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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

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  • 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

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  • Great Bootcamp
    - 3/28/2019
    Chaoran Chen  User Photo
    Chaoran Chen • Data Engineer • Graduate Verified via LinkedIn
    Overall Experience:
    Curriculum:
    Instructors:
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    Great Bootcamp. 

    The program is very comprehensive and intense. The syllabus is well structured. They make sure your time is only spent on most popular and useful technologies which are needed for a Data Scientist. 

    The instructors are very responsive. They also hold events to build the relationship between you and the potential hiring partners.

  • Andrew • Quantitative Programmer • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I went into the bootcamp with little more than a liberal arts B.A. and some online self-training. Within 6 months of finishing the bootcamp, I received multiple job offers and landed a dream job on the strength of my data science skills. The employer found me through the NYC Data Sceince Academy job network-- in that sense, the Academy continued paying dividends well after I had finished. 

    It is hard. When I read reviews that speak negatively the program, I suspect that they are from students who didn't put in the work themselves to take the many learning and job opportunities that NYC DSA provides. If you are willing to devote your time and effort to developing a new skill, you will be rewarded by this program. 

  • Mark Schott • Data Engineer • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    The NYC Data Science Academy 12 week in-person DS bootcamp will give you quality instructional resources, project experience, and beneficial job support. In 12 weeks I was able to learn a vast amount about the field, and gain the momentum necessary to secure a position 2 months after I finished. I greatly enjoyed the intense intellectual environment, as well as the comradery with my fellow students. The instructors, TA’s, and management are approachable and receptive to your needs, as long as you pester them. The bootcamp was not perfect, but I was highly satisfied with my experience, and it was worth the cost. It definitely accelerated my skill set and allowed me to gain experience, while making friends in the process.

    The curriculum has great breadth ranging from fundamental statistics, to carrying out end-to-end analyses in both Python and R, to covering big data tools. The breadth and 12 week time constraint meant that depth was sometimes lacking, but this was reasonable. It just means that you pay more attention to the subjects you're interested in or where your time would be well spent. For example, I am not very interested in NLP, so I didn't devote much energy there, and instead focused on time-series analyses.

    The instructors are all very knowledgeable, and passionate in their respective sub-areas of expertise. They were the real deal. The only drawback was that some of the lectures were hard to follow, but it was not hard to get clarification from the instructor or a fellow student.

    The bootcamp structure is open and approachable. Everyone is on the same floor in midtown Manhattan. Feedback is encouraged, and I really appreciated the opportunity to talk to the people who were in control of things, and who could help guide me in the right direction. This openness was a huge plus in my experience.

    The job support was pretty solid and consistent. I got some interviews from the hiring event, and the door was open for any job-seeking advice I had along the way. New opportunities were pushed in my direction, and I was frequently reminded to stay sharp with my skills. To help with this post-program training material was provided.

    A drawback of the program is that seeking out the help you need was harder than it should have been. As a student you were expected to seek out the help you need. This is fair, but it would have been nice to be challenged a bit more in the curriculum, for example with more mini-DS case studies. Another issue was that It was difficult to get feedback on projects. I think support with projects over the whole process could have been better. On the other hand, you get to choose your own project topics, and you will learn by doing. Also, there is continuing support after the bootcamp has ended until you find a job at the least. You also have a place to come sutdy even after the bootcamp is done.

    All in all, go down and talk to them if you have any doubts. Talk to the students, teachers, and anyone else you can find. I think if you love to learn, and you are proactive about seeking out the help you need, then this bootcamp will help you learn a ton in a short amount of time.

     

  • Jessie • Graduate
    Overall Experience:
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    N/A

    How I started

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

    Preapre for the full-time bootcamp-Prework

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

    12-Week Full-Time BOOTCMAP

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    5. Projects are just namesake. 

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

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

     

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

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

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

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

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

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

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

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

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

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

    Search for Schools:

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

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

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

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

    Prework:

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

    During the Bootcamp:

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

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

    Curriculum:

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

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

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

    Project:

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

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

    Job Assistence:

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

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

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

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

    The Bootcamp:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

     

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

     

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

     

    My advice for a strong finish.


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

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

    2. This bootcamp faces people with different backgrounds, who can choose between "how to use machine learning" and "how to implement the machine learning(more math). TAs helped a lot if you wanna learn more advanced level.

