Become a Data Scientist in 12 Weeks 


Receive extensive in-person instruction, hands-on project experience, and personalized career support

2016 & 2017 Best Data Science Bootcamp by SwitchUp

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Top-ranked, most comprehensive curriculum

NYC Data Science Academy's 12-week Immersive Bootcamp prepares you with everything you need to land a job as data scientist. Our Bootcamp students receive over 420 hours of education and hands-on practice. You'll learn data science with R, Python, Machine Learning, Hadoop & Spark, Github, and SQL as well as the most popular and useful R and Python packages like XgBoost, dplyr, ggplot2, Pandas, Scikit-learn, and more.

Throughout the program, you'll build at least 4 real-world projects using techniques from visual and statistical analyses to supervised and unsupervised machine learning algorithms and big data technologies. Nothing proves your skills better than having solid projects presented in your portofolio.


July 2nd, 2018

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What will you learn?

Our bootcamp is renowned for the depth and breadth of the curriculum, the richness of the lectures in both programming and statistics, and for its demanding nature. We are the only data science bootcamp that teaches not just Python but also R, Hadoop, and Spark



Unit 1 | Data Science Toolkit

Learn to work from the command line - a must have skill for all data scientists. Work with basic Linux commands, text editing, and Git for version control. MySQL is taught with extensive practice on data manipulation.

Unit 2 | Data Analytics & Visualization with R

Dive deep into R programming language from basic syntax to advanced packages and data visualization (e.g. tidyr, dplyr, string manipulation, ggplot2, R Shiny). Create a data-centric application with interactive visualizations.

Unit 3 | Data Analytics & Visualization with Python

Basic Python programming, followed by versatile packages such as Numpy, Scipy, Matplotlib, Pandas, and Beautifulsoup. Exposure to NoSQL and MongoDB. Complete a Python web scraping project.

Unit 4 | R Machine Learning

Descriptive statistics, hypothesis testing, missingness, imputation & KNN, simple linear regression, multiple linear regression, generalized linear models, PCA, ridge/lasso, trees, random forests, bagging, boosting, support vector machines, neural networks, time series analysis, unsupervised learning. Complete a Kaggle competition.

Unit 5 | Python Machine Learning

Deepen machine learning skills with scikit learn. Focus on data cleaning, feature extraction, natural language processing, modeling and model selection using regression, SVM, PCA, tree models, clustering and more.


Unit 6 | High Performance Computing, Hadoop, & Spark

Learn the concepts of high performance computing with parallel computing skills in Python and R. Introduction to MapReduce, Hadoop, Hive, Spark, and Spark MLlib.

Capstone Project & Job Placement Support

Complete a capstone project. Resume review, tips of interview skills, and opportunities to interview with potential employers.


“NYC Data Science Academy is exactly the accelerator you need to get your career in the data science field off the ground.”

David Steinmetz - Machine Learning Data Engineer @ Capital One 
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