDs- more bachelor’s degrees.
My understanding is that in Data Science, companies are looking for people with higher degrees. But here that’s not necessarily the case.
What is the application like for the Data Engineering class? Is there a coding challenge?
The application process is fairly straightforward. Here is the link to the application. We are mostly interested in people’s background and reasons for wanting to become a data engineer. I don’t require any samples of code on the application, though sometimes I will ask applicants to send me a sample. Based on their background and the interview we can assess if we think they will do well in the program.
How long is the Data Engineering class?
The Data Engineering class is 6 weeks long and the Data Science program is 12 weeks. We’re offering Data Engineering for the first time so we may tweak that in the future.
What is your teaching style? Will the course be project-based or will it be a lot of lecture?
Fundamentally, it’s going to be a combination of lecture and hands-on, interspersed. There will be homework every night and projects where students will be asked to find their own data sources that they’re interested in and do something with that data.
There’s the overriding imperative to produce a resume or a portfolio and get these students to where they really understand not only the technology we’re teaching but the general lay of the land in the field so that they will interview well and have something to show.
We’ll also do things like pair programming and code reviews. Students will be expected to find some new technologies on the web and give lectures on them, so a pretty broad range of things, but the core of it will be a lecture/lab kind of environment.
I’ve only rarely taught to a small number of students. Even in graduate level classes at Illinois, I had 30 or 40 students. So that will be a different experience and I want to try a bunch of things.
How much emphasis is there on job placement?
We do mock interviews and we have hiring partners. We also host a lot of meetups and have speakers from real companies give talks to the students about what it’s like in the real world. We do a lot to make sure that students will be able to interview well or have something to show. I’ve been spending all my time developing the curriculum, but Janet and Vivian are always working on job prep and hiring partnerships.
Will you give pre-work for students to do before they actually get to the bootcamp?
I think any professor will tell you this: you can give people pre-work but you can’t depend on their having done it. We like Learning Python the Hard Way. But in the case of data engineering, I’m not really assuming any prior knowledge so there’s not really any preparation. I give students suggestions of things they should do but in my experience, it’s not something you should rely upon if you’re teaching.
What is the ideal cohort size for the Data Engineering class?
We will keep this class under 20 students– it is our first time offering this class and we’ll have an instructor and a TA. That’s a nice ratio that we need to maintain if we’re going to support every student and make sure they can all do the homework and projects.
One thing I’ve noticed from observing the Data Science course is that instructors are meeting every day to talk about what happened in the class, which students are having problems, what could be improved in the curriculum, and which students need extra help and on what.
Did you see a similar feedback loop at the undergraduate university level?
No. University is different- every professor teaches what they want, the way they want, more or less. There’s no effective oversight.
Furthermore, the classes are almost never lecture/lab so you don’t get an idea of how well the students are understanding material. I think every school has student evaluations at the very end of a semester, but those serve no purpose in helping the professor improve that semester, and they certainly have nothing to do with a hands-on approach to helping students. I worked hard to teach what I considered the important material for students to learn at that time- and I think almost every professor does- but there’s no real oversight to speak of.
Do you expect every student to make it through the class or do you expect some attrition?
We don’t have traditional tests and our expectation is that everyone will make it through. We’re certainly not planning to weed anyone out. We’re all about supporting every student. Everyone here is really focused on making sure that every single student does well.
You don’t have to be a data scientist to read into these statistics: A McKinsey Global Institute report estimates that by 2018 the US could be facing a shortage of more than 140,000 data scientists. The field of data science is growing, and with it so does the demand for qualified data scientists. Sounds like a good time to pursue data science, right? No kidding! Data scientists make an average national salary of $118,000. If you’re looking to break into data science, or just trying to refresh and hone the skills you already have, Course Report has you covered. Check out this comprehensive list of the best data science bootcamps and programs in the U.S. and Europe for technologies like Hadoop, R, and Python.
A former statistician at Brown University, Vivian Zhang started a Meetup group in New York City in 2013 teaching topics in computer science and data science. As the meetup size and demand grew, NYC Data Science Academy was born, and what started as a set of weekend classes has grown into a full-fledged Data Science academy, which features a 12-week data science bootcamp. We sit down with Vivian to talk about the importance of the R language in data science, keeping a tight schedule at a bootcamp, and the types of applicants who excel in the course.
Remember, the Course Report community is eligible for a $500 scholarship to NYC Data Science Academy!
Tell us about your background and your experience with education and data science!
I got a Masters degree in Computer Science in 2008 and a Masters in Statistics in 2009.
I was working as a statistician with Brown University. I worked with professors, writing the code for their papers; I designed my career path so that I could learn as much as I could.
What motivated you to start the first NYC Data Science Academy classes?
I do a lot of volunteer work; in May 2013 I started my meetup and started teaching people computer science classes. After a while, the meetup group asked me to offer a class, which is how I got started. We started to offer weekend classes in November 2013.
Why did you decide to expand into the 12-week bootcamp?
