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NYC Data Science Academy

New York City, Online

NYC Data Science Academy

Avg Rating:4.83 ( 272 reviews )

NYC Data Science Academy offers 12-week data science bootcamps in New York City. In these programs, students learn beginner and intermediate levels of Data Science with R, Python, Hadoop, Spark, Github, and SQL as well as popular and useful R and Python packages like XgBoost, Caret, dplyr, ggplot2, Pandas, scikit-learn, and more. Once the learning foundation has been set, students work on multiple projects through the bootcamp. The program distinguishes itself by balancing intensive lectures with real world project work, and by the breadth of its curriculum. Throughout the program students work alone and in teams to create at least four projects that are showcased to employers through multiple channels; private on-campus hiring partner events, student blogs, meetups, and filmed presentations. 

Ideal applicants should have a Masters or PhD degree in Science, Technology, Engineering or Math or equivalent experience in quantitative science or programming. Candidates with BA’s who have appropriate experience are also considered.

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

Recent NYC Data Science Academy Reviews: Rating 4.83

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  • 12-Week Immersive Data Science Bootcamp (In-person)

    Apply
    Start Date Rolling Start Date
    Cost$17,600
    Class size50
    LocationNew York City
    In this program students will learn the modern data analytic techniques and master the requisite skills, such as Python and R programming languages as well as Hadoop, to address real-world data science problems. Throughout the program, students work alone and in teams to create at least five projects that are showcased to employers. Along the way, students will have assistance in preparing for the job search through resume review, interview preparation, and opportunities to interview with our hiring partners. Successful completion of the curriculum will present a certification of graduation certified by the New York State Board of Education.
    Financing
    Deposit$5,000
    Financing
    • Climb Credit Loan $400* pm for 60 months

    • Full Tuition Total $17,600

    • Skills Fund Student Loan
    $397.88 pm for 60 months
    Tuition PlansWe have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    ScholarshipLimited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill LevelIdeal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Workhttp://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement TestNo
    InterviewYes
  • Big Data with Amazon Cloud, Hadoop/Spark and Docker

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    Data Science, Python, Hadoop, Spark, Data Structures
    In PersonPart Time5 Hours/week2 Weeks
    Start Date January 14, 2020
    Cost$2,990
    Class size10
    LocationNew York City
    This is a 6-week evening program providing a hands-on introduction to the Hadoop and Spark ecosystem of Big Data technologies. The course will cover these key components of Apache Hadoop: HDFS, MapReduce with streaming, Hive, and Spark. Programming will be done in Python. The course will begin with a review of Python concepts needed for our examples. The course format is interactive. Students will need to bring laptops to class. We will do our work on AWS (Amazon Web Services); instructions will be provided ahead of time on how to connect to AWS and obtain an account.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelStudents are expected to be familiar with using an operating system from the command line; knowledge of Python is helpful.
    Placement TestNo
    InterviewNo
    More Start Dates
    January 14, 2020 - New York City Apply by January 10, 2020
    April 21, 2020 - New York City Apply by April 15, 2020
  • Data Science with Python: Data Analysis and Visualization (Weekend Course)

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    Start Date January 19, 2020
    Cost$1,590
    Class size20
    LocationNew York City, Online
    This five week course is an introduction to data analysis with the Python programming language, and is aimed at beginners. We introduce how to work with different data structure in Python. We covered the most popular modules, including Numpy, Scipy, Pandas, matplotlib, and Seaborn, to do data analytics and visualization. We use ipython notebook to demonstrate the results of codes and change codes interactively during the class. Our past students include people with no programming experience or those who have minimal exposure to Python. Students told us our classes are very informative, engaging, and hands-on.
    Financing
    DepositN/A
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    Getting in
    Minimum Skill LevelKnowledge of basic data types (e.g. string, numeric), data structures (e.g. list, tuple, dictionary) Familiarity with concepts of list comprehension and for/while loop
    Placement TestNo
    InterviewNo
    More Start Dates
    January 19, 2020 - New York City Apply by January 15, 2020
    March 7, 2020 - New York City Apply by March 1, 2020
  • Data Science with Python: Machine Learning (Weekend Course)

