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NYC Data Science Academy

New York City, Online

NYC Data Science Academy

Avg Rating:4.83 ( 272 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-Week Immersive Data Science Bootcamp (In-person)

    Apply
    Start Date Rolling Start Date
    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
    • Climb Credit Loan $400* pm for 60 months

    • Full Tuition Total $17,600

    • Skills Fund 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
  • Big Data with Amazon Cloud, Hadoop/Spark and Docker

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    Data Science, Python, Hadoop, Spark, Data Structures
    In PersonPart Time5 Hours/week2 Weeks
    Start Date January 14, 2020
    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
    More Start Dates
    January 14, 2020 - New York City Apply by January 10, 2020
    April 21, 2020 - New York City Apply by April 15, 2020
  • Data Science with Python: Data Analysis and Visualization (Weekend Course)

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    Start Date January 19, 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
    January 19, 2020 - New York City Apply by January 15, 2020
    March 7, 2020 - New York City Apply by March 1, 2020
  • Data Science with Python: Machine Learning (Weekend Course)

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    Data Science, R, Machine Learning, Artificial Intelligence
    In PersonPart Time7 Hours/week5 Weeks
    Start Date January 19, 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
    January 19, 2020 - New York City Apply by January 15, 2020
    March 7, 2020 - New York City Apply by March 1, 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 January 18, 2020
    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
    More Start Dates
    January 18, 2020 - New York City Apply by January 15, 2020
    April 18, 2020 - New York City Apply by April 10, 2020
  • Data Science with R: Machine Learning (Weekend Course)

    Apply
    Data Science, R, Machine Learning
    In PersonPart Time7 Hours/week6 Weeks
    Start Date January 18, 2020
    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
    More Start Dates
    January 18, 2020 - New York City Apply by January 15, 2020
    April 18, 2020 - New York City Apply by April 15, 2020
  • Data Science with Tableau (Online)

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    Data Science, Data Visualization, Business Intelligence
    In PersonPart Time5 Hours/week3 Weeks
    Start Date Rolling Start Date
    Cost$1,590
    Class size20
    LocationOnline
    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. 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. We utilized 4 user cases drawn from finance (public data from major stock exchanges) and sitcom data (Game of Thrones 1 ).
    Financing
    Deposit1590
    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 LevelKnow 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 WorkBefore 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).
    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
  • Introduction to Python (Online)

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    Data Science, Python, Data Visualization, SQL, Data Structures
    OnlinePart Time5 Hours/week2 Weeks
    Start Date Rolling Start Date
    Cost$795
    Class size25
    LocationOnline
    The Introduction to Python covers the basic Python programming. Graduates will be able to write Python programs that read files, manipulate strings, and perform mathematical computations. The class is hands-on; participants will learn to write Python program by practicing coding along the way. The course covers most of the basic features of the Python language, including built-in data types and control structures, Python’s featured supporting object-oriented and functional programming, intro to Pandas, and regular expression.
    Financing
    Deposit795
    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 LevelComfort with Windows, Mac or Linux environment and ability to install third-party software.
    Prep WorkThis 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.
    Placement TestNo
    InterviewNo
  • Introductory Python (Evenings)

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    Start Date January 14, 2020
    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
    More Start Dates
    January 14, 2020 - New York City Apply by January 10, 2020
    March 9, 2020 - New York City Apply by March 5, 2020
  • Remote Immersive Data Science Bootcamp (Online)

    Apply
    Start Date Rolling Start Date
    Cost$17,600
    Class size25
    LocationOnline
    Our Remote Intensive Bootcamp is an online full-time program. We live-stream our on-campus classes to the remote intensive bootcamp students. 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. Live Classes: You will learn real-time streamed lectures as well as have access to prerecorded modules and coding questions for additional practice. Live Communication: They will learn real-time 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
    • Climb Credit Loan $400* pm for 60 months

    • Full Tuition Total $17,600

    • Skills Fund 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
  • Remote Self-paced Data Science Bootcamp (Online)

    Apply
    Start Date Rolling Start Date
    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
    • Climb Credit Loan $400* pm for 60 months

    • Full Tuition Total $17,600

    • Skills Fund 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

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  • Andrew Rubino • Data Lead • Graduate
    Overall Experience:
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    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.

     
  • Shivakumar Ranganathan • Graduate
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    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.

