nyc-data-science-academy-logo

NYC Data Science Academy

New York City, Online

NYC Data Science Academy

Avg Rating:4.83 ( 266 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 (266) reviews for NYC Data Science Academy →

Recent NYC Data Science Academy News

Read all (20) 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 Hours/week2 Weeks
    Start Date
    January 14, 2020
    Cost
    $2,990
    Class size
    10
    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
    January 14, 2020 - New York CityApply by January 10, 2020
    April 21, 2020 - New York CityApply by April 15, 2020
  • Data Science with Python: Data Analysis and Visualization (Weekend Course)

    Apply
    Start Date
    October 27, 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
    October 27, 2019 - New York CityApply by October 25, 2019
    January 19, 2020 - New York CityApply by January 15, 2020
    March 7, 2020 - New York CityApply by March 1, 2020
  • Data Science with Python: Machine Learning (Weekend Course)

    Apply
    Data Science, R, Machine Learning, Artificial Intelligence
    In PersonPart Time7 Hours/week5 Weeks
    Start Date
    October 27, 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
    October 27, 2019 - New York CityApply by October 25, 2019
    January 19, 2020 - New York CityApply by January 15, 2020
    March 7, 2020 - New York CityApply by March 1, 2020
  • Data Science with R: Data Analysis and Visualization (Weekend Course)

    Apply
    Data Science, R, Data Visualization, Data Analytics , Data Structures
    In PersonPart Time7 Hours/week6 Weeks
    Start Date
    October 26, 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
    October 26, 2019 - New York CityApply by October 24, 2019
    January 18, 2020 - New York CityApply by January 15, 2020
    April 18, 2020 - New York CityApply 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
    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
    January 18, 2020 - New York CityApply by January 15, 2020
    April 18, 2020 - New York CityApply by April 15, 2020
  • Data Science with Tableau (Online)

    Apply
    Data Science, Data Visualization, Business Intelligence
    In PersonPart Time5 Hours/week3 Weeks
    Start Date
    Rolling Start Date
    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
    None scheduled
    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
  • Introduction to Python (Online)

    Apply
    Data Science, Python, Data Visualization, SQL, Data Structures
    OnlinePart Time5 Hours/week2 Weeks
    Start Date
    Rolling Start Date
    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
    January 14, 2020
    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
    January 14, 2020 - New York CityApply by January 10, 2020
    March 9, 2020 - New York CityApply by March 5, 2020
  • 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

  • Melanie • Data Science Modeler • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    Best decision I have ever made for my career. If you also have realized that the way we collect, analyze, understand, and utilize data will determine the future and potentially your career path too, this boot camp is what you need to do. I came in expecting a strict syllabus and skilled instructors but what I got was so much more. 

    My experience with the NYC Academy for Data Science was fascinating, exhausting, humbling, and rewarding. I learned more than I could have imagined and I made new life-long friends. Your fellows will be a colorful mix from across degrees, disciplines, and nationalities. That also means that you will hear and learn from different perspectives, which will expand your horizon significantly. All instructors and TAs (highly skilled, smart, funny, friendly, and fantastic) are there to support and help you with whatever problem you have and the syllabus is business-oriented to prepare you the best way possible for your future endeavors. After 12 weeks, you will know how to code, how to tackle data science projects, and how to communicate your findings effectively. You will also know how to manage Big Data or how to build complex Deep Learning algorithms.

    It won't be easy though and although they give you all the resources you need, it will be up to you how much you get out of it. So expect all-nighters, some frustrating and seemingly hopeless moments combined with desperate runs to the coffee machine. BUT IT IS WORTH IT. You can do anything for 3 months and if you put all you have into it, you won't regret a single minute. 

    I would do it again. Like I said, best decision I have ever made for my career. 

     

  • Good and Difficult
    - 6/13/2019
    Adrian Gillerman • Global Data Analyst • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

           The bootcamp offers even seasoned machine learning practitioners something new, and everyone (regardless of their coding background) can get something out of it.  The instructors are nice and want you to succeed but there is so much information packed into such a short amount of time. I liked this bootcamp but found it difficult.

