All Classes

Full overview of all NYCSDA part-time in-person classes. Launch your career as a data scientist through our cutting edge curriculum, real–world projects, and personalized career support. Pick and choose the best class that fits your schedule. Seats are limited!

Big Data with Amazon Cloud, Hadoop/Spark and Docker

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.

Class Dates
Sep 10, 2019 - Oct 17, 2019 Add to Cart

Introductory Python

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.

Class Dates
Aug 19, 2019 - Sep 16, 2019 Add to Cart
Oct 21, 2019 - Nov 18, 2019 Add to Cart

Data Science with Python: Data Analysis and Visualization

This class is a comprehensive introduction to data science with Python programming language. This class targets people who have some basic knowledge of programming and want to take it to the next level. It introduces how to work with different data structures in Python and covers the most popular data analytics and visualization modules, including numpy, scipy, pandas, matplotlib, and seaborn. We use Ipython notebook to demonstrate the results of codes and change codes interactively throughout the class.

Class Dates
Sep 8, 2019 - Oct 6, 2019 Add to Cart
Oct 27, 2019 - Dec 8, 2019 Add to Cart

Data Science with Python: Machine Learning

This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of 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.

Class Dates
Sep 8, 2019 - Oct 6, 2019 Add to Cart
Oct 27, 2019 - Dec 8, 2019 Add to Cart

Data Science with R: Data Analysis and Visualization

This course is a 35-hour program designed to provide a comprehensive introduction to R. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.

Class Dates
Sep 7, 2019 - Oct 5, 2019 Add to Cart
Oct 26, 2019 - Dec 7, 2019 Add to Cart

Data Science with R: Machine Learning

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 of 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.

Class Dates
Oct 26, 2019 - Dec 7, 2019 Add to Cart