  • Samriddhi Shakya • Data Scientist at QxBranch • Graduate
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    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.

    Cheers NYCDSA

  • Worth the effort
    - 11/7/2017
    Chris Lian • Graduate
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    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.

     

  • Dave L • Graduate
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    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.

    The Good:

    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.

    2. I've been in my current job about ten weeks, and I've already used most of the curriculum.  I use R every day, Python frequently, and a fair amount of SQL and Mongo. Good BI visualization can really set you apart, so the early part of the course has been most valuable. I've already done a fair amount of scraping, too, and occasionally contributed on some machine learning.  The only thing that would be useful for my job that the program didn't cover was JavaScript, but that's really more relevant to my field (advertising) than data science.

    The Solid:

    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.

  • Katie • Data scientist • Graduate
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    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.

  • Yabin Fan • Data Engineer • Graduate
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    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.

     

  • Andrew • Quantitative Programmer • Graduate
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    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. 

  • Mark Schott • Data Engineer • Graduate
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    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.

     

  • Jessie • Graduate
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    How I started

    When I started getting interested in data science, I was looking for any online courses/ bootcamp to help me get into the data science world. And I found out NYC Data Science Academy was the best out of all the data science bootcamps in terms of curriculum, and after talked to Claire and Vivian about how they helped students to locate job after the bootcamp, I decided to apply for the full-time bootcamp.

    Preapre for the full-time bootcamp-Prework

    After I got admitted, I got access to the online prework courses (Python and R introductory courses). I really love how they organized each video and there are exercises following with each part in the video. I didn't know anything about R and Python before, and the pre-work assignments are overlaped with the content of the first 4 weeks of bootcamp, so it helped me to adapt to the speed of the class in the bootcamp.

    12-Week Full-Time BOOTCMAP

    The bootcamp was very intense, the curriculum was comprehensive, which covered from data anlysis, machine learning in both Python and R, and introduction to Big Data tools. We also did 4 different projects, which was important especially when it comes to job hunting. 

    I didn't look for full-time data science jobs after the bootcamp because I got admission to the master program at Columbia University, and thanks to my experience at NYC Data Science Academy, I can handle the programming and data work in my current courses and projects. 

    And I still receive ongoing career support from the team and chat with instructors and students time from time now. It was so nice to work with all these hard-working and passinate people in the data science world.  100% RECOMMENDED

  • Cristina • Graduate
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    The 12-week data science bootcamp is a highly useful experience both for people looking to transition into data science and those who seek to expand their skills set in their current field. A core dimension of the program is exposing and drilling you in a range of technologies that are very sought after on the market for data scientist/data analyst/quantitative analyst positions, spanning fields from tech, finance, healthcare, marketing, research etc. On this front, a distinguishing feature of the bootcamp is that you work in both either R and Python as primary languages, so that you become proficient in both languages by the end of the bootcamp, which is very valuable. Besides these languages, the bootcamp also teaches staples of database, file management and version control like bash, git, SQL (MySQL, SQLite) and MongoDB as well as in demand big data tools like Spark, Hive, Hadoop, AWS, etc. This is a very powerful set of tools to have under your belt. Certainly, the more familiar you are with these technologies before joining the bootcamp, and the more intense effort you are willing to put into practicing these skills, the more you will be able to get out of the experience, and you must be prepared to absorb new technologies very fast, but at the bootcamp, you get a structured and very supportive environment to help you achieve this. The TAs are excellent, very dedicated, and willing to assist you with any question or problem you may have.

    A second core dimension of the program is anchoring you in the mechanics of statistical inference, machine learning algorithms, and data mining. The curriculum is grounded in canonical texts in machine learning and is organized such that you get to cover a lot of ground in a short time. The concepts are explained very clearly, first from a mathematical perspective and then implemented step by step in both R and Python. The instructors have various specialties in which they teach, are extremely knowledgeable and can answer questions at any level of depth.

    Tying these first two dimensions together are mostly daily problem sets and five projects, where you get to use the skills you are learning. For the projects, you can even go beyond, learning and adding a technology or skill that may not have been directly taught, if it serves your project. You will have ample support from the TAs and instructors through all this, and your classmates will make a very useful learning community. For the projects, it is helpful to give yourself plenty of time in advance to come up with ideas, but here again your TAs, instructors, and classmates will be very helpful sounding boards.