           On a more personal note, I would not have gotten the job that I have now at Bloomberg without this bootcamp. I received letters of recommendation from two instructors as well as a practice interview with one of the camp’s alumni who had recently gone through the same interview process. The interviewer mentioned that the bootcamp made me stick out as an applicant and was part of the reason why I got this position. As I reflect on this experience though, I felt like I was floundering the entire time and overall the experience was not enjoyable because of the magnitude of work. However, I ended up learning so much about R and the different machine learning techniques. Even though I came from a mathematical background with some machine learning experience I was not able to comprehend some material during the program.

            Some of you reading this review may be thinking in a strictly cost-benefit mindset. For me, the financial benefit has outweighed the cost especially because I am at the beginning of my career. Because of the quality of the instructors, the opportunities the bootcamp unlocks, and the interesting subject material I would recommend this bootcamp to anyone looking for a challenging way to jumpstart their machine learning adventure.

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

    I took the Python course about Data Analysis and Visualization. Tony taught everything in a very clear and organized manner. Highly recommend the course to anyone who are interested to learn more about data analytics and pursue the data scientist career.

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

    I attended the Introductory Python course at NYC Data Science Academy taught by Tony Shultz. I really appreciate his style of teaching - it was a great balance of theory and practice. He also brought a lot of energy to the class which made a big difference especially as it was in the evenings after work. He also takes the time to go through the HWs and clarify students' questions - he is clearly very engaged in teaching and makes a lot of effort to support his students. 
    I highly recommend this class.

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

    Jon Krohn gave a wide overview of basic deep learning models - convolutional, recurrent, some unsupervised learning approaches, etc. This is obviously a lot of material and there are only 5 sessions, so discussion was typically sacrificing depth for breadth.

    As a starting point to understand DL, e.g. for someone with traditional stat/quant background, this is a great course. Jon explains the basic mechanics of a neural network down to matrix transformations. 

    On the other hand, if you already understand the basic DL and are looking to develop practical application skills including TensorFlow, this course may be a slight disappointment. 

  • Great Experience
    - 5/20/2019
    HF • Student
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    The course “Data Science with Python: Data Analysis and Visualization” is perfect if you are fairly new to “Data Science”. It begins with a general overview of basic knowledge in python before going to the analysis content. You will learn Numpy, Pandas, Visualization and Seaborn after you finish the course. The instructor Anthony Schultz introduced every chapter in a slow and comprehensive pace. The course notes are well organized. You can ask any questions during the class and Anthony will explain your question with great amount of details. I would love to recommend to take this class if you are planning to pursuing your career in Data Science. Five Stars!!

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

    The teacher was able to properly explain all the concepts. Very easy to follow and understand. Homework looks challenging but if you listen in class, it's doable. Would definitely recommend. 

  • Marius P. • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    You can read books about deep learning and in fact the instructor Jon Krohn has a new book out as well. However, there is a gap between reading and understanding conceptually and writing code to solve a real-world problem. This course completely fills the gap.

     

    The instructor does give you the conceptual foundations, assuming no prior knowledge. I did have some prior knowledge, but still the class is self-contained.

    I would say 30-40% of class time is spent discussing code that solves a practical problem. I think this is the perfect balance: you can't delve more into code without a global understanding why all those parameters may be required and you can't delve deeper into theory without neglecting the practical question of "where do I begin".

     

    The instructor is very personable and easy to approach about his own experience in the industry.

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

    I came across NYC Data Science Academy when I was looking for certification courses with classroom learning and I am glad I made a decision to take it up. It was a well planned course which was spread over five Sundays. Our instructor, Anthony Schultz, was extremely knowledgeable and passionate about teaching Python which made the whole course a very enjoyable experience. The course content had in depth coverage with examples to elaborate each concept, giving us the opportunity to learn as we progress. By the end of this course, you would get a good grip of Python which you can apply in your daily work.

  • 74066366EB14503E0F379266E1EFA24EFB3C29F54BD0751E50E8025293CD82A8
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    I took Data Science with Python: Data Analysis and Visualization with Tony Schultz. The course was a wide-ranging overview of basic Python, Numpy, Scipy, Pandas, and Matplotlib (and a little Seaborn) that managed to fit into 5 sessions. The materials were organized and gave nice opportunities to practice. Tony was extremely helpful and made all questions welcome.