    3. Chris and Vivian helped prepare resume and the interview practice, and the hiring partner event was very helpful to present myself to the hiring managers directly. 

    Great appreciate!

  • Rahul Bhat • Graduate
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    Took the weekend course for Machine Learning with R. Course was very helpful in helping me understand the basics of Machine Learning, different models. My instructor was Luke. He was very helpful and would spend enough time covering each topic. He even took an additional class because he didnt want to rush through the material. Overall I am quite satisfied with the results. Would recommend Luke to anyone else who is interested to venture into Machine Learning field.

  • L. Kan
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    Overall:

    I will recommend this bootcamp to anyone who is eager to learn and have great passion towards data science. Before I attended the bootcamp, I received my master degree in marketing from school. I did not have a lot of math and coding background back then. With my passion towards data science, I decided to take a deep dive and applied for the NYCDSA’s 12 weeks bootcamp. Due to my limited coding background, they did not accept me at the beginning, instead, they provided one month prep course for me to get prepared for the bootcamp. I attended the September cohort after the prep course. It was one of the best decisions I have ever made. With the extensive knowledge and training, and great support in job assistance, I am able to land my dream job in 2 months after bootcamp.

    Curriculum:

    The curriculum is very well designed. I did a lot of research before I applied for this bootcamp. NYCDSA is the only program that covered data science in both R and python, big data processing tools such as Hadoop, Spark at once. I am really glad that I started my journey in data science with them, so my foundation in data science is much stronger now than I was when taking online classes by myself before. The curriculum will also help you to develop a portfolio for job hunting, however, you are the one who decide how much effort you want to put in and how far you want to go. 

    Instructors:

    The instructors and TAs are the best part of the bootcamp. You will never receive this kind of learning experience through online courses. They are all very knowledgeable in computer science, statistics, and machine learning. They are so passionate, and so willing to help each student. There were so many times that they stayed after 7pm, came in during weekends, answered slacks questions at 11pm to provide extra helps. They are far more than just instructors and TAs, but also supportive friends after I finished the program.

    Job assistance:

    I got hired by a major ad agency as a data scientist within two months of completing the bootcamp.

    The hiring team put the best effort to help their students. They hosted awesome hiring partners event, which the students got to make connections and talk to some great companies, such as IBM, citi bank, Mindshare, Publicis and so on. During my job hunting process, Vivian tried her best to provide any connections with the companies that I really wanted to get in. I also received a lot of helps and mentorship from Chris. The way he helped me to prepare for net-working and interviews are very strategic, systematic, and effective. I learned so much and I won’t get this far without him. You can learn all the hard skills anywhere else, but this kind of support and mentorship is hard to find even if you have a lot of money.

    They provide as many helps as they can, but you have to be proactive and eager to learn to take everything in! 

  • MDS • Researcher • Graduate
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    This bootcamp is no longer the great place it used to be. Vivian Zhang (the crazy CEO) has made the curriculum into a joke, and you waste a lot of time on nonsense apps like shiny, blog posts because she thinks that's what employers want. It is not. No one in the industry pays attention to the things she believes they do, which is 1 reason why it has been so difficult to place all candidates in a job. Do not believe for 1 minute she will help you get a job. Only your true qualifications will help with that. 

    Her TAs are wonderful and brilliant. It is not their fault that she bulldozes their efforts. But she has complete control of the camp. 

    It is very difficult to watch her cheapen the brand of what started out so great, and single-handedly destroy the hard work of everyone who works for her.

    She let in people to the camp who were completely unqualified to be there, including non-STEM bachelors, or MBAs with no technical background whatsoever, who were a huge burden on the class. If you are a STEM phd, you had better go to Insight or Ivy Data Science if you want an intensive course in ML and statistics. "Data science" is a marketing phrase for analytics. If you do not have a technical background, you should know that the 16,000$ package Vivian is selling you is too good to be true. You cannot be made a data scientist in 3 months, and there is no way she will be able to place you in a data science job. Most people from cohorts 5 and above are still looking for jobs months afterwards. The ones that do find jobs 6 months-1 year afterwards, do so without any help from Vivian.