    The third core pillar of the program is job search assistance. During the bootcamp there is a series of job search preparation lectures that include resume review, search and interview tips, interview type quizzes, and industry guest speakers. You will be encouraged, given many useful tips, and get motivated to become very disciplined and efficient in your approach. At the end of the bootcamp, each cohort is invited to a career event focused exclusively on the newly graduated cohort, with hiring managers from a diverse set of companies. You won’t be handheld, and you must apply discipline and commitment to your search, applications and preparation for interviews after the boot camp ends, but the job search assistance from the bootcamp will continue until you find a job, with the opportunity to interview with companies that hold interviews at the bootcamp, referrals to positions you’re applying through the frequent advertisements sent to alumni or to positions you apply on your own, a bank of practice interview questions, interview performance review, and an alumni dedicated place to come work on your search at the bootcamp headquarters available to all alumni.

    The bootcamp is a very intense experience and you have to commit yourself to it 120% percent, but you will leave with a skill set that will advance your career, a set of projects and a certification that, in my experience, will attract many recruiters, the drive and inspiration to push yourself to reach your maximum potential, and a support network that will be there for as you pursue your professional dreams post-bootcamp. In addition, in my experience and that of several of my cohort classmates, both during and after the bootcamp, you will become an independent learner in the field of data science who is capable to advance and add to their skill set on their own.  This is an invaluable skill in any data-related role you will hold. In sum, the bootcamp is a highly valuable experience that will leave a very positive impact on your professional life. I am very grateful I made this choice and I can only wholeheartedly recommend it to everyone else.

  • Wann-Jiiun Ma • Fraud Data Scienst • Graduate
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    I was a postdoc at Duke University (Engineering) and my background is control and optimization. I participated NYCDSA Online Data Science Program (10/2016 - 02/2017).

    NYCDSA online video lectures have a very high quality. I found that the well-organized class material and lecture notes very helpful to prepare for data scientist job interviews. I can review the material repeatedly until I am ready for the interviews. Also, almost everything I need to pass a data scientist job interview is covered in the program, which can certainly facilitate my interview preparation.

    Finally, Vivian and the entire NYCDSA team are extremely helpful for data scientist job searching. From resume/cover letter revision, coding practice, to data scientist technical interview preparation, NYCDSA has enabled me to make a smooth transition from academia to industry.

    I found a job at Citibank as a Fraud Data Scientist. Without NYCDSA online program, I would have to spend much more time to find the right direction to achieve what I have been able to accomplish through this online program.

  • Chris V. • Marketing Science Manager
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    I can’t begin to describe how inclusive, rigorous, and rewarding is New York City Data Science Academy’s Data Science Boot Camp. It is inclusive in the sense that those applicants it admits are not exclusively from among statisticians, mathematicians, and computer scientists. Professionals from other disciplines who recognize and appreciate the importance of a more structured, quantitative approach to business comprise a significant component of each boot camp cycle, providing context, breadth of experience, and real-world application to the data science discipline. In turn, they benefit from the experience and capabilities of their classmates specializing in more technical disciplines.
     
    The boot camp program is rigorous in that it demands broad competence not just in a single language. While competing programs may focus exclusively on developing competence narrowly in R or Python, the NYCDSA boot camp demands that its recruits master both. This gives those who complete the program greater flexibility, as they can appreciate the strengths of each language and invoke one or the other as best suits the task at hand.
     
    Apart from the new skills obtained, the NYCDSA boot camp is rewarding in other key respects. The curriculum is grounded in teamwork, giving participants a greater appreciation of how to build and manage teams in a business context. This team-based approach also fosters friendships among classmates who, as in a military boot camp, become battle buddies as they overcome the significant business and technical challenges they confront. The resulting camaraderie produces a very strong alumni support network that provides boot camp graduates with long-term opportunities for professional growth and advancement.
     
    The NYCDSA boot camp provided me with the precise set of skills I sought, allowing me to continue my career on a higher plane and enrich the value I provide to my clients in their pursuit of market share and increased profits. I am incredibly grateful and appreciative of the opportunities which NYCDSA’s boot camp has afforded me, both personally and professionally.
  • Great Experience
    - 8/6/2017
    Eli Raymond • Technical Analyst
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    I took the Part Time Into to Data Science with Python class as a means of hands on introduction to some of the more common and powerful Python libraries used for data science, and I found it to be perfect for that.  This is certainly not as rigorous as a bootcamp, but if what you're looking for is an introduction or even something to supplement learning on your own time, I highly recommend it.  I would definitely say I am now comfortably proficient with the basics of each library covered and I have a great foundation I can build upon.