  • Naveen Ramachandran
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    The class was concise and impactful. I really enjoyed the topics covered and thought that the homework was really beneficial in driving home the coursework. The pace of the course was pretty reasonable and I was able to follow the curiculum very well.

  • Great course
    - 4/14/2019
    Erinc Eyuboglu
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    I really enjoyed the contents and the samples that Anthony gave us. If you have some background about coding and maths this course is for you to start to build up your skills with phython.

  • Lukas Frei • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I chose the NYC Data Science Academy over other bootcamps mainly because of its outstanding reviews as well as the fact that it offers its courses simultaneously in R and in Python. 
    After working through the extensive prework in Python, R, and SQL (an absolute must if you do not have any previous coding experience), I was perfectly prepared to attend the bootcamp.

    The bootcamp itself offered far more than I had expected. Phenomenal teachers that will give their everything to help you understand the topics, TA's that are always there to help with smaller questions as well as great classmates that made studying a lot easier. Nevertheless, you should not come here expecting a relaxed ride. Teaching data science in twelve weeks requires a fast pace and it will be up to you to put in those hours to complete the homework and review lectures.

    The curriculum itself is centered around four projects that deal with all aspects of the data science process, from gathering data (web scraping) over creating a dashboard to working on machine learning group projects. My initial intention coming here was to learn as much as possible as fast as possible and this bootcamp turned out to be everything that I had hoped for.

    On top of that, Vivian and the team offered several job search and interview preparation lectures and talks that I have not seen anywhere else. If you are looking for a data science bootcamp that covers all data science topics from start to finish and you are willing to put in the work, this bootcamp is the best decision you can make.

  • Great teaching!
    - 2/3/2019
    Ilya Fischhoff • Postdoc • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    I took NYC Data Science Academy's deep learning course, taught by Jon Krohn. Jon is a terrific teacher, and I would heartily recommend this course! Jon had tremendous enthusiasm and patience for questions while still keeping us on track with the schedule. He really broke things down into bite-size, understandable pieces, while still covering a lot of breadth and depth. I appreciated Jon making available his draft book, this really complemented the lectures. I liked the format of once-a-week lessons because it gave time for concepts to sink in and to practice things with my own data in between sessions. I appreciated that Jon made time to troubleshoot challenges we experienced in our own projects. The course was both a great introduction to concepts and to some of the ways people are applying deep learning; here examples from Jon's day job were valuable. One aspect of the course that was very helpful was that Jon set up a Docker environment for us, and shared very clear instructions for getting our computers set up with it in advance. We were all ready to go from the start. I'm all the more grateful for Jon having set that up after recently spending half of a workshop (run by a different data science academy) wrestling with Anaconda. The Jupyter notebooks all just worked, so we could focus on learning. 

  • Amazing Program!
    - 1/11/2019
    Shiva Panicker • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I entered the bootcamp with a background in Mathematics and some previous exposure to machine learning theory. Programming was relatively new to me at the time, so my first course of action was to familiarize myself with Python and R. Thankfully, NYC Data Science Academy provided over 30hrs of pre-bootcamp material (video lectures, in-person lectures, and textbooks), which included coding in Python, R, and some introductory statistics. The program kicks off with a small overview of the tools needed to begin studying data science (mostly to bring everyone up to the same page), and then dives right in to data visualization as the first major topic of study. There is a strong sense of camaraderie with students throughout the program, and there is a strong family-like atmosphere in the space. Most importantly, the curriculum is very strong, and the instructors have deep practical and theoretical knowledge on the material covered. I found it a privilege to study under some of NYCDSA's staff, as they really are the cream of the crop. On top of a strong curriculum, there are many career workshops during and after the program is complete. Some of the workshops include professional development, discussions with industry leaders and practitioners (Kirk Borne spoke to my cohort), and interview/case study prep. The post-graduation network is incredibly strong and supportive for alumni seeking jobs, with frequent interview and networking opportunities provided. Vivian (the program founder) has a vast professional network, which she utilizes for student and alumni needs.  I'm incredibly happy to have completed the program, and to have worked with such a terrific set of instructors, teaching assistants, and classmates during that time. I couldn't have asked for a better start to my data science career.