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    Data Science, R, Machine Learning, Artificial Intelligence
    In PersonPart Time7 Hours/week5 Weeks
    Start Date January 19, 2020
    Cost$1,990
    Class size10
    LocationNew York City, Online
    This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. This includes linear regression, Naïve Bayes classifiers, logistic regression, linear discriminant analysis, cross-validation, bootstrapping, feature selection, regularization, model selection, SVM, decision trees, random forest, PCA, K-Means, and Hierarchical clustering. In addition, this course teaches the basics of natural language processing. After successfully completing this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelCompletion of Data Science with Python: Data Analysis; Data Science with R: Machine Learning
    Placement TestNo
    InterviewNo
    More Start Dates
    January 19, 2020 - New York City Apply by January 15, 2020
    March 7, 2020 - New York City Apply by March 1, 2020
  • Data Science with R: Data Analysis and Visualization (Weekend Course)

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    Data Science, R, Data Visualization, Data Analytics , Data Structures
    In PersonPart Time7 Hours/week6 Weeks
    Start Date January 18, 2020
    Cost$2,190
    Class size15
    LocationNew York City, Online
    This course is designed to provide a comprehensive introduction to R. Students will practice programming and analyzing data with R. Students will learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models to data. In addition to a theoretical framework in which to understand the process of data analysis, this course focuses on the practical tools needed in data analysis. This course also covers the creation of dynamic reports with the knitr package in R as well as the creation of dynamic dashboards with R Shiny. By the end of the course, students will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting the code.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelBasic knowledge about computer components Basic knowledge about programming
    Prep WorkNone
    Placement TestNo
    InterviewNo
    More Start Dates
    January 18, 2020 - New York City Apply by January 15, 2020
    April 18, 2020 - New York City Apply by April 10, 2020
  • Data Science with R: Machine Learning (Weekend Course)

    Apply
    Data Science, R, Machine Learning
    In PersonPart Time7 Hours/week6 Weeks
    Start Date January 18, 2020
    Cost$2,990
    Class size40
    LocationNew York City, Online
    This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R. It will introduce you to data mining, performance measures and dimension reduction, regression models, both linear and generalized, KNN and Naïve Bayes models, tree models, and SVMs as well as the Association Rule for analysis. After successfully completing this course, you will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, and implement these methods to solve real-world problems. Unit 1: Foundations of Statistics and Simple Linear Regression Unit 2: Multiple Linear Regression and Generalized Linear Model Unit 3: kNN and Naive Bayes, the Curse of Dimensionality Unit 4: Tree Models and SVMs Unit 5: Cluster Analysis and Neural Networks Final Project After 35 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelKnowledge of Python programming Able to munge, analyze, and visualize data in Python
    Prep WorkKnowledge of R programming Able to munge, analyze, and visualize data in R
    Placement TestNo
    InterviewNo
    More Start Dates
    January 18, 2020 - New York City Apply by January 15, 2020
    April 18, 2020 - New York City Apply by April 15, 2020
  • Data Science with Tableau (Online)

