nyc-data-science-academy-logo

NYC Data Science Academy

New York City, Online

NYC Data Science Academy

Avg Rating:4.83 ( 281 reviews )

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

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

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

Recent NYC Data Science Academy Reviews: Rating 4.83

all (281) reviews for NYC Data Science Academy →

Recent NYC Data Science Academy News

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

    Apply
    Start Date March 30, 2020
    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
    $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
    More Start Dates
    March 30, 2020 - New York City Apply by March 18, 2020
  • 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 April 21, 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
    April 21, 2020 - New York City Apply by April 20, 2020
  • Data Science with Python: Data Analysis and Visualization (Weekend Course)

    Apply
    Start Date March 7, 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
    March 7, 2020 - New York City Apply by March 6, 2020
    April 19, 2020 - New York City Apply by April 18, 2020
    June 13, 2020 - New York City Apply by June 12, 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 March 7, 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
    March 7, 2020 - New York City Apply by March 6, 2020
    June 13, 2020 - New York City Apply by June 12, 2020
    April 19, 2020 - Online Apply by April 18, 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 April 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
    April 18, 2020 - New York City Apply by April 17, 2020
  • Data Science with R: Machine Learning (Weekend Course)

    Apply
    Data Science, R, Machine Learning
    In PersonPart Time7 Hours/week6 Weeks
    Start Date April 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
    April 18, 2020 - New York City Apply by April 17, 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 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)

    Apply
    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)

    Apply
    Start Date March 9, 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
    March 9, 2020 - New York City Apply by March 8, 2020
    April 20, 2020 - New York City Apply by April 19, 2020
    June 2, 2020 - New York City Apply by June 1, 2020
  • Live Online Data Science Bootcamp (Online)

    Apply
    Start Date March 30, 2020
    Cost$14,960
    Class size25
    LocationOnline
    Our Live Online 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
    • Full Tuition Total $17,600
    • Climb Credit Loan $400* pm for 60 months
    • SkillsFund 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
    More Start Dates
    March 30, 2020 - Online Apply by March 18, 2020
    March 30, 2020 - Online Apply by February 15, 2020
  • Part-time Online 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
    • Full Tuition Total $17,600
    • Climb Credit Loan $400* pm for 60 months
    • SkillsFund 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

Review Guidelines

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

Click here to log in or sign up and continue.

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

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

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

You must log in to submit a review.

Click here to log in or sign up and continue.

Shared Review

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

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

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

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

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

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

  • Chao Shi  User Photo
    Chao Shi • Graduate Verified via LinkedIn
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    Long story short --

    I have a PhD in computational geoscience and worked as a geophysicist in Houston for five years. I joined NYCDSA for the 12-week bootcamp, and worked as hard as I could. I was hired after my first interview, with an offer in hand within two weeks post graduation. NYCDSA has helped me achieve this smooth transition into a brand new field in just 3.5 months.


    How I made the decision to join --

    1) The time commitment is right: I was willing to put in a few months of my time through well-designed highly-intensive training, rather than spending a year or so to learn on my own. I do not want to go through a one-to-two-year data science master's program, considering a) I have a computational PhD degree, and b) although many data science theories have been long established, data science platforms and tools are evolving fast.

    2) Word-of-Mouth: I have friends in New York working in the data domain recommending this academy over other data science training offerings. "richer content", "up-to-date material", "good instructors" are among the key words that I recall.

    3) A balanced focus on teaching and job service: I have interviewed with a few different data science bootcamps. Many of them gave me a feeling that they want me to be 90% ready for a data scientist role coming in, and they are only willing to do the 10% polishing to get me "sold". NYCDSA convinced me with their road map that they will first focus on teaching the content that they are proud of, then switch gear near the end to the job search part. They shared their online pre-work content with me, so I could get ready. I was impressed by the quality of the recorded lectures and coding platform, which further boosted my faith in the academy.


