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NYC Data Science Academy

New York City, Online

NYC Data Science Academy

Avg Rating:4.84 ( 289 reviews )

NYC Data Science Academy offers 12-week data science bootcamps in New York City. In these programs, students learn beginner and intermediate levels of Data Science with R, Python, Hadoop, Spark, Github, and SQL as well as 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. 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. 

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.

Throughout the data bootcamp, students are assisted in preparing for the employment process through resume review and interview preparation. NYC Data Science Academy works closely with hiring partners and recruiting firms to create a pipeline of interest for its students.

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  • 12-Weeks In-Person Data Science Bootcamp

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    Start Date July 6, 2020
    Cost$17,600
    Class size50
    LocationNew York City
    In this program students will learn the modern data analytic techniques and master 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. Along the way, students will have assistance in preparing for the job search through resume review, interview preparation, and opportunities to interview with our hiring partners. Successful completion of the curriculum will present a certification of graduation certified by the New York State Board of Education.
    Financing
    Deposit$5,000
    Financing
    $397.88 pm for 60 months
    Tuition PlansWe have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    ScholarshipLimited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill LevelIdeal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Workhttp://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement TestNo
    InterviewYes
    More Start Dates
    July 6, 2020 - New York City Apply by June 19, 2020
  • Big Data with Amazon Cloud, Hadoop/Spark and Docker

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    Data Science, Hadoop, Spark, Data Structures, Python, Cloud Computing
    In PersonPart Time5 Hours/week2 Weeks
    Start Date None scheduled
    Cost$2,990
    Class size10
    LocationNew York City
    This is a 6-week evening program providing a hands-on introduction to the Hadoop and Spark ecosystem of Big Data technologies. The course will cover these key components of Apache Hadoop: HDFS, MapReduce with streaming, Hive, and Spark. Programming will be done in Python. The course will begin with a review of Python concepts needed for our examples. The course format is interactive. Students will need to bring laptops to class. We will do our work on AWS (Amazon Web Services); instructions will be provided ahead of time on how to connect to AWS and obtain an account.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelStudents are expected to be familiar with using an operating system from the command line; knowledge of Python is helpful.
    Placement TestNo
    InterviewNo
  • Data Science with Python: Data Analysis and Visualization (Weekend Course)

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    Start Date June 13, 2020
    Cost$1,590
    Class size20
    LocationNew York City, Online
    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.
    Financing
    DepositN/A
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    Getting in
    Minimum Skill LevelKnowledge 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
    Placement TestNo
    InterviewNo
    More Start Dates
    June 13, 2020 - New York City Apply by June 12, 2020
  • Data Science with Python: Machine Learning (Weekend Course)

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    Data Science, R, Artificial Intelligence, Machine Learning
    In PersonPart Time7 Hours/week5 Weeks
    Start Date June 13, 2020
    Cost$1,990
    Class size10
    LocationNew York City, Online
    This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. This includes linear regression, Naïve Bayes classifiers, logistic regression, linear discriminant analysis, cross-validation, bootstrapping, feature selection, regularization, model selection, SVM, decision trees, random forest, PCA, K-Means, and Hierarchical clustering. In addition, this course teaches the basics of natural language processing. After successfully completing this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelCompletion of Data Science with Python: Data Analysis; Data Science with R: Machine Learning
    Placement TestNo
    InterviewNo
    More Start Dates
    June 13, 2020 - New York City Apply by June 12, 2020
  • Data Science with R: Data Analysis and Visualization (Weekend Course)

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    Data Science, R, Data Visualization, Data Analytics , Data Structures
    In PersonPart Time7 Hours/week6 Weeks
    Start Date None scheduled
    Cost$2,190
    Class size15
    LocationNew York City, Online
    This course is designed to provide a comprehensive introduction to R. Students will practice programming and analyzing data with R. Students will learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models to data. In addition to a theoretical framework in which to understand the process of data analysis, this course focuses on the practical tools needed in data analysis. This course also covers the creation of dynamic reports with the knitr package in R as well as the creation of dynamic dashboards with R Shiny. By the end of the course, students will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting the code.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelBasic knowledge about computer components Basic knowledge about programming
    Prep WorkNone
    Placement TestNo
    InterviewNo
  • Data Science with R: Machine Learning (Weekend Course)