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    Data Science, Data Visualization, Business Intelligence
    In PersonPart Time5 Hours/week3 Weeks
    Start Date Rolling Start Date
    Cost$1,590
    Class size20
    LocationOnline
    This course offers an accelerated intensive learning experience with Tableau – the growing standard in business intelligence for data visualization and dashboard creation. Without prior experience, students will learn to work with multiple data sources, create compelling visualizations, and roll out their data science products for continuous, scalable outputs to key stakeholders. By building insight and weaving narrative, students will be empowered to harness data in a striking way that provides value to organizations large and small. This course offers an accelerated intensive learning experience with Tableau – the growing standard in business intelligence for data visualization and dashboard creation. Without prior experience, students will learn to work with multiple data sources, create compelling visualizations, and roll out their data science products for continuous, scalable outputs to key stakeholders. By building insight and weaving narrative, students will be empowered to harness data in a striking way that provides value to organizations large and small. We utilized 4 user cases drawn from finance (public data from major stock exchanges) and sitcom data (Game of Thrones 1 ).
    Financing
    Deposit1590
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    Getting in
    Minimum Skill LevelKnow how to use Mac, Windows. Familiarity with relational databases is preferred but not required. gain a greater appreciation for the logic underlying Tableau’s features utilize their capstone project to visualize ‘big data’
    Prep WorkBefore the course begins, pre-work will be available for students interested in strengthening their ability to access and extract data from relational databases (e.g. SQL-based servers).
    Placement TestNo
    InterviewNo
  • Deep Learning with Tensorflow (Weekends and In-Person Only)

    Apply
    Start Date None scheduled
    Cost$2,990
    Class size15
    LocationNew York City
    Via analogy to biological neurons and human perception, this course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, the most popular open-source Deep Learning library. Essential theory will be covered in a manner that provides students with an intuitive understanding of Deep Learning’s underlying foundations. Paired with hands-on code run-throughs in Jupyter notebooks as well as strategies for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across the major contemporary families: Convolutional Nets for machine vision; Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis; Generative Adversarial Networks for producing realistic images; and Reinforcement Learning for playing video games.
    Financing
    DepositN/A
    Getting in
    Minimum Skill LevelObject-oriented programming, ideally Python (introductory course: https://nycdatascience.com/courses/introductory-python/) Simple shell commands, e.g., in Bash (tutorial of the fundamentals: https://learnpythonthehardway.org/book/appendixa.html)
    Placement TestNo
    InterviewNo
  • Introduction to Python (Online)

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    Data Science, Python, Data Visualization, SQL, Data Structures
    OnlinePart Time5 Hours/week2 Weeks
    Start Date Rolling Start Date
    Cost$795
    Class size25
    LocationOnline
    The Introduction to Python covers the basic Python programming. Graduates will be able to write Python programs that read files, manipulate strings, and perform mathematical computations. The class is hands-on; participants will learn to write Python program by practicing coding along the way. The course covers most of the basic features of the Python language, including built-in data types and control structures, Python’s featured supporting object-oriented and functional programming, intro to Pandas, and regular expression.
    Financing
    Deposit795
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    Getting in
    Minimum Skill LevelComfort with Windows, Mac or Linux environment and ability to install third-party software.
    Prep WorkThis is a class for computer-literate people with no programming background who wish to learn basic Python programming. The course is aimed at those who want to learn “data wrangling” – manipulating downloaded files to make them amenable to analysis.
    Placement TestNo
    InterviewNo
  • Introductory Python (Evenings)

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    Start Date January 14, 2020
    Cost$1,590
    Class size40
    LocationNew York City
    This is a class for computer-literate people with no programming background who wish to learn basic Python programming. The course is aimed at those who want to learn “data wrangling” – manipulating downloaded files to make them amenable to analysis. We concentrate on language basics such as list and string manipulation, control structures, simple data analysis packages, and introduce modules for downloading data from the web. This Introductory Python class runs over four weeks, with five hours of class per week (split into 2 ½ hour evening classes). Classes will be given in a lab setting, with student exercises mixed with lectures. Students should bring a laptop to class. There will be a modest amount of homework after each class.
    Financing
    Deposit$1590
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    Getting in
    Minimum Skill LevelThis Introductory Python class is designed for computer-literate people with no programming background who wish to learn basic Python programming.
    Prep WorkIn the class, we will use Python 3. If you are following this video to set up Python environment, please make sure you download the Python 3.X version starting from 1 min 23 s in the video. Link: https://vimeo.com/160172414
    Placement TestNo
    InterviewNo
    More Start Dates
    January 14, 2020 - New York City Apply by January 10, 2020
    March 9, 2020 - New York City Apply by March 5, 2020
  • Remote Immersive Data Science Bootcamp (Online)