    I work for a media company that is transitioning to Python. They were made aware of Vivians reputation and decided to send all their data analysts for corporate training in Metis. I was asked for my recommendation and I have to agree that the quality of teaching is better at Metis, since Vivian has fired most of her good teachers, and the median quality of students can be terrible, since she lets in who ever applies, regardless of their technical skills. I also spoke to several prospective students who told me they pulled out of DSA because they heard that Vivian was letting in anybody who wanted to become a data scientist, even business analysts, and was unable to get most people a permanent job. She lists "internships" as permanent jobs, and cannot support the candidate when those internships terminate. The people in my office who know Vivian do not think that DSA looks good on my resume mainly because they think "she is trying to industrialize data scientists like a factory".

    She also expanded the bootcamp from 20 students to 80 students, which should be a good indication of the quality you can expect.

    Some things Vivian has done:

    1. Lied by saying her acceptance rate for students is 10%. It is 100%. Maybe even 200%, if you consider all the people she tries to recruit. Do you know someone who want to be a data scientist? Tell them to send their 5000$ deposit!!
    2. Fired her main lecturer and dedicated TAs, but not before working them like slaves.
    3. Turned shiny app, visualization, web development and A/B testing into parts of the curriculum because she heard from 1 employer that they like it. It has nothing to do with "data science". She will just throw everything at a wall and see what sticks
    4. Focus first on the jobs for chinese, american, or PHD students to improve her job ratio. The way she makes ''tailored recommendations'' is to put all 100 students CVs into a ZIP file, email this zip file to HR/the HIRING MANAGER and ask if there are any jobs for people in the 'awesome' 5GB of cvs she is sending. With many exclamation points!!! 
    5. is so aggressive with employers and contacts that she scares them away. See 4.
    6. If you want to work in finance she will tell you that finance is dead in New york and no one is hiring in there anymore. You should work for a start up instead, preferably one on her list
    7. If you are coming from outside New York she will send you back there and let you find a job on your own
    8. Her main advice is: send your CV to 10 companies every day on linkedin and hope someone replies. This advice costs approx. $16,000. Do you want a mock interview from someone not from the same company you applied for? That will cost $50.
    9. If you do get a job, be prepared for an awkward phone call about how you should hire data scientists from her when you join your company
    10. Even if you are not interested in the job, she will sign you up to interview with everyone in Manhattan that she knows. Do you know this self employed guy Ben Reid or Snakes and ladders? Let them interview you for a job you don't want just for fun. Ben loves interviewing! 
    11. If you are a scientist, this is probably the worst one of all. The way Vivian prepares students for interviews is by having them memorize thousands of interview questions, categorized by company. This does two things: hide a lack of deep understanding with shallow memorization, and fool interviewers into thinking that a candidate who has memorized the entire interviewbook is more qualified than one who hasnt. It is as close to cheating as you can get, and the most obvious manifestation of the data science mass industrialization and over saturation: forget about understanding deep scientific concepts, just make sure you know what your interviewer will ask you before the interview. She is a shameless cheater.
    12. This is related to 11. If you are Chinese, Vivian will prep you before and after for the interviews. If not, she will tell you that giving you answers to interview questions is "unfair to other students."
  • Mr.
    - 1/31/2017
    Abhishek Desai • Student
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    I took the DATA SCIENCE WITH PYTHON: DATA ANALYSIS AND VISUALIZATION (WEEKENDS), with Aiko Liu.  Aiko is an excellent teacher, who taught methodically and progressively.  The course was extremely well designed, and elevated my skilset by building my understanding step by step in a structured fashion. I would highly recommend the instructor and the class if you want to truly develop a solid foundation to build your skills on.

  • peng wang • Student
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    I graduated summa cum laude from a top college and attend a top Economics PhD program. I attended a boot camp at this institution to boost my empirical research skills. To be honest, even though I've been receiving America's best education, I was still really amazed by the high quality of the education it provided.

    The coursework is highly organized with crystal clear logic flow. The faculty here are super competent and very approachable. At this program, you will learn TONS of very interesting and practical skills, build an impressive resume and open many doors in industry.

    The job placement support here is fantastic. I attended this boot camp to gain empirical research skills for academia and thus did not actively look for industry jobs, but I saw that my classmates have benefited tremendously on job search from this program. Starting from week 3, the program prepares you for professional development. Many HR's from different industries are invited for talks/info sessions/networking events. Students have many opportunities to build connections and to prove themselves. 

    You will have to work hard as the boot camp is very intensive, but that's exactly why it can give you so much and prepare you so well for your future.  

    I would highly recommend this program to anyone!

