The data explosion we are experiencing in every aspect of our lives from social media to smart cars to internet of things requires a deeper look at data analytics. Data analytics is the application of tools and techniques for analyzing raw data to find patterns, develop models and mine actionable insights. Today performing data analysis is both a science and an art. Even though data analytics is highly automated as processes and algorithms, using them appropriately, and making sense of the results is still an art and experience.

This course is focused on an introduction to data analytics followed by hands-on exercises. It is tailored for beginners and researchers who want to learn how to perform data analytics in a visual programming environment. In this course we will specifically concentrate on one aspect of data analytics called supervised predictive learning. Two types of predictive learning will be explored – decision trees and regression. We will use some open-source tools and build data analytics pipelines as part of the exercises.

This course will count as 4.0 CSS short course credit hours.