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

    • Full Tuition Total $17,600

    • Skills Fund Student Loan
    $397.88 pm for 60 months
    Tuition PlansWe have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    ScholarshipLimited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill LevelIdeal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Workhttp://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement TestNo
    InterviewYes
  • Remote Self-paced Data Science Bootcamp (Online)

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

    • Full Tuition Total $17,600

    • Skills Fund Student Loan
    $397.88 pm for 60 months
    Tuition PlansWe have full-financing available through SkillsFund and ClimbCredit financial loan. It is approximately 400/per month for 60 months.
    Refund / GuaranteeNYC Data Science Academy’s refund policy adheres to both ACCET and NYS Education Department guidelines. Visit https://nycdatascience.com/refund-and-regulations/ for more details.
    ScholarshipLimited number of scholarships available to qualified candidates.
    Getting in
    Minimum Skill LevelIdeal applicants should have a Masters or Ph.D. degree in Science, Technology, Engineering or Math or equivalent experience of quantitative science or programming.
    Prep Workhttp://blog.nycdatascience.com/faculty/data-science-bootcamp-pre-work/
    Placement TestNo
    InterviewYes

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  • Richie Bui • Graduate
    Overall Experience:
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    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
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    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:
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    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
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    (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
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    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
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    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

  • Raymond Liang • Student
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    Coming from the finance field, I had very little experience in coding. I had dabbled with a little bit of VBA where I worked, but by no means could I have claimed to know how to code. That being said, I feel this class provided me a great foundation to Python. Tony, the instructor, made sure the concepts were reinforced by easy to understand examples, and even silly examples at times which was greatly appreciated. The in class exercises were also helpful to glue the concepts together and homework was also given for more practice.  The one thing I could have suggested in terms of the weekend class itself is to perhaps allow for a longer day so the teacher do not have to rush over any particular topic. Overall, I really enjoyed this class and would recommend it to anyone who is looking to get their hands dirty with coding and data analysis.

  • Dav D. • Graduate
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    I joined NYCDSA in late 2016, and there is no doubt about that the Data Science Bootcamp had helped me a lot in achieving my career goal. I was looking among several data science bootcamp back then, and my primary concerns were job placement rate, background of people who might join the bootcamp, and what industries had the alumni been working for. I decided to join NYCDSA because it gave me statistical answer of my concerns, not to mention how resourceful it would be speaking of its alumni network. 

    The only thing that I felt they could do better, well in 2 yrs ago, would be the career service, and the design of project groups. It would be helpful to arrange people with working experience with those who may not.

     

    The best thing about this program is its brand. I had several final-round interviews and some of the interviewers were from NYC. These interviewers, they know about NYCDSA, how the curriculum is organized, and how much practice students would receive from the bootcamp, so it is not challenging for me to leave some impression on them.

    If you are thinking about a career switch, enrichment of experience on resume, or just an investment towards personal skillset, NYCDSA is definitely the place where you would wanna go.

     

  • Anthony Tagliente • Data Associate
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    This is the second course I've taken with NYC Data Science Academy, they continue to offer excellent courses and materials.

  • Lee S-K • Student
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    I really enjoyed the Data Analysis and Visualization course.  I took the Intro to Python class with Tony a couple months ago, and this course (also taught by him) was a great next step in my journey towards hopefully becoming an expert in Python.  Whereas the first course gave us a great background in the basics, this one gave us a more concentrated ability to apply what we had learned to real-world problems (analyzing data and being able to display the results, which is more what you would do in a work environment).  Also, the opportunity to put together our own project at the end of the coures is extremely useful.  Highly recommend this course!

  • Raj Atri • Student
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    I've taken both the introductory and Data Science course with Tony and it's been an incredibly effective and fruitful introduction to coding. I'm at the point now where I am working on a different project with python everyday and am greatly looking forward to the next class i will be taking. 