    Experience at the bootcamp --

    1) The content

    The teaching material is well developed and feels fresh. They keep polishing the core content and introduce many newly developed "jump start" sessions along the way. You are well informed about what's new out there while learning all the fundamentals.

    2) The instructors

    They have a stable teaching team here. Unlike many other camps which keep losing instructors and hiring recently graduated trainees as instructors, NYCDSA has a stable team. The majority of them started working here from years ago when the 12-week bootcamp was initiated.

    They are a knowledgeable, friendly and hardworking group of people, with finance, math, computer science, physics background. When they are not teaching, they either help the students or work together on their side projects. It is smooth to learn from people you respect and admire.

    3) The fellow campers

    A vast majority of the students here have or are working on a graduate STEM degree, with a solid quantitative background. Many also bring in years of experience from finance, health care, software engineering, marketing or other fields. What they all share is a strong will to perform and succeed in data science.

    I feel honored to have worked with a few of them on the group projects. We helped each other not just during the bootcamp, but also during the job search period. I am convinced that it is a great professional and personal network to be in, for the long future after our time at the academy.

    4) The career service

    NYCDSA organizes hiring events for each cohort. You will see quite a few Fortune 500 companies coming to the event, as well as promising start-ups. The NYSDSA career team verify the job vacancies, collect details about the hiring teams, and prepare cohort members individually for a successful outcome (resume, LinkedIn, GitHub, blog posts, interview skills, and many other aspects.) They also utilize their own personal network to get interview opportunities when they see a great match.

    They keep supporting and motivating the students during the course of job search. There are rooms set aside for graduates to come back to and work on things. Here you get daily check-in's from the instructing team and helpful discussion with fellow cohort members. I have been enjoying this cozy and welcoming space often, and plan to keep gaining knowledge and energy from this ideally located data science hub.


    Advice for future students --

    1) Complete the pre-work, have an initial plan for the projects coming in.
    2) Work hard during the bootcamp, be curious and independent. Treat it as a 3-month internship.
    3) Plan to jump right into job hunting effort right after.
    4) When working with wonderful teammates, make sure to deliver your parts; after achieving your goals, remind yourself that you have been kindly helped along the way.


    Closing comments --

    It has been a great investment. With the guidance, help, and support from NYCDSA, my job preparation and search time frame has been shortened by at least 3-6 months. For people with solid STEM background and strong desire to work in Data Science, this bootcamp should be a challenging and rewarding journey. I would continue to cherish the relationship I have built with my mentors and friends met during Cohort 9 at the academy. I wish them well.

  • Great Bootcamp
    - 8/20/2017
    Claire Vignon  User Photo
    Claire Vignon Verified via Linkedin
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    Going to NYC Data Science Academy is a decision I don’t regret for a second. These were ones of the most challenging 3 months but well worth it. I learnt a lot and got a lot of support that I would not have gotten anywhere else.

    As long as you are ready to put in a lot of sweat, hours and effort, you will be successful and do extremely well because you will always get the support of the TAs, staff and fellow students. You are surrounded by a bunch of smart people and TAs who are here to support you and help you grow. 

    The fact that NYC DSA selects students with a Masters or PhD degree is a big plus because you end up working with people from whom you can learn tremendously. Their experience and background make the bootcamp that much more interesting.

    The curriculum is solid and half of it is dedicated to machine learning. Some bootcamps only dedicate a few weeks to machine learning which does not make sense to me given that it is the core of a data science position. The curriculum keeps evolving based on the feedback the students give every week during the pulse check. 

    I believe that you won’t have any difficulty finding a data science position after attending the bootcamp as long as you have the drive and treat the bootcamp and your job hunting as a full time job. 

    Also, NYC DSA offers a lot of help in your job hunting. The last 3 weeks of the bootcamp are dedicated to helping you with your job hunt (don’t worry you’ll still be working on your data science skillset in the meantime with probably the toughest classes of the bootcamp happening at that time too…). You’ll receive a lot of support to find a job from the staff and they will prepare you for interviews.