  • 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.
  • Katie Sohn • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    NYCDSA is definitely a great program for anyone looking to get into data science. I can honestly say it was one of the most challenging and rewarding things I have done. Despite not coming from a STEM background or having any sort of relevant experience initially, the bootcamp still prepared me to be able to land a data science job after. 

    Prior to deciding to join the bootcamp I was referred by a friend who also did not come from a STEM background that highly recommended the program. I decided to try a part time Python course that was offered by the academy to see if this was something I could continue to pursue while also doing more of my own research. After the class I applied and got into the January cohort but decided to defer my acceptance to the following cohort. I think that if anyone is considering the bootcamp and does not have any sort of applicable experience like myself, I highly recommend giving yourself time to complete as much of the pre-work as possible before diving in. Between Python and R, I would try to get as familiar with Python as possible through the pre-work and even beyond that if possible. The staff emphasized this during the interview process and after being accepted, but this became even more apparent once the bootcamp started. The first few weeks of the cohort were more of a review from the pre-work and the class I had taken as we quickly learned both R and Python. However, because I had completed the course and completed a good amount of pre-work, I was able to focus more on solidifying concepts rather than struggling to understand and keep up with learning the code on the fly. This also helped towards the end when completing more complex projects, but I think that if I had dedicated an even greater amount of time beforehand, this could have helped me to push myself even further during the bootcamp. 

    The reason I picked NYCDSA was because it had the best overall reviews and I wanted to learn both Python and R (I noticed some other bootcamps focused only on Python). The whole program was well structured and organized, but quickly became very intense once we started going into completely new material. It is all definitely manageable however, and what you get out of it really will be what you put into the bootcamp yourself. The staff also makes sure to simultaneously help you get your resume ready by the time you graduate and prep you for what to expect for interviews. 

    In terms of my experience post bootcamp, the staff still does everything they can to support you in the job hunting process which I really appreciated and have a pretty large network to tap into. For myself, there was a lot of work to be done in terms of networking and preparation given that I was pivoting into an entirely different field, but I found that domain experience definitely helped in my case in terms of getting some interviews. 

    When I look back on the bootcamp I'm really amazed at all we accomplished and learned in a relatively short time. I also made a lot of close friends in my cohort given that the majority of our time during the three months was spent together. It was great  to learn alongside people with diverse experiences/backgrounds and I feel that for the most part everyone was supportive of one another. If you're interested in the field, willing to sacrifice the time, and want to challenge yourself, then I highly recommend signing up!

  • Introductory Python
    - 11/20/2018
    Americo Pietropaolo • Applicant
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    This course has been my first attempt to learn how to code. I have enjoyed it a lot. Very interesting, and I would recommend it to anybody that, as myself, has no prior experience with coding. Among other things, our teacher ( Tony Schultz) was outstanding. He is extremely well prepared, very focused on actually making us learn and like Python, is well organized, and has a very engaging way of running his lessons. Overall it has been a great experience, and it's very likely that I will take other classes at NYC Data Science Academy.

  • Michelle Vu • Director of Business Operations
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    This review is for the Introduction to Python course. My background is in business intelligence, business process and data governance. I use SQL at my job daily but have had no formal programming training.

    The introduction Python course was perfect for beginners. Tony, my instructor was extremely knowledgeable and did a fantastic job explaining concepts clearly.  I was very impressed with the number of topics that were covered in 8 sessions and how organized the course and the communication was. For an evening class after a full work day, his personality kept the class lively and interesting.

    If you’re like me and coming in with no programming background, I highly recommend reading up and taking some free online Python courses beforehand. Overall, this was a great learning experience and I highly recommend NYCDSA.

  • Richie Bui • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    Getting straight to the point, this bootcamp is for people who actually want to learn data science, not for people who are looking to get paid more money. If you don't have the passion and grit to handle an intensive bootcamp as NYCDSA teaches, don't bother coming. I am coming from a B.S. in Biology and worked for 3 years at a large company and this is my advice to those who have NO CODING experience.