  • Deekshita Amaravadi • Web/Mobile Engineer • Graduate
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    I took the Deep Learning Course from July - August 2018. This is a part time course for 5 weekends. Jon Krohn is an amazing instructor. I am a Web Engineer and have zero to little idea about Deep learning. For me to sit in class and not be intimidated by the course and fundamentals of Deep learning is a testament to Jon's teaching. The course is a really good starter kit for anyone looking to get into Deep Learning and AI in general. There is some knowledge of python that might be useful to have prior to taking this course as there are many live exercises worked on python. You will be given an opportunity to complete a project during the course which I suggest you do as it will make you apply the skills you review right after class that will make learning the concepts more enjoyable. All in all, I will 100% recommend this course for anyone who is looking to get a thorough overview of the fundamentals and philosophy of Deep Learning and its future.

  • Intro to Python
    - 7/24/2018
    Lee S-K • Student
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    The Intro to Python course was great!  Tony is a fantastic teacher who really took the time to get us acquainted with a new programming language, while simultaneously making the course challenging through both the in-class exercises and the homework assignments.  I felt the course was especially well structured, as each part really built on top of the last to provide us with a clear understanding of the basics of Python.  I highly recommend this course to anyone interested in starting on a path towards Python fluency.

  • Raajiv R • Graduate
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    I did not have experience in programming before this course. Had taken some courses in Java and pearl almost 9 years ago. I have a healthcare background and I was worried if this course will be advanced for an entry level. I can say now with confidence that this course not only taught me the basics of python and data analysis but also established a foundation to which I will be able to add advanced concepts in the future. I like the way the course was structured. Tony was punctual and the class always started on time. Tony encouraged us asking questions and was patient in explaining the concepts. Overall very happy with my decision to take this course and would recommend to anyone who is ooking to jump into the field of datascience.

  • Vera Feng • Student
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    As a Math major interested in Data Science, I've benefited tremendously from this course. Tony is a structured, detailed instructor. The course materials are practical and handy; and the lectures were helpful in both understanding the materials and honing up a programming mindset. I walk away from this class, feeling ready for challenges in my future job. I would strongly recommend this course with Tony to anyone who is new to programming and wants to have a head start. 

  • Dean Goldman • Data Engineer • Graduate
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    I could not be more satisfied with my experience at NYC Data Science Academy. I met incredibly impressive and interesting people, learned more than I ever thought I could, made friends, and worked very hard. Within a few weeks after graduating, I was offered my first full-time job as a data engineer. (I do not come from a CS, math or stats background).  

    NYCDSA is very succesful at getting their students fluent in the tools and technologies of data science, and prepared for finding great jobs in the field. The bootcamp is exhilerating, and the people are truly the best. By the end, you will be ready to for a data science position, and you will have broadened your horizons.

  • Lakshmi Prabha Sudharsanom • Data Scientist • Graduate
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    I took the 12-weeks bootcamp at the NYC Data Science Academy. I am a postdoc in India. I had been writing graph algorithms for social network analysis and analyzing data using MS-Excel. I decided to extend myself to data science from data analysis.

    I researched for about six months to choose a proper academy to learn data science. I came up with a decision to join NYC Data Science Academy, because I can learn both R and Python within three months. But, I learnt more than I thought. I gained hands-on experience in Python, R, SQL, Spark, deep learning, Hadoop and Hive.

    The materials are technically sound for job interviews. Many experienced professors and industry professionals design and teach the materials. The course laid a solid foundation in data science. The people in the academy show keen interest in every student to understand the concept, in resume writing and above all, to get a job.

  • Yisong Tao • Graduate
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    I attended the 12-week boot-camp at NYC Data Science Academy from Sep. 2016 to Dec. 2016. Before that I got my PhD in Chemistry and worked 6 years' as a Post-doc in biochemistry. 

