nyc-data-science-academy-logo

NYC Data Science Academy

New York City, Online

NYC Data Science Academy

Avg Rating:4.83 ( 263 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.

Recent NYC Data Science Academy Reviews: Rating 4.83

all (263) reviews for NYC Data Science Academy →

Recent NYC Data Science Academy News

Read all (21) articles about NYC Data Science Academy →
  • 12-Week Immersive Data Science Bootcamp (In-person)

    Apply
    Start Date
    Rolling Start Date
    Cost
    $17,600
    Class size
    50
    Location
    New 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 monthsFull Tuition Total $17,600Skills Fund Student Loan$397.88 pm for 60 months
    Tuition Plans
    We have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / Guarantee
    NYC 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.
    Scholarship
    Limited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill Level
    Ideal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Work
    http://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement Test
    No
    Interview
    Yes
  • Big Data with Amazon Cloud, Hadoop/Spark and Docker

    Apply
    Data Science, Python, Hadoop, Spark, Data Structures
    In PersonPart Time5 Weeks
    Start Date
    September 10, 2019
    Cost
    $2,840
    Class size
    N/A
    Location
    New 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
    Deposit
    N/A
    Getting in
    Minimum Skill Level
    Students are expected to be familiar with using an operating system from the command line; knowledge of Python is helpful.
    Placement Test
    No
    Interview
    No
    More Start Dates
    September 10, 2019 - New York CityApply by September 6, 2019
  • Data Science with Python: Data Analysis and Visualization (Weekend Course)

    Apply
    Start Date
    September 8, 2019
    Cost
    $1,590
    Class size
    20
    Location
    New 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
    Deposit
    N/A
    Refund / Guarantee
    NYC 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 Level
    Knowledge of basic data types (e.g. string, numeric), data structures (e.g. list, tuple, dictionary) Familiarity with concepts of list comprehension and for/while loop
    Placement Test
    No
    Interview
    No
    More Start Dates
    September 8, 2019 - New York CityApply by September 6, 2019
    October 27, 2019 - New York CityApply by October 25, 2019
  • Data Science with Python: Machine Learning (Weekend Course)

    Apply
    Data Science, R, Machine Learning, Artificial Intelligence
    In PersonPart Time7 Hours/week5 Weeks
    Start Date
    September 8, 2019
    Cost
    $1,990
    Class size
    10
    Location
    New 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
    Deposit
    N/A
    Getting in
    Minimum Skill Level
    Completion of Data Science with Python: Data Analysis; Data Science with R: Machine Learning
    Placement Test
    No
    Interview
    No
    More Start Dates
    September 8, 2019 - New York CityApply by September 6, 2019
    October 27, 2019 - New York CityApply by October 25, 2019
  • Data Science with R: Data Analysis and Visualization (Weekend Course)

    Apply
    Data Science, R, Data Structures, Data Visualization, Data Analytics
    In PersonPart Time7 Hours/week6 Weeks
    Start Date
    September 7, 2019
    Cost
    $2,190
    Class size
    15
    Location
    New 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
    Deposit
    N/A
    Getting in
    Minimum Skill Level
    Basic knowledge about computer components Basic knowledge about programming
    Prep Work
    None
    Placement Test
    No
    Interview
    No
    More Start Dates
    September 7, 2019 - New York CityApply by September 5, 2019
    October 26, 2019 - New York CityApply by October 24, 2019
  • Data Science with R: Machine Learning (Weekend Course)

    Apply
    Data Science, R, Machine Learning
    In PersonPart Time7 Hours/week6 Weeks
    Start Date
    October 26, 2019
    Cost
    $2,990
    Class size
    40
    Location
    New 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
    Deposit
    N/A
    Getting in
    Minimum Skill Level
    Knowledge of Python programming Able to munge, analyze, and visualize data in Python
    Prep Work
    Knowledge of R programming Able to munge, analyze, and visualize data in R
    Placement Test
    No
    Interview
    No
    More Start Dates
    October 26, 2019 - New York CityApply by October 24, 2019
  • Data Science with Tableau (Online)