    Advice:
    -DO ALL THE PREWORK. This is not a joke, do all the prework as much as you can to be prepared for what's to come during the bootcamp. Yes, it will be disappointing failing over and over again but you need to be okay and comfortable failing in order to be learning the material that is given to you
    -SUPPLEMENTAL COURSES. Along with the pre-work, they recommend you take Andrew Ng's Machine Learning course and brush up on your linear algebra because you'll need it for the end of the course and you won't have time to do that during the time of the course
    -TRY, FAIL, REPEAT. In order for you to get comfortable with coding, you're gonna need to repeat the same line of code over and over again until you get it in your head. I know, because I had to do this myself throughout the whole course.
    -ASK YOUR NEIGHBOR. The first day is always awkward meeting new people, but with how much work you have to do, you'll get over it fast and want someone else's view on a particular matter. The teachers there are great, but sometimes you'll need someone to explain in a new way or you have to explain it to someone else so you can solidify your own knowledge. So do yourself a favor and ask a neighbor before asking the teacher first.
    -PREPARE. This course can be a breeze, or this course can be a challenging experiencing. It is so easy for anyone to come and just breeze through all the material they give you, but it is really up to you to make it harder for yourself and learn topics and implementing them right away. If you want to make the most out of it then you need to COME EARLY, STAY LATE, ASK QUESTIONS.

    Many think this course starts off slow, but take this time to grab a strong foundation in your coding skills in R and Python because after the first weeks are gone, you're gonna miss it. The course becomes relentless in terms of the material they give you and how much time you have to get it done.

    PROS:
    - This bootcamp helps those who have no coding experience but does require you to have some knowledge in statistics and linear algebra (if you don't have any of that under your belt, brush up)
    -Materials given to learn and understand the material is a great learning experience
    -Super friendly and willing to help you
    -Upfront about hiring and what you need to do to get hired
    -Hiring event was an amazing opportunity to get you started with interviews

    CONS:
    -Some of the teaching staff know English as a second language, so it may be difficult for some to understand clearly what they are trying to explain. Be patient and ask questions when appropriate if you are confused.
    -Space is rather small and does not provide the most optimal space to do group projects.
    -Although the hiring event was absolutely amazing, some of the requirements do ask a pretty demanding qualification and a majority of job postings are not entry level/associate positions (keep this in mind)

    CONCLUSION
    I recommend this course to anyone who is planning to take data science as a new career path. Keep in mind that this field is interdisciplinary and allows for people of all range of backgrounds to be in data science. The teacher, staff, and fellows were amazing and glad I got to learn as much as I did, however, after this bootcamp, it is actually just the beginning of how much you actually can learn of what's out there in the field today.

  • Jian Qiao • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    Well arranged curriculum.

    Very nice and helpful professors.

    Although it seems as a great initial investment, but definitely worth it

  • Great Class!
    - 11/4/2018
    Nidhi
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    I took the part time Data Analysis and Visualization weekend course with Tony Schultz. I really enjoyed the curriculum and the class. I did not have any experience with Python before joining his class but had basic knowledge on data structures. The first class is a basic overview and felt a bit rushed for me as I had no previous python experience. However the pre-reading for this class got me upto speed on those concepts. The rest of the classes were well paced and I enjoyed learning those concepts. Tony's curriculum was very hands on and thats the bit I really liked about this class. Would recommend this class to anyone.

  • James • Student
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    (I’m reviewing the 12-Week Full Time Data Science Bootcamp. They offer an identical program online, but there are some benefits to attending the program in-person.)

    Background: I received a PhD in chemistry from a great university in 2016 and worked as a postdoc for 2 years. In grad school I learned to code and analyze data extensively with MATLAB and during my postdoc I learned very basic Python. When I reached the end of my contract I realized that I was not enjoying other parts of scientific research as much as programming and data analysis, so I decided to apply to do software engineering science-adjacent companies. With a background only in MATLAB and minor Python, I didn’t get any positive responses. After stumbling across an article about transitioning from scientific research to software engineering, I soon started reading about Data Science which sits at the intersection of my professional experience and my ideal workday schedule (coding over chemistry).