  • Spencer Stebbins • Data Scientist • Graduate
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    Preface:

    I attended the July cohort and was then a Data Science in Residence at the NYCDSA prior to accepting an offer for a Data Scientist role at a consulting firm based in NYC. I will do my best in this review to be as straightforward about my experience and address a lot of questions I had prior to the program and have understood a lot of incoming students to have had through prepping them for the program. 

    My Overall Opinion:

    Lets start with this: get over your hesitation, take a leap of faith, and you surely will not regret your decision to attend the NYC Data Science Academy 12 Week Bootcamp. Whatever reservations you have, you are not alone; almost all graduates have felt that prior to attending the program. You may be wondering about if this program will really teach me the necessary skills to get a job, am I prepared enough for this, will the program be rigorous enough, and most pivotally; is this the right decision for me? I cannot speak for you, but I can attest to my own experience and I graduated this program with no regrets, soak in a waterfall of new knowledge and skills, and have walked away a smarter, more capable, more confident professional now employed Data Scientist in field. The program is amazing, and the instructors are passionate, and you will learn a lot, but that also being said, you get as much as you put in and the first step is trusting in yourself to join and commit to the three rigorous and insightful months ahead of you. 

    My Background:

    Prior to attending NYCDSA, I was a software engineer at a large shipping company for almost 2 years focusing primarily on front end development. Before that I attending another bootcamp called Hack Reactor, similar to NYCDSA, to study software engineering and javascript. It was growing interest in machine learning, the success of my experience at Hack Reactor and the thirst for that similar immersive and intensive program that led to me to the NYC Data Science Academy. In another life, I graduated from NYU with a degree in Music Business.

    Common Questions:

    Do you need a masters or a background in a quantitative field?

    The answer is no. No, you do not need a masters degree or professional experience in a quantitative field. During my cohort, I thought some of the best and most creative presentations and chosen topics by students were from some that had no prior coding or heavy math experience. That being said, coming in with a math or software engineering background will definitely allow you to vamp up your projects flashiness and you may have an easier time understanding some of the formulas associated with the advanced machine learning algorithms. 

    Will I get a job from this program? What are the job stats? etc.. 

    Although I can't attest to job stats, I received my first offer less than 6 weeks after the program ended. I met the firm at the Career Day at the end of the program. Vivian, the CEO of NYCDSA, is the job placement tsar and wizard and without her and her relationships with so many great firms there would not be as many companies at the career day as there are: Spotify, JP Morgan Chase, MindShare, etc.. Vivian will do every in her power to connect you with your dream job. That being said, getting an offer is entirely on you. You must have great visual and complex projects, you must interview well, and you must be actively job seeking and cultivating relationships on your own. Vivian and the team will do everything they can to prepare you and connect you with your dream job, but you need to show up with the bus fair to ride the bus. I cannot stress this enough. 

    How does this compare to other bootcamps?

    I am actually in a unique position to answer this question. A few years prior to graduating NYCDSA, I graduated Hack Reactor, which is a similar 12 week bootcamp, but for software engineering. Prior to that I took web development course at General Assembly, Coursera Machine Learning courses, Harvard Extension School’s Intro to Data Science class to name a few. Needless to say, I’m hungry for knowledge and a challenge. I can’t speak to the other Data Science bootcamps out there like Metis or Galvanize, but I can say that there is nothing like 3 months of learning something at a such a rapid pace. The sum of its parts is greater than the whole and you can spend years learning on your own secondary to whatever else you are doing or you can jump in the deep end. I did before with Hack Reactor and walked away amazed and the same held true for NYCDSA. 

    Inside Tips:

    Pick good project topics. Your projects will be portfolio to potential employers and if your project outshines the other students, employers will take notice. 

    Pick good project team mates. I didn't have a poor experience with anyone I worked with, but it goes without saying that if you pick bright, easy to work with project partners, you'll be able to build a better end product.

    If you don’t know how to code in R or Python and have no prior experience code - Learn now. Codeschool.com is an excellent introductory resource. 

    DO NOT think you will have a social life. This is only 3 months of your life, but 3 very important months, so give it your all and don't expect to get as much out of it if you don't.

    Comments to the Academy:

    Scaling the program will require more job assistance personnel.

    Increasing the amount of coding done in the program may be beneficial as most jobs now require coding every day and are rarely purely theoretically

    Additional content on computer vision would be fun

    More TAs and one on one knowledge quizzes (like done as a group at the end of the course)

Thanks!