    Apply
    Data Science, Data Visualization, Business Intelligence
    In PersonPart Time5 Hours/week3 Weeks
    Start Date
    None scheduled
    Cost
    $1,590
    Class size
    20
    Location
    Online
    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
    Deposit
    1590
    Refund / Guarantee
    NYC 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 Level
    Know how to use Mac, Windows. Familiarity with relational databases is preferred but not required. gain a greater appreciation for the logic underlying Tableau’s features utilize their capstone project to visualize ‘big data’
    Prep Work
    Before the course begins, pre-work will be available for students interested in strengthening their ability to access and extract data from relational databases (e.g. SQL-based servers).
    Placement Test
    No
    Interview
    No
  • Deep Learning with Tensorflow (Weekends and In-Person Only)

    Apply
    Start Date
    October 19, 2019
    Cost
    $2,990
    Class size
    15
    Location
    New 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
    Deposit
    N/A
    Getting in
    Minimum Skill Level
    Object-oriented programming, ideally Python (introductory course: https://nycdatascience.com/courses/introductory-python/) Simple shell commands, e.g., in Bash (tutorial of the fundamentals: https://learnpythonthehardway.org/book/appendixa.html)
    Placement Test
    No
    Interview
    No
    More Start Dates
    October 19, 2019 - New York CityApply by October 15, 2019
  • Introduction to Python (Online)

    Apply
    Data Science, Python, SQL, Data Structures, Data Visualization
    OnlinePart Time5 Hours/week2 Weeks
    Start Date
    None scheduled
    Cost
    $795
    Class size
    25
    Location
    Online
    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
    Deposit
    795
    Refund / Guarantee
    NYC 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 Level
    Comfort with Windows, Mac or Linux environment and ability to install third-party software.
    Prep Work
    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.
    Placement Test
    No
    Interview
    No
  • Introductory Python (Evenings)

    Apply
    Start Date
    October 21, 2019
    Cost
    $1,590
    Class size
    40
    Location
    New 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 / Guarantee
    NYC 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 Level
    This Introductory Python class is designed for computer-literate people with no programming background who wish to learn basic Python programming.
    Prep Work
    In 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 Test
    No
    Interview
    No
    More Start Dates
    October 21, 2019 - New York CityApply by October 18, 2019
  • Remote Immersive Data Science Bootcamp (Online)

    Apply
    Start Date
    Rolling Start Date
    Cost
    $17,600
    Class size
    25
    Location
    Online
    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
    Deposit
    5000
    Financing
    Climb Credit Loan $400* pm for 60 monthsFull Tuition Total $17,600Skills Fund Student Loan$397.88 pm for 60 months
    Tuition Plans
    We have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / Guarantee
    NYC 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.
    Scholarship
    Limited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill Level
    Ideal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Work
    http://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement Test
    No
    Interview
    Yes
  • Remote Self-paced Data Science Bootcamp (Online)

    Apply
    Start Date
    Rolling Start Date
    Cost
    $17,600
    Class size
    25
    Location
    Online
    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
    Deposit
    17600
    Financing
    Climb Credit Loan $400* pm for 60 monthsFull Tuition Total $17,600Skills Fund Student Loan$397.88 pm for 60 months
    Tuition Plans
    We have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / Guarantee
    NYC 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.
    Scholarship
    Limited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill Level
    Ideal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Work
    http://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement Test
    No
    Interview
    Yes

Review Guidelines

  • Only Applicants, Students, and Graduates are permitted to leave reviews on Course Report.
  • Post clear, valuable, and honest information that will be useful and informative to future coding bootcampers. Think about what your bootcamp excelled at and what might have been better.
  • Be nice to others; don't attack others.
  • Use good grammar and check your spelling.
  • Don't post reviews on behalf of other students or impersonate any person, or falsely state or otherwise misrepresent your affiliation with a person or entity.
  • Don't spam or post fake reviews intended to boost or lower ratings.
  • Don't post or link to content that is sexually explicit.
  • Don't post or link to content that is abusive or hateful or threatens or harasses others.
  • Please do not submit duplicate or multiple reviews. These will be deleted. Email moderators to revise a review or click the link in the email you receive when submitting a review.
  • Please note that we reserve the right to review and remove commentary that violates our policies.
You must log in to submit a review.