We saw more and more students were getting jobs as data scientists after just taking the weekend class; we thought we could help more people change their lives by offering the full-time bootcamp.
What types of students do you see at NYC Data Science Academy?
We train three types of people. More than half of them were already in data analytics roles, and most of them were engineers. We also see ~20% who are senior managers; they manage 50 data scientists and need to know what they’re doing right or wrong. So we train senior managers who were already in positions.
We also see around 30% who want to switch careers. They are software engineers or business analysts. I saw a lot of people from academic backgrounds who are stuck in their post-doc, struggling to find a professor position. It’s not easy to make the jump but it’s good that they have a good analytic background so I’m very confident that I can train the programming side and get them ready for the job.
Do most of your students have undergraduate degrees or are they PhD students?
In the current cohort we have 14 students on campus. We have two students who are engineers taking the class remotely from Atlanta. Those two have Masters degrees. Of the 14 people we have in New York, we have 6 PhDs, 6 Masters and 2 bachelors.
The two bachelors degrees we admitted already had a good statistics background. Also, they were already doing data analytics work. They quit their jobs right before the bootcamp. Most people quit their jobs the last day of January to join the bootcamp.
Why did you start the Data Science Academy in New York instead of another city?
I used to live in Silicon Valley. I think New York is becoming the second Silicon Valley so it’s the best place to start training data scientists.
NYC Data Science Academy teaches R as well as Python. Can you tell us a little about R as a language and why it’s important for data science?
I own a data science consulting firm and do data science for a living, and I always find R has an advantage over Python because it has more than 6,000 packages and there are 4 million R users globally.
Since day one, when R was born, it was designed for data analytics and statistical learning. Python is more for software engineering. I would classify Python, Java, and C++ in the same group. People in the Python community are migrating their components into the data science field, which is why they now have Non-Py and Panda. You can use the same syntax to cause a similar function in Python. So it’s getting there but not as far as R gets.
How did you develop the curriculum for the bootcamp?
We spent 15 months testing that out before we started the bootcamp. All the lessons we teach in the bootcamp, we offered over the weekend class; so the lesson on R for beginners, we’ve taught more than 10 times now and the machine learning lesson we’ve offered two times. We validated our material by teaching it at meetups and the weekend class - so when we started the bootcamp, we were ready; we spent months to get here.
How many women do you have in the class?
We have two. In the June cohort we’re going to have 5 women.
We announced the bootcamp on December 15, admissions finished on January 15, and the class started on February 1st. Given such a short amount of time, women were more hesitant to make such an investment.
This time promotion started early and we already have 5 women. I feel like if we give longer time for consideration, we can get more female candidates.
What is the application process? Do applicants need to have technical skills or do they need more logical skills?
We have a programming question in the application form. We want to know what technical level you are, how you work with a team and the most difficult work you have done regarding data analytics.
We need to know if you can be a good candidate for a data scientist position
Did you get a lot of applications for this first cohort?
This batch is amazing. We’ve got a director from Deloitte who quit his job to do the Data Science Academy. He worked at Deloitte for 25 years, got so excited about data science, and decided to become a Director of Data Science instead of Director of Finance.
What kind of job preparation or guarantee are you able to give students? Do you have formal hiring partners?
First, I’m very well connected in New York. I have meetup groups that have more than 3,400 members, with a lot of well-known members. We are also planning a job fair so hiring partners can come in to see students’ work.
Even for my weekend class, students will do a demo day. Last time we did Python class demo day. We have people come in to see the students’ work as they finish the 20-hour class.
I remember one time on demo day, a student brought their grandparents and a parent and they made it like a graduation ceremony.
Are your students in the bootcamp working on a project throughout the whole course or small projects?
The day the get admitted we will start to work with them. Within one day they are making their first project. The day we accept you, we start to work with you.
What kind of pre-work do you expect students to complete?
One of our requirements is we want you to finish 9 Coursera classes before you start class. You need to finish them before you come, unless you run out of time. We also ask you to write 5 project proposals before the boot camp so we can work on examples that attract their attention.
Who are the instructors?
I’m teaching and two of my past students are helping to teach. We have Janet, who had a PhD and MBA. She’s covering the statistics side. We have Brian who graduated from CMU, he’s a CS major. Both of them took my class a year ago and now they are working with me.
I do a three-hour lecture every day. We run the boot camp like West Point. We start at 9:30am, finish at 12:00pm then we have another session from 2:30 to 3:30. I teach R, Python and Hadoop, Brian teaches 3DS and Github.
I think a lot of bootcamps have a really loose structure. I think it’s more efficient if students can get their body and mind prepared. Every day students need to do preview for the next class, you need to do homework, you need to do projects. In the first half hour we do code review, we do presentations, we record all the student presentations. So in week one we really do micro-orientation.
Is there anything else that you wanted to add about Data Science Academy or bootcamps in general?
We encourage hardworking smart people and really dedicated people to apply. This is a gift for yourself. We don’t often get the chance to learn every day for three months so we hope people take that gift for themselves and gain the benefits. And student should keep learning.
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