    The curriculum was well organized. 12-week courses covered R and Python programming, statistical analysis and machine learning. During the last few weeks, big data tools like Hadoop and Spark were also covered. For one coming from academic background, the breadth of topics and difficulty level felt just right. The boot-camp courses weren't supposed to teach you everything, but they did prepare me very well if I wanted to explore further data science topics.   

    The boot-camp was intense. I was only sleeping for 4 hours during boot-camp days. It was demanding both physically and mentally, but I wasn't alone in the journey. The instructors, TAs and classmates were all amazing. This boot-camp takes students from very different backgrounds, which I think is a unique advantage. I was able to see people applying data science tools to their own expertise brilliantly, fashion, marketing, IT, health care... It was very helpful for me who was looking to step outside of academia. 

    The job assistance is good. There is networking, resume-editing and mock-interviews. Vivian also personally helps students tracking their job hunting status. I wasn't able to use lots of their services due to my personal situation, but for the one position I applied for I was able to impress the hiring manager with the machine learning knowledge and skills I acquired at NYCDSA and got an offer the same day. 

  • Great class!
    - 3/3/2018
    Chris
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    Great class. The instructor did a great job of keeping us on track and packing a lot of content into 20 hours. 

  • Mustafa Koroglu • Post-Doctoral Fellow • Graduate
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    The motivation in attending a data science bootcamp generally starts with reading testimonials, which are the experiences and thoughts of past fellows. I believe that my experience as a remote participant in the Summer 2017 bootcamp is unique as the NYC Data Science Academy made it live stream to me throughout the bootcamp. This is totally different experience than doing bootcamp online as I was able to join lectures, industry expert speaker sessions, and workshops as the fellows did in person. This was indeed great opportunity for me as I was able to ask my questions during the lectures and seminars and participated actively with my comments. The crucial point here is that live streaming the bootcamp and communicating through Slack channel made it possible for the team at the Academy to track my progress in the assignments and projects daily and encouraged me to participate fully in the bootcamp. 
     
    I can definitely say that attending the NYC Data Science Academy bootcamp was one of the greatest investments in my life. I learnt how to code efficiently in R and learnt coding in Python with real life projects. I am hundred percent sure that my attendance to the bootcamp let me to find my current data science related post doctoral position. I could suggest future candidates to take some initial online courses for machine learning and deep learning, where they will find themselves more comfortable while approaching highly-technical projects in the bootcamp. 
     
    One of the strongest side of this bootcamp at the NYC Data Science Academy is the time and effort that the hiring team spends on. For remote participants, this is much more valuable than any other thing as you are receiving constant help in finding best job depending on your portfolio. And you feel that you are a member of an excellent group of data scientist. As an active learner of data science field, I recommend the NYC Data Science Academy with full of confidence.
  • Al Mercado • Student
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    The approach in this course was more different that what I've seen in most workshops on this topic. It was more visual because Deep Learning sometimes requires mathematical derivations to understand the algorithms. I have read a few seminal Google papers on the topic and they're not alwasys easy to follow. John did not dive too much into this area and preferred to use powerpoint slides and diagrams by hand to explian it before we looked at the code.

    Overall the coding portion was made easier by John's coding style and we were able to follow along with the Keras modules. However, we did not get to cover all the topics (like autoencoders or deep belief networks) that I've seen other presentations get into and I would have liked to learn more. But the foucs on NLP and the last topic on GAN got me more curious about those topics. I would recommend it to those who want to learn it more and I would even be willing to talk about what I tried as a result of taking the course in any future session that he runs with a new group.

     

  • Former Alumni from Cohort 003 • Data Scientist • Student
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    I was previously a student in one of the early cohorts of for the 12-Week Data Science Boot Camp, and it was one of the best experiences for my career.

    Before going into lengthy detail, I will break this up into three sections: Before, During, and After in the hope of conveying a more comprehensive experience. 