  • Reza • Student
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    I took the Intro to Python and Python for Data Analysis/Viz courses. I came in not knowing much about python but came away knowing how the syntax and how I can use python for my job. I took both classes with Tony and he was great at explaining the nuances of the program without getting us lost in the unecessary details.

    I would recommend taking these classes if you want to learn python while also being introduced to aspects of data science and analysis.

  • Annie • Student
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    I will strongly advice not to take up online for the 12 week data science bootcamp offered by NYC data science. It is not worth the money you pay. It is better to do an MS with the same amount. Reasons

    1. Lectures are unprofessional. Just plain text and not much plots or anything to really explain things

    2. Instructors are just there to read the slides aloud and nothing more. The machine learning lectures and slides are just terrible. It is better to take up interactive tutorials such as datacamp than to review this. There has been no change or improvement from the school having brought this up.

    3. Teaching assistants provided are not very knowledgable. They are just there to review your project. You are really lost when you have to ask them a question

    4. Online is a big mistake because you are not learning anything significant other than what is being taught as there is less interaction with other students.

    5. Projects are just namesake. 

    I will remind you - DO NOT TAKE UP ONLINE FOR THIS BOOTCAMP. 16K IS A HUGE AMOUNT

    If only I could turn back time and run with my 16k. 

     

  • JC • Graduate
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    This is a highly intensive program with immediate returns. If you want to turbocharge your career progression in the world of data analytics, this is without a doubt the best decision you can make. However, this isn't a free pass into the field of data science; if you do not have the passion or are not willing to put in the work, it will be a waste of your time and money.

    Prior to enrolling in this program, my major consideration was: If I skip the program and use the free resources online, would I be able to learn this stuff on my own?

    Naturally, this was the path I initially pursued. But with a full time job, even if you put 2 hours aside each day to learn on your own, there will be days where you are too drained and lack motivation. Leaving my employer and investing in this program kept my accountability up and allowed me to gain new skills in 12 weeks that would have otherwise taken months or years of consecutive study. The content in the program is not secret sauce. Things you learn here can be found for free online. But in this generation, what can't be found for free online? Where this program shines is the extremely bright instructors and peers that you get to interact with on a daily basis. There were countless times during the lectures in machine learning where I was lost. If this was a MOOC, I would probably be replaying the video for the 7th time and then hopelessly closing the tab. At NYCDSA, you have access to some of the most talented and extremely patient resources (with the credentials to back it up) to help you until you understand what's going on.

    The instructors are there for your to utilize, but you need to take action on your own. If you get the instructors engaged, their passion for data science is very contagious, and you will learn a lot just by bouncing ideas off each other and actively problem solving. This will be extremely advantageous for your projects. Please don't sit idle and let the instructors debug your code, you will learn nothing except how to press ctrl + enter.

    In regards to job assistance, NYCDSA is a great way to expand and grow your network. Vivian and Chris both have deep connections to the data science employers in the NYC area. Although the company I am now employed at wasn't a result of the school's networking, I have gotten many interviews and employers reaching out to me solely due to Vivian and Chris. At the end of the day, getting a job requires putting in the work. Do not expect to come out of the program with employers throwing themselves at you for simply having NYCDSA on your resume.

    Vivian and Chris provide great support to help you improve your job search experience, but they will not baby you. There are 40+ students they need to support. If you need help your best bet is to reach out instead of waiting around for Viv and Chris to ask you if you're okay.

    My overall rating is 4 stars due to the course pacing. Whether this was an effect of too many/ too diverse of a class, or change in instructor(s), There were a handful of lectures that went too far over the scheduled time. For some of the later lectures in the program, there was a pattern of discontinuation of lectures. This was difficult to manage especially if it occured over a weekend. For example, if a Friday lecture ran over time, it would carry over to the next morning session on Monday. I would have much rather prefered a continued lecture in the afternoon while the material was fresh in my head. Although this was my personal experience, rest assured that Vivian and Chris are constantly improving the student experience and something like this is probably already being worked on.