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    Data Science, R, Machine Learning
    In PersonPart Time7 Hours/week6 Weeks
    Start Date None scheduled
    Cost$2,990
    Class size40
    LocationNew York City, Online
    This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R. It will introduce you to data mining, performance measures and dimension reduction, regression models, both linear and generalized, KNN and Naïve Bayes models, tree models, and SVMs as well as the Association Rule for analysis. After successfully completing this course, you will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, and implement these methods to solve real-world problems. Unit 1: Foundations of Statistics and Simple Linear Regression Unit 2: Multiple Linear Regression and Generalized Linear Model Unit 3: kNN and Naive Bayes, the Curse of Dimensionality Unit 4: Tree Models and SVMs Unit 5: Cluster Analysis and Neural Networks Final Project After 35 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelKnowledge of Python programming Able to munge, analyze, and visualize data in Python
    Prep WorkKnowledge of R programming Able to munge, analyze, and visualize data in R
    Placement TestNo
    InterviewNo
  • Deep Learning with Tensorflow (Weekends and In-Person Only)

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    Start Date None scheduled
    Cost$2,990
    Class size15
    LocationNew York City
    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.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelObject-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)
    Placement TestNo
    InterviewNo
  • Full-time Online Data Science Bootcamp

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    Start Date July 6, 2020
    Cost$17,600
    Class size25
    LocationOnline
    This program was designed for students that have the time to be a full-time student, but can't commute to our school. Students will be placed on a rigorous curriculum that spans from 9:30 AM to 6:00 PM EST as well as have access to prerecorded modules with over 1000 coding challenge questions on online learning platform for additional practice. In addition, they have access to dedicated TA’s as well as the larger network of a shared slack channel between both in-person and remote bootcamp students. Classes: You will learn streamed lectures as well as have access to prerecorded modules and coding questions for additional practice. Personalized Job Support: Students also have access to the full resources of NYC Data Science Academy to help them find their dream job upon graduation. Our curriculum covers the expanse of all the skills required in the data science industry. We cover both R and Python as well as Machine Learning Theory, Big Data, and Deep Learning.
    Financing
    Deposit5000
    Financing
    • Full Tuition Total $17,600
    • Climb Credit Loan $400* pm for 60 months
    • SkillsFund Student Loan $397.88 pm for 60 months

    Tuition PlansWe have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    ScholarshipLimited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill LevelIdeal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Workhttp://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement TestNo
    InterviewYes
    More Start Dates
    July 6, 2020 - Online Apply by June 19, 2020
  • Introductory Python (Evenings)

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    Start Date None scheduled
    Cost$1,590
    Class size40
    LocationNew York City
    This is a class for computer-literate people with no programming background who wish to learn basic Python programming. The course is aimed at those who want to learn “data wrangling” – manipulating downloaded files to make them amenable to analysis. We concentrate on language basics such as list and string manipulation, control structures, simple data analysis packages, and introduce modules for downloading data from the web. This Introductory Python class runs over four weeks, with five hours of class per week (split into 2 ½ hour evening classes). Classes will be given in a lab setting, with student exercises mixed with lectures. Students should bring a laptop to class. There will be a modest amount of homework after each class.
    Financing
    Deposit$1590
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    Getting in
    Minimum Skill LevelThis Introductory Python class is designed for computer-literate people with no programming background who wish to learn basic Python programming.
    Prep WorkIn the class, we will use Python 3. If you are following this video to set up Python environment, please make sure you download the Python 3.X version starting from 1 min 23 s in the video. Link: https://vimeo.com/160172414
    Placement TestNo
    InterviewNo
  • Part-time Online Data Science Bootcamp

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    Start Date July 6, 2020
    Cost$17,600
    Class size25
    LocationOnline
    This is an online part-time self-paced program. Students have 4 - 10 months to complete this program. The curriculum is the same as our on-campus program, with full-financing options, career support and with a one on one support from our mentors. This program is designed for students that work full-time and are not able to quit their jobs. Our curriculum is drawn from data science engagement with corporate consulting and training, hiring partners and active industry participation. Our remote bootcamp ensures that students achieve a very high level of proficiency. Students are expected to dedicate themselves fully to this program and fulfill all the requirements, which include completing lecture videos, daily homework, and four projects. The Remote Bootcamp is built as a collaborative environment utilizing online chat and meeting systems. Students also have the opportunity to collaborate on homework, projects, job applications, interview preparation, paired programming, and even further through our extended alumni community. We work closely with hiring partners and recruiting firms to create a pipeline of interests for students. Each student receives one-on-one support with job searching and access to all kinds of job assistance resources, including coding reviews, interview prep, resume workshop, and access to our exclusive hiring partner network.
    Financing
    Deposit17600
    Financing
    • Full Tuition Total $17,600
    • Climb Credit Loan $400* pm for 60 months
    • SkillsFund Student Loan $397.88 pm for 60 months