    All in all, get ready to work hard and if you do, this will be one of the best decisions you will ever make to advance your career in data science

  • YR Gong  User Photo
    YR Gong • Graduate Verified via LinkedIn
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I had been in the 12 week data science bootcamp last year summer, which changed my view to myself about leaning data science. It was a big challenge for me at that time as I had little programming experience. Although the course was extremely intensive, the tutors and TAs were very helpful and encourage us to find a way to achieve the goal. Also, I believe that the course are very comprehansive and good for someone who really wanna find  a related job in Data Science industry. Now I am studying Master of Data science in University of Rochester. My experience in the 3 month bootcamp, defenitely increased my chance to be admiited. I would like to say thanks to all of them who helped me at that time and make me ever stronger. 

  • 100% Recommended!
    - 8/14/2017
    Yvonne  User Photo
    Yvonne • Data Scientist • Graduate Verified via LinkedIn
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    Overall: 

    100% YES!!! I wholeheartedly recommend NYC Data Science Academy! If you want to switch into data science, the bootcamp will help you land your dream job. I got an internship offer shortly after the end of bootcamp(~2.5 weeks) through the bootcamp’s hiring partner event, and recently became a full-time data scientist at the same company. All of this would not have been possible without the help of NYCDSA!

     

    Full Review

    When I was reading through bootcamp reviews, I personally thought it was more helpful to find people of similar background as mine and see how well they fared.  For instance, knowing that people with only bachelors degree attended NYCDSA and got data scientist jobs helped to not only inspire me, but also to set realistic expectations on what type of jobs I could get and how long it could take. So here is a blurb about myself:

     

    TL-DR; 

    • Bachelors degree In math and economics
    • <1 yr of work experience
    • Limited exposure to R/SQL/Big Data tools prior to the bootcamp(Does using a select statement in SQL count?)
    • Prior experience coding in C and basic Python through an intro level computer science course in college. No exposure to data packages in Python. 

     

    Prior to coming into the bootcamp, I asked myself: “Is the bootcamp worth the $16,000 investment?” Is it going to give me enough skills to find a job as a data scientist?”. If you read my introduction, the ultimate answer is an obvious YES! Below, I am listing the top 7 reasons why I think NYDSCA was a worth investment for me:

    1. Top-notch job assistance:  From teaching how to craft your resume to preparing for technical interviews, Vivian and Chris did an excellent job at explaining what interviewing for data scientist positions was like. This “soft skill” portion was really important for me since I wasn’t accustomed to interviewing for technical roles. Thank you Vivian for always pushing me to become a better data scientist! Thank you Chris for all the earnest advices on job hunting!
    2. Connections of the bootcamp: As I mentioned earlier, I got my job through the bootcamp’s hiring partner event. Being able to take advantage of the wide network of employers definitely made “getting the foot in the door” much easier
    3. Learning both R and Python: I wanted to learn both as I knew it would help me cover my bases and be prepared for most data science-related jobs. 
    4. Transforming me into a confident coder: having learned coding through a class in college and a bit of self-learning, I knew I needed to improve my skills if I ever wanted to land a serious job as a data scientist. The pre-work material along with projects were really helpful in that sense
    5. Structured curriculum: There is a lot of thought put into the structure of each class. It was very nice to have all materials that I needed to learn organized for me so that my only worry was to learn.  
    6. Instructors: They deserve their own section as most staff have been teaching for quite a few cohorts. They are all very knowledgeable and approachable. Special shoutout to Zeyu for being an amazing TA and always offering helpful guidance through my projects!
    7. Projects: Each project covers an essential area of data science-data visualization, web scraping, machine learning - and I learned so much through them. The projects were also essential to build my data science portfolio and showcase my skillset to employers.