    TL;DR: come in with a passion, put in the effort, there is much to be gained

  • Solid Class
    - 6/8/2017
    Kevin M • Graduate
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    This course was a solid intro to using python for simple data analysis projects. Tony was a great instructor - he was very thorough with his explanations and made sure that we got through all of the materials necessary for us to progress through the curriculum. My main knock on the class was how short it was - if you're thinking about taking this class, keep in mind that there are only 5 classes, so all of the material that needs to be covered is jam packed into each session. While we did end up covering everything as displayed on the curriculum, it's harder to remember everything that is densely packed into each class.

  • Arjun Singh Yadav • Data Scientist • Graduate
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    I am Data Scientist at Anheuser Busch today and I am going to share my experience with finding the right school and struggle that I faced.

    Search for Schools:

    It was hard to choose the right school for Data Science just by being online, I had recently graduated as a robotics and automation engineer and all my programming skills were result of self-education, In late August I was selected for the Interview process at New York Data Science Academy and General Assembly.

                                              I first came to New York Data Science academy and I was interviewed by one instructor and one Business person, I never met them after that day so I don’t remember their names now, they asked me a series of programming questions, pretty basics some were based on the online application I filled and later some on my knowledge of statistics, it seemed to go well but at the end of the interview I was rejected.

                      The reason was I was not ready for the bootcamp, they had previously seen other student promise that they can learn quickly but what ended up happening was they left the bootcamp in middle, I was upset cause I really wanted to do it, but I also knew I was in a hurry, they suggested to take it slow and come for next cohort so I will not just be able to survive the bootcamp but also learn and really take something valuable from the camp. 

                     I later went to General Assembly and attended their interview and introductory session, they had a much larger location, also a big conference room, I really like the place, I was provided an instructor who gave me a brief understanding of the course and what it would feel like. A week later I had my interview with Lally M, I had filled and application online but I never answered any programming questions, Lally was very nice and asked me few general questions, and also gave me few advices but all and all she did not really bothered if I was ready for the cohert, at the end of the interview she said I can join this cohort. 

    Prework:

                      It’s obvious what I did next,  I left the option of General Assembly and prepared on my own for next 3 months, I met Vivan personally In November she is the Co-founder of New York Data Science Academy, she gave me access to a online learning and testing platform which is called the prework for the bootcamp, it covered all the basics of Python and R.

    During the Bootcamp:

    My Bootcamp started on 09th January 2017, I felt very confident during the bootcamp, I remember Chris said on the very first day "There are very few things in life that will give you a chance to have absolute devotion", in the beginning we were introduced to all the subjects again for first week, so even if you did not do prework you would not suffer entirely but I would suggest do your prework well.

                                 Things started to get serious as soon as second week finished and it did not stop after that, we had classes from 9:30 AM to 3:00-4:00 PM, after which there was homework at the end of each day, added to it we had to do our first project, also I had to study for the subjects I learnt in the class, also I had to keep my LinkedIn and resume up to date, also you better keep going to networking events, all in one day with you getting enough sleep and food to have the same energy the next day.

    Curriculum:

    1) Introduction to R and Python , Unix (which helps in big data part)

    2) Machine Learning - (a) Importing Data (b) Foundation of statistical Methods (c) Missing value and Imputation (d) Simple Linear Regression (e) Multiple Linear Regression (f) Generalized Linear Model and kNN Model (g) Regularization and Cross Validation (e) Tree Models, Bagging , Boosting , Random Forest (f) SVM (g) Time Series Analysis (h) Cross-Validation (i) Bootstraping (j) Feature Selection (k) Regularizartion (l) Hyperperameter Tunning (m) SVM (n) Tree Models (o) Association Rule (p) Naive Bayes (q) Principal Component Analysis (r) Clustering (s) Unsupervised Learning (t) XGBOOST (u) Tensorflow (v) Netral Network (w) Concolutional Neural Nets.

    3) Big Data - (a) Hadoop (b) pig (c) hive (d) nosql (e) Apache Spark (f) DATAiku(platform) (g) DataBricks(platform) (h) Virtual Environment - Docker.

    Project:

    1) Try to solve real life problems in your Projects, you will have the freedom to choose your projects, choose them wisely. I had a chance to do one of the Kaggle project, it helped me learn from some of the best Data Scientist in the world and if I did not understand something in any project, the teaching assistants came to rescue.

    2) You will do 4 to 5 projects including Capstone, finish them well, do not leave any part mid way, submit your code on Github and you will be told to write blogs on the platform provided by the academy, write them well.