    Tuition PlansWe have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    ScholarshipLimited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill LevelIdeal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Workhttp://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement TestNo
    InterviewYes
    More Start Dates
    July 6, 2020 - Online Apply by June 19, 2020
  • Deep Learning
    - 2/9/2018
    Mahipal Singareddy  User Photo
    Mahipal Singareddy • Student Verified via LinkedIn
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    I attended the Deep Learning course at the NY Data Science Academy that was taught by Jon Krohn during October 2017 - December 2017. Overall it was exactly what I hoped it would be. It gave me a strong foundation of all the core deep learning concepts. The class was hosted on every other saturday which allowed enough time to fully explore a particular topic between classes. Jon made a particular effort to keep the material simple and explained the concepts intuitely rather than with complicated math. Jon has great understanding of the content and is well prepared for each lesson. I thoroughly enjoyed the class and it has motivated me to pivot my career into deep learning. I would recommend this class to anyone who is passionate about deep learning but don't know where to begin. It is also great for anyone who has worked with some but not all of the deep learning techniques.

  • Patrick Masi-Phelps  User Photo
    Patrick Masi-Phelps • Data Scientist • Graduate Verified via LinkedIn
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    TL;DR: The NYCDSA full-time bootcamp is a great way to start a career pivot into data science. The curriculum covers the most important topics in the space; the instructors are accessible and actually care; and you'll get out what you put in.

    The first four weeks are python and R basics. These can get a little tedious and heavy on programming, but are important so that you can fly around when doing the more advanced stuff. The next couple are on web scraping, then a few more on machine learning, then some on more advanced topics like distributed computing, time series analysis, and deep learning. 

    The 3-hour morning lectures are helpful and touch on the most important issues from a high level. The homework assignments (usually every other day) and projects (4 total) are how you actually retain the information. If you stay on top of these assignments and aren't afraid to ask for help, you'll learn so so much! I thought the first ~8 weeks were thorough and well-paced. I thought the last 4 weeks rushed through a number of advanced topics without enough time to retin the information through projects, homeworks, and other "real world" practice. I'd recommend choosing a few of these topics to really focus on in your projects rather than trying to understand everything from the last 4 weeks just from a high level.

    This particular bootcamp has a lot of industry connections. After graduating, the hiring partner cocktail event will lead to a number of interviews with companies looking for entry level people.

    Taking this bootcamp (or any other, frankly) provides a stamp of legitimacy on your pivot to data science that you probably couldn't get from just teaching yourself python and doing online coursera courses.

    You'll get out what you put in with NYCDSA. Sometimes this means 60 hour weeks right before projects are due.

  • Raj Tiwari  User Photo
    Raj Tiwari • Research & Analytics • Student Verified via LinkedIn
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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.

  • John Chen  User Photo
    John Chen • Student Verified via LinkedIn
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    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. 

  • Thomas Kassel  User Photo
    Thomas Kassel • Data Scientist • Graduate Verified via LinkedIn
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    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.

  • Aarsh Sachdeva  User Photo
    Aarsh Sachdeva • Quantitative Research Portfolio Analyst, Derivatives • Graduate Verified via LinkedIn
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    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. 

  • Jake  User Photo
    Jake • Graduate Verified via LinkedIn
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    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 

  • Scott Edenbaum  User Photo
    Scott Edenbaum • Graduate Verified via GitHub
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    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.

  • Haseeb Durrani  User Photo
    Haseeb Durrani • Data Scientist • Graduate Verified via LinkedIn
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    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. 

    Job Hunt:

    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. 

  • Great Bootcamp
    - 9/10/2017
    Cheryl  User Photo
    Cheryl • Graduate Verified via LinkedIn
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    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.

  • Chao Shi  User Photo
    Chao Shi • Graduate Verified via LinkedIn
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    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.

  • Great Bootcamp
    - 8/20/2017
    Claire Vignon  User Photo
    Claire Vignon Verified via Linkedin
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    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

Thanks!