Click here to log in or sign up and continue.

Hey there! As of 11/1/16 is now Hack Reactor. If you graduated from prior to October 2016, Please leave your review for . Otherwise, please leave your review for Hack Reactor.

Title
Description
Rating
Overall Experience:
Curriculum:
Instructors:
Job Assistance:
School Details
About You

Non-anonymous, verified reviews are always more valuable (and trustworthy) to future bootcampers. Anonymous reviews will be shown to readers last.

You must log in to submit a review.

Click here to log in or sign up and continue.

Shared Review

  • Sunanda Mishra
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
      I went to NYCDSA in hopes of changing careers. My undergraduate degree was in statistics, and I worked as an actuary for 6 years and earned my actuarial credentials in this time. I was bored of actuarial work and was looking for a change into data science since my background was related. While I had the statistics knowledge, I needed to beef up my coding experience.   After reading tons and tons of reviews of bootcamps, I narrowed it down to NYCDSA, Metis and Galvanize. NYCDSA had the most comprehensive syllabus and it looked like students found jobs relatively quickly after the bootcamp ended.   NYCDSA lived up to my expectations with respect to how comprehensive the program was. The projects really helped me solidify my knowledge in Machine Learning techniques and my practice with coding. I will say, as an actuary and statistics major, my knowledge of statistics was already pretty decent. NYCDSA went through a semester's worth of course material in 1-2 days at times- this may be too fast to get a thorough understanding, however it's up to the student to study more outside of the bootcamp if needed.   After the bootcamp, NYCDSA helped a ton with finding a job. After the hiring partner event, I had 5 interviews lined up with well established companies. Further, Vivian has connections with TONS of companies in NYC and her connections helped a lot with securing interviews beyond those at the hiring partner event. Vivian would reach out to us regularly to see how our job hunt is progressing and even offerred to have 1x1 sessions to make sure there was a constant pipeline of interviews.   After 2 months of interviewing, I received 2 job offers. Both offers were from insurance companies that valued my actuarial background and my newly acquired data science skills. I can definitely say, I wouldn't have been able to land these jobs without being able to speak to the projects I did at NYCDSA during those interviews.
  • Anonymous • Data Scientist • Student
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I would recommend this program to people who are interested in starting or enhancing a career in or related to data science. The program covers a wide range of topics and they constantly add new materials so you can learn the tools that have high industry demand. I just started working as a data scientist and engineers at my company are learning these new tools as well. 

    I highly recommend the chief instructor, Chris, at this bootcamp. He is a talented teacher. Those who have prior experience taking a machine learning or a statistics class would understand that it is not easy to have a good instructor. I took statistics and machine learning classes at university, but Chris surprised me with better ways of understanding statistical concepts and advanced algorithms.

    Everyone enters this bootcamp with high expectation in education and career assistance, but you are the only one who can choose to get the best out of it. The TAs and instructors are very helpful when you have questions and they provide guidance to the right tools and methods. I suggest anyone who comes to this bootcamp to be prepared to work hard and learn a lot of new things in a short period of time. 

    A final suggestion to those who are interested in becoming a data scientist. The most important thing that I learned from this bootcamp is modesty. Modesty is critical because as a data scientist, you never want to assume results. Also, data science is developing field and requires continuous learning even after the program. I would recommend this bootcamp to anyone who is ready to join the exciting world of data science.

  • Anonymous
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    Great course! The slides were clear and the content was very useful. Plenty of opportunities to practice and work in groups. Derek was a great instructor, allowing plenty of time of questions and making the course very interactive. He was also always available to answer questions in between classes and help us with work related projects as well. I have learned a lot and would definitely recommend this course.

  • Student
    - 7/24/2016
    Anonymous • Student
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    This course provided all the fundamentals and resouces we need to learn data analysis and visualization. The instructor was approachable and helpful when special assistence was needed inside and outside of classroom. Even though the five weeks course was intense but I'm a pleased to receive after class assistance and was encouraged to learn continuously. I hope to receive job assistance and look forward to seeing support in this area for all students and alumnis.