    Job Assistence:

    1) Mock Interview sessions - Coding Test, theory of Machine Learning Test.

    2) Resume Writing Session - Resumen writing, cover letter, Job status sheets, email - cold warm, Meeting Letter, thank you notes.

    3) General Prep Session - Photo Shoot, Linkedin, In house Meetups, In house video recording during project presentation.

    4) Interviews - Hiring Event, Introduction to Employers, Referal.

    The Bootcamp:

    The bootcamp changed my life as I am no longer the same person I was before January, even though 50% students in my batch had a PhD, 46% had a Masters and many years of job experience, I was among the 4% Undergraduate category who luckily got here, but this played in my favour as I learnt a lot from my peers, I truly came out a Star in my own eyes, I would like to give the credit to Vivan and Chris as a host in my Ex Home, who were supportive at the end of each day, coming up to you and checking on  you if you are still doing okay, the reason I finished all my projects in Flying colours was my Teaching Assistants, who stayed back every day till 7:00PM sometimes and came back in at 8:30 AM, yet managed to stay active on Slack during the night during, I would also like to thank my peers with who I learned many Industry Skills and Best Practices, during my group projects I was led by a Math PhD Mr. Domingos, who now works at google, I had a chance to grow enormously on his team.

    At the end of the cohort we all graduate in flying colours, it was very emotional day, thank fully we all are still in touch, I strongly suggest to make meaningful relationships during the cohort, after the school, you constantly have doubts and questions, like after job interviews, during your projects in the company, your colleagues will help you understand and overcome these doubts. 

    After the cohort we had our official Hiring day just a week later on 5th April 2017, I managed to meet 12 - 15 different companies, out of which the one I had a heavy interest in was IBM (who would not). Later I got a referral from IBM and I am currently Interviewing as a Data Scientist for IBM advance Global Analytical Team. I was trained to crack these interviews and trained to speak and write during the bootcamp, so I would do pretty well. One more advice will be to take this week and go through all the lectures once again and not give in the temptation to rest or relax. 

    I got my offer after 15 days of the bootcamp but for many it can take few months. At the end I would say 3 months of bootcamp can only prepare you to understand the science behind the scenes and provide capacity to be a more analytical person, but to be a good Data Scientist, I still study everyday, learn about ongoing research and connect with knowlegeable people.

  • Stefan Heinz • Business Analyst • Graduate
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    The 12-week Data Science Bootcamp offered by NYC Data Science Academy is a very intense but also very rewarding experience. It is very demanding and forces everyone to keep up at all times. That being said, I can only recommend taking the program if you want to become a data scientist. Here you'll learn all the skills you'll ever need for working in the field.

    The bootcamp starts with an introduction to Unix and Git, followed by two weeks each of introduction to R and then 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 such as Hive, Pig, and Yarn, database programming (SQL, NoSQL) and various useful jump-start sessions such as Shiny, Knitr, Geo/Maps. Every lessons covers both theory and practice, here in terms of R and Python code using basic and well known datasets. From time to time, industry professionals and alumni drop by for guest lectures in the afternoon.

    For questions concerning assigned homework, project work or just better understanding of the material, great TAs are at hand throughout day to help out every single student to make sure everyone gets the best out of the bootcamp.

    Every cohort is a very heterogeneous group consisting of people of all kinds of backgrounds, from fresh graduates to senior managers and PhDs from all kinds of fields, all interested in learning everything they need to know about data science.

    To make sure everybody will be able to apply their newly-learned skills in the foreseeable future in the real world, they also have sessions on how to get the job you want, covering all aspects from resume writing to actually meeting people looking for candidates at one of their networking events. Hiring assistance continues even after the bootcamp is finished.

  • A Great Investment
    - 5/19/2017
    Xinyuan Wu • Data Scientist • Graduate
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    The 3-month study at NYC Data Science Academy was a wonderful experience. For me, I polished my R and Python skills, did 5 projects including web scraping and machine learning, and more importantly, developed strong connections with many great people. 3 month after graduation I was hired as a data scientist by a data research company in NYC. 

    The curriculum is well organized. We learned R and Python programming, statistical analysis and machine learning. During the last few weeks, big data tools like Hadoop and Spark was also covered. As a student coming from an academic background, I feel the classes taught by NYCDSA are pretty good. The knowledge covered in this class are broad and the difficulty feels just right. You can't expect to learn everything in 3 months, but the courses give you the ability to further explore any data science topic on your own. Both instructors and students are awesome in my cohort and I really learned a lot from them. 
     