    Before:

    Before attending the NYCDSA, I had just finished my undergraduate degree in an engineering field.  After taking an Intro ML course my senior year, I grew increasingly interested in the applications of AI and Machine Learning in industry.  When I discovered the field of data science, I was instantly hooked, but I knew I needed to learn more. 

    I had a decent amount of programming experience, mostly with low-level languages and systems, but lacked statistical intuition and experience with scripting langauges.  After some more digging, I knew that my skillset at the time would not be competitive for the actual job market.  While my undergraduate education was excellent, the curriculum was reasonably classic, most of my courses focused on theory and textbook problems and what I lacked was skills and experience for real-world applications.  While graduate school was an option, at the time I wasn't sure if Data Science was a field I wanted to commit more formal education to without experiencing it first hand.  So after applying to various boot camps, I chose the NYCDSA to jumpstart my career. 

    The interviewing process for the NYCDSA was simple and effective and included phone screens, code exercises, and some Q&A.  I chose this program because the company was relatively new and small and felt more like a community rather than a factory to churn out data scientists like other boot camps I came across.  After making my decision, I promptly moved to NYC after graduating and began my journey into data science. 

     

    During:

    The company was relatively new at the time I attended, but the caliber and experience of their faculty were top-notch, and it was transparent how passionate the staff was about developing the best and most efficient curriculum.  Within another couple of years, they were able to scale up, expand, and further improve their curriculum and course content at an exponential rate.

    It is important to note that your mileage may vary with any boot camp.  This program offers awesome resources, but ultimately it's up to the student to determine how much he or she will get out of it.  I spent an average of 60~90 hours a week on campus for the 12-week program.  The course is still manageable for people who have families or other commitments, and I have seen many successful part-time students graduate from my cohort.  I just wanted to note that this was particular to my experience.  

    The curriculum covers various aspects of data science and offers a cutting-edge foundational overview of stats, machine learning, and programming.  The core languages are R and Python.  The philosophy of spreading the breadth of languages rather than just choosing one is reflective of the fact that the tools in Data Science are highly mutable.  Rather than memorizing a single programming paradigm, the goal is to expand your programmatic and discrete logic intuition so that you can pick up other languages if needed.

    After class, the projects were engaging, and it was enjoyable to engage with other data enthusiasts and attend data science meetups and conferences together.  I still keep in touch with many of my cohort mates years after the program. 

    Moreover, Vivian and company have a robust network of many NYC companies and quickly expanding to other areas of the country.  By the 9th week, I've already had a couple of interviews lined up at top firms in the area and was able to practice and refine my onsite interviewing skills.  

    Three months after the boot camp, I landed my dream first job at a fast-growing tech-startup in Chicago after receiving multiple offers.

     

    After:

    After landing my role as a Data Scientist, I still maintained a close relationship with the NYCDSA.

    The support of the NYCDSA does not terminate at the end of the cohort, and they do a phenomenal job of providing ongoing support for their alumni and students.  After the boot camp, I've continued to receive ongoing help (practice interviews, coding exercises, access to review course content, network events, resume editing) which were crucial to my future employment.

    Not only do they offer support for students seeking employment but they provide support for education as well.  I was actively interested in an advanced degree in a field that leverages machine learning, and my experience at the boot camp only affirmed this for me.    Many of the faculty come from incredible backgrounds and are very willing to share their experiences.  Their advice and transparency were constructive in my choice of graduate program.  Currently, I am a part-time Masters candidate at a top data science program with a focus on Machine Learning and Data Ethics while working full-time as a data scientist at my current company. 

    The skills that I learned from the NYCDSA and later refined at my current company significantly prepared me for my graduate courses, allowing me to test out of basic classes and attend advanced topics in machine learning and statistics.   The skills from the boot camp carry over very well for most data science roles or education programs.

    In short, what I wish to convey is that a career in data science is a journey of continual learning and NYCDSA does everything they can to optimize the learning experience for their students.

