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

**2016 & 2017 Best Data Science Bootcamp** by SwitchUp

**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.

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**.

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.

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.**

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.**

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.**

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.

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

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

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