  • Anonymous
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    I got a lot out of this course but the curriculum is very challenging. Calling this a beginner level course is overly optomistic. It's basically just a list of code examples.I have years of experience teaching technical material in statistics and research methods and have learned that it's generally not helpful to just dump a bunch of information on students without explaining the relevance of the information through practical and intuitive examples. There is too much emphasis on basic comp sci and not enough explanation of why understanding these principles is even relevant. Why do I need to write an algorithm to test if a matrix is a magic square or calculate roots to analyze data in python? You really don't. Teach the essentials coding techniques needed to analyze and visualize data first and focus on only the most critical material. Save the computer science for a computer science class. 

  • Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I was a member of the January-April 2016 cohort, and I have fond memories of the experience, difficult and stressful as it was. The instructors and TAs are overqualified and brilliant, and Christopher Makris gives the broadest and deepest lectures that time affords. Zeyu is a magician. I would call it comparable to a master's program in machine learning, if the student puts the necessary additional time for self learning and independent study. The bootcamp was one of the most informative, rich and interesting experiences of my life, but it comes with several caveats. For one, this isn't grade school, so you are expected to learn from trial and error on your own and be comfortable with mastering theory as well as execution. The staff are there as a complementary resource, and shouldn't be relied upon 24/7 as a crutch for lack of ability to work independently. In other words, you get out of the bootcamp exactly what effort you put in, and you should be able to figure out the gaps on your own.

    This brings me to my next point, probably the only complaint I have with the bootcamp--the lack of selectiveness in admissions. Management is responsible for choosing a group of students that befits the brand exclusivity, and in my view admissions is not selective enough. This may hurt the camp in the long term. Several people came from non-technical disciplines and were very much able to learn quickly, but there were a couple who saw the instructors are their own personal tutors and significantly slowed the lecture process for others. If you have to ask a question every 10 minutes that interrupts the class schedule, or spend hours with TAs only to forget everything and have to repeat repeat the personal tutor process again, you should not apply here. It's not fair to other students. You will monopolize instructors' valuable time. There is no magic fix for becoming a data scientist, and after school age you should be able to learn on your own.  In the age of the internet, there is no excuse for not being able to use Google. What I noticed from our cohort was the less someone knows, the more they talk. This is more a problem of the admissions officers/CEO than of the students who do not fit in; they should foresee these kinds of problems in the interview process and make sure that whoever gets in is technically competent. People who see this as a quick entry to becoming a data scientist should also be aware that not everyone who learns to program will be a good data scientist, and you won't simply be offered a job afterwards. What is instrumental in your career post-bootcamp are your original skills and experience. It is not a way to expedite the job search if you have recently become unemployed. 

    The interview process post-bootcamp is also autonomous, and you shouldn't expect to be given many interviews automatically unless you manage to find contacts on your own. Your projects are your own personal portfolio, and being self reliant on your ability will serve you better in the long run.

    To summarize, the main lecturer is brilliant, an amazing teacher, who covers as much as possible in the limited time. You will learn more than you ever expected. The TAs are a major resource, but the main weakness is the admissions process. And lastly, if you can't learn things on your own, don't sour the bootcamp for others. There are many online courses, such as Coursera, which will be better for you. 

     

  • Anonymous • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I have mixed feelings. The students are really the best part about the program. They have such an eclectic background that I learned a lot from them. The classes themselves are at best, overpriced. The materials and intruction is roughy on par with what you'd find in Coursera courses. I don't regret the program and I did learn a lot, but I question the value for the cost. That said, it probably depends on your needs. If you need time where you solely devote yourself with like-minded individuals, it might be worth it. If you place emphasis on the curriculum and instruction, there is a slew of resources out there that are at least as decently taught (if not better), but at a much better value. Also, the curriculum states there are advisors, but although they may be advisors to the program, you'll only see one or two of them for a short 30 minute talk at the end of the program.