    If you made it all the way to the end, thank you reading this review! All in all, NYCDSA was great!! It worked perfectly for me as it gave me the skills (both technical and soft) I needed to land a data scientist job.  BE PREPARED TO WORK HARD. Treat both the bootcamp and job hunting as a full-time job and you will be rewarded. :)

  • Best 3 Months Ever
    - 8/10/2017
    Kamal Sandhu  User Photo
    Kamal Sandhu • Graduate Verified via LinkedIn
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I attended the January-March 2017 boot camp of the New York Data Science Academy. It was the most densely packed and learning filled 3 month period of my life. NYCDSA has the right balance of theory and practise built into their curriculum.

    Projects were fun and challenging. Instructors and TA's with expertise in both coding and statistics were available round the clock. I personally asked for assistance on many topics and was more than satisfied with the help. Staff's knowledge about theory and real world applications blew my mind.

    Perhaps, the best part about NYCDSA is working with fellow students who are as passionate, knowledgeable and hard working as you are. Highly recommended.

  • Kyle   User Photo
    Kyle • Data Scientist • Graduate Verified via LinkedIn
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    Honestly one of the best decisions I’ve ever made. Yes it’s a reasonably difficult course, but if you are truly interested in data science you enjoy every second of it. Like anything, you get out what you put in. If you’re ready to work as hard as you need to in order to master this wealth of knowledge in 12 weeks, this course is 100% for you.

    The instructors and TAs are excellent, all accomplished data scientists with a wealth of skill and knowledge. The resources, from slides to code examples and practice questions, are things I will continue to use throughout my career as a data scientist. There is ALWAYS more to learn in the field of data science.

    If you’re thinking about going because you simply want a pay raise, then don’t. The course is relatively difficult, and if you aren’t willing to put in the work to master everything you need to land a job, then you won’t get a job. Simple as that.

    However, if you are committed to becoming an expert data science, the job support here is immense. There are mock interviews, code interview practice questions, linkedin workshops, presentations from hiring companies and data scientists etc…I myself recently accepted a dream offer from a company I was connected with through the bootcamp.

    You likely won’t get a job immediately, it’ll take awhile and a lot of interview practice. It took me about 3 months. If you haven’t mastered the skills you need to be a data scientist, then you don’t have the skills to pass through the interview process. But again, if you are committed there is no shortage of resources made available to you. If you do not succeed here, it is because you did not put as much effort into them as they did into you.

    Finally, an underrated part of the experience is the other students. Some of my best friends in the city I met through the bootcamp, and we still go out for drinks all the time. The course not only provides you with knowledge, but connections. It’s a room full of intelligent, driven and entrepreneurial people. You could expect nothing less.

    If you want to be a data scientist, and more importantly you have the drive to learn and succeed, you’ll thrive here. Simple as that.

  • Life Changed
    - 5/26/2017
    Mayank Shah  User Photo
    Mayank Shah • Data Scientist • Graduate Verified via LinkedIn
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    This course was the best thing to ever happen to me. In 20 weeks (4: pre work, 12: course, 4 job hunt) I went from someone who couldn't write 'Hello World' in python to a full blown Data Scientist, making six figures, with multiple companies vying for my interest. 

    What you should know:

    You will get as much out of this course as you put in. I had many, many days where I was working well past midnight and back in class by 9:30am. You learn how to learn, which is THE skill required for any coding job. The curriculum is intensive, and a lot of times I couldn't totally complete the homework without checking for answers from my peers, and that's okay! In the real world, much of your job will be interacting and working with a team. 

    Course:
    Go every day, work hard, finish the projects on time, and hold yourself accountable. The lecturers do a great job, but ultimately when you're 24+ years old, nobody is going to spoon feed you. The homework is great, but when you try to put everything you've learned together into a well rounded project (there are 4-5 projects), that is when you really understand what is going on. Throw yourself full bore into the projects, and take pride in your work. 90% of what I learned, no exaggeration, was in the 3-5 days before projects were due. Its one thing to figure out homework by looking at the example sets, and a different thing entirely to apply those concepts to a data set with different structure and goals. If you are proud of your projects at the end, you will get a job. Period.