    Choosing a bootcamp: I did some research online into free data science courses and bootcamps by reading reviews and talking to a friend who was a hiring manager in software engineering. To save the money, I had considered “rolling my own” bootcamp using open and available courses online, but my friend warned me against this, saying I needed to focus more on building a code portfolio for companies to see and focus less on taking courses. Looking back, I realize she was right. Without any guidance it would have taken me 9 months or more to do what I did in 3 months at the boot camp. I focused my applications on the top-rated programs as well as ones that guaranteed jobs or provided scholarships. After being accepted into two programs (the other offering me a significant scholarship) I based my decision on the reviews of previous students- and NYCDSA had the better reviews. Deciding to change fields after a PhD felt so risky (and even foolish) that I wanted to have no regrets even if I failed. So I picked the program with the most reviews from satisfied students and with the least dissatisfied students.

    The bootcamp: The bootcamp is as hard as it should be. If it weren’t hard, employers wouldn’t take the experience seriously. I treated it like a hiatus in the rest of my life to pursue this singular goal. Sometimes you’re drinking out of a firehose, attending 3-6 hours of lecture a day while working on a project and completing the lecture homework. At the same time, it brings you close to the other students because you’re all going through the same thing. Even after doing a PhD at an Ivy League school, this was one of the hardest things I’ve ever done. 12 weeks will feel like 3 years, but I loved the other students that I got to work with. The course material is good as well- most of the lectures are well-polished and the instructors know the material deeply. Occasionally lectures are hard to follow because not all of the instructors are native English speakers, but the material is improved with every cohort because they collect constant (anonymous) feedback from the students. The instructors are constantly available and asking questions in class is highly encouraged due to the shrinking class sizes. The curriculum is set up well, as the first 4-5 weeks are primarily learning to code, the next 5 weeks focus on machine learning, and the last 2 are divided into data engineering tools and deep learning.

    The result: The most important thing you do at NYCDSA is build a 4-project portfolio in the form of a GitHub account and blog posts describing your work and best findings. These projects cover a range of skills and demonstrate your experience to potential employers. At the end of the day, there would only one thing that would make this bootcamp worthwhile... Whenever anyone asked me if I liked the bootcamp I would respond, “I’ll let you know when I get a job.” The last several weeks of the program focus on helping you practice interviews and communicate what you already know. I don’t consider myself a confident person, but by the end of this program I felt prepared enough to appear confident to potential employers, and this led to my obtaining 2 competing job offers within 4 weeks of leaving the boot camp and still daily phone calls from recruiters (even having left NYC for a much smaller data science market). In the end, no program will be perfect, but the NYCDSA is so committed to transforming itself with every group and getting students hired I’m confident that it’s at least as good as any other program out there.

  • Kelly Ho • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I really enjoyed my data science bootcamp experience at the NYC Data Science Academy. I was initially deciding between several other top bootcamps in NYC but decided to join the NYC Data Science Academy because of the comprehensive course material and programming languages (SQL, R, Python) that were taught at the bootcamp.

    The structured pre-work courses that were available online for students before the bootcamp started preparing me for a solid foundation in programming, as I come from a background with minimal programming experience.

    The instructors and TAs at the bootcamp are extremely helpful and instructive during the bootcamp in addition to the continuous career support after the bootcamp has ended.

    I also really enjoyed the environment of the bootcamp. I was able to connect with people who also love working with data and expand my network through building relationships with current and previous students from the bootcamp.

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

    Prior to my full-time 12-weeks DS bootcamp with the academy, I have started taking weekend classes in Python with the academy for my personal preparation. The weekend class is extremely helpful. You spent 5 hours on Sunday and learn as much as you could during those hours. Because it is taught by using Jupyter notebook, it is really easy to follow and to relearn over the following week. The teacher over the weekend is Tony, who is a physicist and has really good understandings of machine learning theories and on the application of data science across different fields. My weekend cohort consists of recent MBA grads who are looking to lead teams that work closely with software engineers and data scientists. You also see people from the risk practice of consulting/advisory companies. You can also see people who are in the IT department making a transition to the field of Data Science. Last but not least, you'll also have people in graduate school or even college who would dedicate their Sunday afternoon to refine their skills in Python. It is an investment that will pay dividends in your career if you are determined to bring some data science to your toolbox and your workplace.

     

    Since I have attended both the weekend class and the bootcamp, I have strong confidence in the quality of the bootcamp (faculty, cohorts, homework, material). I hope in the future the campus will have more offerings in advanced classes, and possibly classes on linear algebra or computer science in general, which are the foundations of data sci

Thanks!