    However, please note that the Data Science Bootcamp is tailored to provide balanced curriculum among programming, stats, and machine learning and is meant to be a program to strengthen core skillsets rather than a masterclass for already experienced data science veterans.  Although the company does offer more specialized classes in more targeted areas. 

    If you are looking for a program that will teach you everything there is to know about data science within a mere few months, then I'm afraid that those may be in general hard to find since data science itself is both a hybrid of multiple subjects and growing as a professional field. 

    On the other hand, if you are serious about transitioning into data science as a career and if you are seeking a program that offers competitive curriculum, a strong alumni network, and a community of data enthusiasts to engage with, I would strongly recommend looking into this program to jumpstart your career.  I cannot emphasize enough how much this program has benefitted my professional development both during and after the boot camp. 

     

     

     

  • Kathryn Bryant • Data Scientist • Graduate
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    Attending the NYCDSA 12-week bootcamp is the best career decision I've ever made. I came to it after finishing my PhD in math and working for a year in academia. I wanted to get into data science for lots of reasons (leveraging my background, interesting problems, ample job opportunities, interest in coding, good pay, etc.) but after spending six years in graduate school, the last thing I wanted to do was go back to school for two years to learn basic data science skills.

    This program allowed me to do this career change quickly without compromising the quality of the material I learned. The program emphasizes sound foundations in coding in R and Python and in both the theory and application of machine learning. The pace is very fast but you learn and do an incredible amount in those 12 weeks. Even when full absorption of the material isn't possible, the bootcamp does a great job of exposing you to tools and concepts you'll need to be familiar with later. 

    The projects are incredibly valuable for building skills and exposing future employers to your work. You would be spending your money wisely just to come and complete the projects.

    The bootcamp students and the TAs/instructors have varied backgrounds, which also makes for a great environment because you can learn from everyone. The entire company - from fellow students to TAs to instructors to the CTO and COO - is filled with smart, hardworking people who are pushing to help you reach your potential. 

  • W. Zhou • Informatics Specialist • Graduate
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    WHY Data Science and WHY NYCDSA: I had no idea about data science until 2016 Feb when Alphago defeated Li Shishi and it was the first time for me to get to know what is Artificial Intelligence and what is machine learning. Bringing my huge curiosity, I self-learnt an online Machine Learning course on Coursera and was able utilize the skill , for the first time, in my working project and had a positive outcome. Being proud of my achievement, I also realized there was still a long way down the road.  This is actually the reason why I decided to join bootcamp -  fully armed myself with comprehensive data science skillset and then shifting my career towards a new page. NYCDSA is a perfect choice for me as it teaches anything you need to know to work as junior data scientist and allows you to keep full time job as the same time.

     

    Experience: Even though it is an part-time online bootcamp, I was investing 40+ hours / week on studying slides, having office hour with TA and doing projects with current full time students. The bootcamp starts with an introduction Toolkits (Unix,Git,SQL,etc.), followed by introduction to R/Python, then followed by statistics and machine learning immediately applied using R and Python. The comprehensive curriculum is completed by an introduction to Big Data tools. I was able to finish a strong portfolio with 4 projects in Shiny App, Web Scraping, Kaggle and Capstone. I learned a lot especially by collaborating with full time students and TAs, so I would strongly recommend those online students who are physically in NYC, walk in the classroom and collaborate with full-time students on your last two projects.

     

    In terms of job assistance, NYCDSA can provide tremendous assistance on your job hunting after your graduation. Vivian and Chris know what you need to have on your resume to catch HR’s eye and get an interview, and they have a strong network which can expose you to much more opportunities.

     

    Outcome: I finally received offer from 2 Top insurance companies, 2 Top hospitals, and 2 boutique consulting companies. I feel my investment of 7 months of study and $16K totally worth as it allows me to finally launch my dream job. Thank you New York City Data Science Academy and I would recommend it to anyone who has same dream to be a data scientist.

     

Thanks!