    Job Hunt:

    The job is the ultimate goal for 99% of people entering the camp. Unfortunately, there is some confusion about how the search will work. For one, you will not be "given" a job. For most people, the job search will take 1.5-3 months. Vivian has excellent contacts but she also has 40+ students. In order to guarantee yourself a job, you need to approach the process like a data science project. For me, I did "easy apply"s on LinkedIn, 50 a day. These take literally 15 seconds each. I then selected 15 companies a day with a more formal interview process, and sent them a variation of a pre-written cover letter. For my top picks, I tried to find a hiring manager or data scientist on the team, and add them on LinkedIn. I put my name on AngelList, and got many companies reaching out. I humbled myself and told everyone I was more interested in a great learning position, not a great salary. I iteratively changed my own interview methods, including voice tone, inflections, negotiations, honesty levels, until I found a balance that worked for me. You cannot just apply and hope. That is not a method.

    Basically, the bootcamp is the first big step. The second big step is learning how to apply and interview. Many people send out 5-10 applications to their top picks (who are often everyone else's top picks as well) and then sit on their hands and wonder why they haven't gotten a job. When entering a new field, you have to make concessions about your salary and place of work, in order to reap the rewards down the line. Also, without multiple options, you will not be able to negotiate because you'll feel this is your only chance. BROADEN YOUR HORIZONS!

     

    Overall:

    The camp was the best decision I ever made. I read a book called Design Your Life, which basically said take how you want your life to be, then decide what is necessary to get it there.

    I wanted to live in NYC, with a six figure job, working in an office with low stress, and love what I do. NYCDSA made all of that possible. If you have gotten a degree that isn't taking you where you want to be, but you know you're smart and can work hard, I strongly urge you to apply to NYCDSA today. 

  • Excellent class
    - 1/11/2020
    Kirsten Schulz
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    I took  Dr. Krohn's Deep Learning class with the intention of keeping my self updated with new skills. After a week of class I found myself immersed on Deep Learning reading as much articles as I could on the subject. Dr. Krohn taught the class with a perfect balance between the math and the implementations, ideal for our class which had professionals with different backgrounds. We received a lot of information in each of the five sessions but the pace was OK because the 5 weeks were not exactly in a row. I would recommend this class to any body who wants to get ahead with  new technologies or who is just curious about them. If you decide to take the class, make sure to do the reading assignments and play with the software on your own. For the one optional project, I would recommend to immediately start a project with the material of the second class (our first implementation); so that you can get the help that you need if you get stuck and you can even have a chance to make a second project (maybe in NLP).  This would be specially useful  if you are a software developer and want to try the different implementations.

  • Dylan Dempsey • Head of Revenue Operations • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    Very solid class with an excellent professor Ryan Courtney. We covered all the bases and the professor was very careful to make sure that everyone was being brought along with the course material but still went out of his way to challenge us. Classic socratic method style of pushing the class. Like most courses it still comes down to what you are willing to put in time and effort wise but it was an excellent guided adventure. 

  • Amazing Bootcamp
    - 11/2/2019
    zhuoyi liu • Data science fellow • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I graduated in end of June(Cohort 17). Right now I'm a Data engineer at top tech firm in China. 
    The bootcamp materies encompass big data, machine learning, and data analysis tool such as Python, R, SQL.
     This include all the to-go package to be a Data Scientist. I'm so grateful that I attended this bootcamp. Peoples in the bootcamp are very nice and helpful. 
    This is the big community for every people that want to be a data scientist, come and join us! 