  • Anonymous • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    This course reallly gave me the foundation for understand R and applying it to real world data. Studying programming syntax can be challenging and Vivian really encouraged me to complete the final presentation. I am so glad I did!! I now have the confidence to work in R and use in the workplace. Thank you NYC Data Science Academy!! See you again soon.

  • Anonymous
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    This is a very conprehensive course covering R data analysis and visualization techniques. I can't remeber everything I learned in this class, but whenever I encountered data project in my work and study, I always went back to check the slides, examples and libraries provided by this course. 

    Vivian is a very passionate instructor and an excellent data scientists. She offers many hands-on example in class, not just reading and showing slides. She is very serious about the course and push students in a "hard" way. She encouraged all of us to try, I can't get lazy because of her.

    In general, this is a very postive experience. Just squeeze some time in the weekend, you will be amazed how much you can learn within 1-2 months.

     

  • Anonymous • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    I have posted a review also in a different website - but also wanted to share my opinions on this website for others to consider.

    I took the Beginner R course, which was not that great reflecting back on it. In whatever class you take, the teachers make most of the difference, and in my case, we were not taught well. In the end, I cannot even remember if I have absorbed anything at all except for doing basic statistics in R. We were told that Vivian, the founder, would visit, but she never visited. While doing self-study on free R online courses such as EdX, I discovered that the materials we received in this course was similar to what I found in these free online courses about R.

    In the end, we presented our projects, but all of our projects were half-baked, because we did not absorb enough knowledge about R to prepare and present. Although my project was really using the bare basics of R, such as basic statistics (e.g. average, max, min), Vivian said that I did a great job and that I should sign up for the R Intermediate course. She kept on pressuring and asking me when I can join, and I later realized that this academy was really focused on making money off the data science buzz rather than really teaching students about how to master data science.

  • Anonymous • Student
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    Learning to code requires a lot of work - this class gave me the push to invest the time necessary in a structured manner. I found the lecture notes and homework problem sets very useful. Compared to content found on MOOCs and general online searches, the class curriculum is a lot more practical and concrete. 

    Our instructor Liz is very helpful and accommodating. The classroom environment was open to questions/discussion, and Liz was very willing to take the extra step to help me improve my final project.  

    PROS:

    - Very strong curriculum coverage - maximizes the hours that we had in lecture quite effectively. I was able to do a lot more at the end of class than I originally anticipated, and I now have a base of knowledge to apply it to my daily work

    - Great schedule - it's hard to find a weekend class for those who work longer hours on the weekdays 

    SUGGESTIONS FOR THE FUTURE:

    - More problem sets / heavier homework. I learned a lot through the homework and in-class problem sets. Having more problems / worked solutions, even if optional, could really help reinforce concepts faster    

    - I do think people came in with different amounts of knowledge/background experience. Maybe one thing to help equalize the starting field is to assign some 'pre-work' / 'pre-reading' ahead of the class so that a baseline can be established and more time can be spent on the analysis/visualization portion 

  • Great Experience!!
    - 10/23/2018
    Felipe Santos • Data Scientist • Student
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    The staff are friendly, and well prepared. This bootcamp helped me to consolidate and organize my knowledge.  I feel that they gave me exactly what I was looking for, before the bootcamp I was not so sure about my models, now I feel great doing what I do. This is priceless. 

    If you have any doubts about those guys, please don't. They are super honest people, for sure this is a trust worthy organisation.

    Thank you guys, if any of you from NYDSA read this.

    Felipe.

  • Chuanhao Nie • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I attend the bootcamp because one of my friends' recommendation that the bootcamp is a great place to get hands on experience of data science. Actually this bootcamp is as good as I expected. The bootcamp offer everything related to data science, not only the advanced part such as machine learning and deep learning, but also fundamental skills like Pyhton, R and SQL. After the bootcamp, I feel like I am prepared to apply for data science job. 

    I will never forget the 3 month study experience which is owesome. The teachers and assistant here are very knowledgeble. The classmates are also great who have very strong acdemic/working experience. People here are PHDs or masters with around 2-10 years' working experience. I would highly recommend NYCDSA. 

Thanks!