    The job assistance is good. In my opinion, the bootcamp have tried their best to help the students. There are network events, resume editing, and mock interview to help you prepare the job hunting. Vivian and Chris also arranged a few on-campus interviews, which were great chances for practice. It should be noted that the career transition into data science is not easy, and a 3-month study can't change your life. Only high-quality work, nonstop studying, and active networking can make this happen. 
  • A S • Graduate
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    Bottom line: the bootcamp is a swindle, cheat, and ripoff. Save yourself the frustration.

    If I could turn back time, I would not have wasted $16,000+ on tuition and moving to NYC. My money would have been better spent at an accredited program. The whole operation seems glued together like a popsicle stick diorama...it was like a total free-for-all. No rules, structure, or guidance. The few rules in place were hardly ever enforced (homework deadlines? grading? feedback? none). We were left to fend for ourselves with barely any help. I learned more from other students who had previous experience than any of the instructors, most of whom listed on the website had nothing to do with the bootcamp at all. I agree with some of the other posters...Vivian will accept anyone to make a buck. Some people in my cohort had backgrounds so far from data science I wonder how they got accepted in the first place (fashion? what? ok...).

    Response From: Claire Tu of NYC Data Science Academy
    Title: Marketing Manager
    Thursday, May 18 2017
    We’re sorry to hear that you didn’t have a productive bootcamp. The vast majority of the students who attended our bootcamps find them well organized, challenging and beneficial.

    We pride ourselves on our well-designed structurerigorous curriculum, and responsiveness to student’s feedback. Our program structure and curriculum were approved and regulated by BPSS (Bureau of Proprietary School Supervision) and NYCDSA is a licensed school. We are required by law to deliver the program based on our approved curriculum and to give students grades and clear instructions of the standards for passing. All of the course structure, schedules, and graduate requirement are shared with students from Day 1. Our daily schedule begins with a three-hour morning lecture, followed by afternoon student lab sessions, jumpstart sessions, project presentations, or guest speaker lectures. Each student has access to a team of 6-7 instructors and TAs for personalized instructional, project guidance, and feedback. 

    We are always open to alumni feedback, as our alumni are all proudly displayed on our alumni page with their available contact information. We have been constantly receiving positive reviews and feedback regarding the program operation and structure from our students. As quoting to one daily feedback from a student in the current cohort: “I'm very happy with how things have been going, am really appreciative of the time taken to put together a solid curriculum (excellent project topic selection so far) and especially to have customized the curriculum based on student feedback.” 

    In terms of acceptance standards: to ensure that students are fully prepared for the bootcamp, all students are required to complete prerequisite coursework, covering programming, data visualization, and over two hundred coding challenge questions from both R and Python. Additional recommended readings and lectures on calculus, linear algebra, and statistics are also provided. Students who didn’t meet the requirement or aren't a good fit will be asked to defer their enrollment. 

    We believe that education is about creating new opportunities, and it should not solely depend on one’s past education background. We trust in people's potential and have developed an intensive pre-work program to get non-technical people ready for the intensive bootcamp

    The fashion background student that mentioned in the review got a Data Scientist position at a major ad agency within TWO months of graduation.

    That being said, by the end of most recent graduated cohort, among 200 enrolled students, 77% of them hold a degree in STEM majors. Among the rest of 23% NON-STEM background students, 40% of them come from Economics major, 25% from Business and Finance, and 11% from a Marketing background. For the highest degrees enrolled student obtained, 28% holds a PhD’s, 38% Master’s, 5.2% MBA, and 28% Bachelor’s. Each cohort we have 32-36 students. The ratio of students to Instructor/TAs is 6:1.  

    We welcome every student to raise a concern on our daily feedback survey and weekly pulse check, and talk to our school officers and instructors. If you are a prospective student who wants to gain more insights about our program, we welcome you to read those reviews with a VALIDATED real name on Course Report or SwitchUp. Questions and inquiries about program offerings can be directed to admissions@nycdatascience.com.
  • Don't do it
    - 4/26/2017
    Ibadlyneed Tovent • Graduate
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    Someone said this earlier. Vivian accepts anyone and everyone. This really hurt the bootcamp because we spent the first month learning how to code! That's insane!! This bootcamp was more of an introduction to coding than anything. There was a bootcamp employee who wasn't accepting unqualified people, but she either quit or was fired.