  • Josh • Data Scientist
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    I attended the in person 12 Week Bootcamp during the end of 2017 and I am working as a Data Scientist at a highly recognized tech startup!  For those considering studying data science I highly recommend NYC Data Science Academy.  The main differentiator of NYC Data Science Academy from all the competitors is the curriculum.  The founder herself(Vivian) was one of the first era of data scientists who studies statistics and programming before "data science" got popular.  So the curriculum reflects this in that the bootcamp knows what it takes to succeed in data science in the real world.   The 12 weeks consist of SQL, R, Python, Machine Learning Algorithms, and Bid Data/Deep Learning with the emphasis primarily on practical aspect of these tools.  After the 12 weeks I wasn't a master of all things data science, but the bootcamp really equips you to be able to find your own answers to any data science question (engineering as well!) and make yourself a highly sought after candidate.  

    The only "con" I guess is that you have to work your butt off! You're cramming years worth of an equivalent master's degree into 12 weeks, so you really have to be able to focus and have a passion for data and learning.  But as an encouragement,  all the people that worked hard ALL ended up with a really solid data job that they are proud of. You don't have to take my word for it, check LinkedIn to verify that NYC Data Science Acadey worked for a lot of great students

  • SEAN SW LEE • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    Before I attended NYC data science academy bootcamp, I already had chances to take some short introductory courses in R, Python, and machine learning at other institutes. But even after taking such courses,  my skills  and understanding were very limited, and I actually had difficulties doing researches. This is why I came to attend the NYC data science bootcamp.

    During the bootcamp, we learned data science/machine learning not only in R but also in Python. The codes that I got from lecture codes and  homework are valuable, and I find them  very helpful for conducting my research. In particular, since R and Python have their own highs and lows, I'm able to use both R and Python in complementary ways in my researches, which are actually very good points of the bootcamp program.

    When I conmpare the lecture notes from NYC data science bootcamp with lecture notes obtained elsewhere, I find that the lecture notes from NYC data science bootcamp are well organized to successfully explain fundamental theories.

    I think the teachers here are very knowledgeable, and their lectures are very  efficient. Many of the teachers also shared their experience in the data science industry and gave us ideas about what it is like to work in the data science industry, which was another good point of this program. When students needed helps, TAs, managers, or teachers were available either in person or on slacks for discussions.

    I think this is a highly recommendable program for anyone interested in data science. But, before this program starts, enrolled students are highly recommended to do their best in the preliminary lectures to be better prepared. In addition, students need to work really hard in lectures, homework, and projects.

    I'd highly recommend this program.

  • Paul C • Data Analyst • Student
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:
    N/A

    I recently completed two NYCDSA courses: 'Python Introductory' and 'Python Data Visualization & Analysis' taught by Tony Schultz in July 2019. First, the courses provide a really great foundation to learn python and bulding out your technical skills for applications. Second, Tony is a great instructor and walks you through everything. He takes the time to explain difficult concepts and provides simple explanations to understanding code. Highly recommend to those who are looking to build out their technical skills.

  • A+ Bootcamp
    - 7/22/2019
    David Levy • Data Analyst at FanDuel Sportsbook • Graduate
    Overall Experience:
    Curriculum:
    Instructors:
    Job Assistance:

    Amazing bootcamp and experience! The teachers and TA's are extremely knowledgable and caring- going over and beyond in their support of students. The program is also exceptionally well-managed and upper management genuinely cares about the success of their students; both throughout the bootcamp and after graduation.

    As in most things in life- you will get what you put in. Be prepared to work really hard- you will learn a TON and set yourself up for success in the data science field. You will also build an outstanding network and support system of like-minded, good people!

    HIGHLY recommended!

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

    I took the "Data Science with Python: Data Analysis and Visualization" class taught by Tony Schultz this summer.  The course content is thorough, well formed and covered a lot of areas within the short amount of time. Tony was great with explaining all the concepts. Often times he covered more than just one way to solve a problem and encouraged us to develop our own coding styles. Definitely recommend this  class to all working professionals interested in learning Data Science using Python. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

     

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

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

     

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

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

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

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

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

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

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

Thanks!