    Vivian is all talk. She barely remembers most of the things she says, and the things she says are conflicting. In the first week, she promised everyone would be "senior data scientist" and shouldn't accept anything less. At the end, she said the opposite. I'm a month into job searching after the bootcamp, and I haven't been able to get interviews. I'll have to go back to what I was doing before the bootcamp, same as other people. Even most people from the last few cohorts are still looking for jobs.

    Please don't join this bootcamp. It was a waste of $16000. I can't believe I paid that. I'm furious. I feel cheated. Give me my money back...going to have to report Vivian and this bootcamp.

  • Bootcamp Journery
    - 4/22/2017
    Jhonasttan Regalado • VP Production Support Manager • Graduate
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    I started working in the financial industry in 1998 and have had roles in IT spanning development and production support. I attended the NYC Data Science Academy bootcamp during a three month sabbatical from work and it was a worthwhile investment. I have gone back to work to a new and challenging role that allows me to apply my new found skills (such as data exploration, visualization, and analysis for decision making) as a production support manager for an ultra low latency and algorithmic trading platform at a top financial institution.

     

    What did it take for me to achieve success at the bootcamp?

     

    My three months at the academy was intense. I had a strong support system at home and at the school. My instructors and TAs were smart, caring and invested in my development every step of the way. Delivering on five different projects that stretch your knowledge of Data Science and Machine Learning fundamentals, Python and R programming, through daily classroom and homework practice was exhausting yet rewarding because you were not alone through the journey. As an early riser, the academy facilities were available to me starting at 7AM daily.

     

    My advice for a strong finish.


    I strongly advise that you complete the prep work provided by the academy by the time you start the bootcamp. The amount of work expected to be completed during the three-month journey is not an easy feat; however, the projects you are exposed to, the knowledge you gain and the practical experience you collect through individual and team projects is indispensable and can be quickly applied upon your return to work. Going into the bootcamp I felt uncomfortable thinking of myself as a potential Data Scientist. Leaving the bootcamp I am comfortable with the fundamentals of Data Science and the application of hypothesis testing to data problems. I am not a Data Science unicorn, hence, I rely on my new found strengths and maximize the talents within my team to investigate and find solutions to technical problems.

  • Lukasz • Graduate
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    I studied mechanical engineering and physics for my undergrad at a top university and work in product management with a focus on search. I took this class to satisfy a personal interest in the subject matter and familiarize myself enough with the fundamentals of machine learning to be able to explore the field more deeply on my own. I was also motivated by a career interest: the subject matter is highly relevant to my domain, and I feel that developing an understanding of the concepts and how to deploy them myself will make me better at my job long-term. Prior to enrolling in the class, I spent roughly 8-10 hours learning R and felt sufficiently prepared (I had some previous programming experience).
     
    In the end I was extremely happy with this class (Machine Learning in R on Saturdays, 8 hrs at a time). The curriculum and content were excellent, the instructor, Luke, was fantastic and the assignments were challenging and informative. 
     
    I felt the course did a really great job of driving home the core fundamentals of each subject with a focus on statistics, mathematical theory, derivations and best practices. We covered a LOT of material, yet the material had a lot of depth. I thought the sequencing of the subject matter was very well thought out as well. The class was demanding and had the caliber of a graduate-level course.
     
    The course also struck a very nice balance between theory and implementation. After learning about a new model, we would immediately implement it in class using R on our own machines. Luke did a particularly great job at relating the implementation back to the concepts and teaching us how to interpret outcomes of our analyses (I can’t stress enough how important this latter point was for me). He has a really strong grasp of the subject matter, he’s very patient and responsive to questions, offers a lot of insightful commentary on the theory, implementations and best practices, and he cares about his students a lot. The homework assignments complement the class nicely as well, helping to drive home the methods taught in class and how to interpret your work.
     
    If you’re interested in developing a strong understanding of the fundamentals of machine learning in a rigorous format, this class is for you. I also couldn’t recommend Luke as an instructor more. He’s awesome! I was also was very pleased with my choice of the R class. R reduces a lot of the friction in model implementation, which allowed me to focus on developing an understanding of the concepts and interpreting results. 

Thanks!