ADA 202: Principles and Techniques of Data Analytics II

Goal: This course builds on Principles and Techniques of Data Analytics I to provide students with a more robust understanding of the tools of a Data Scientist. Data Analytics combines data, computation and inferential thinking to solve challenging problems to thereby better understand the world. This class explores key principles and techniques of data science, including quantitative critical thinking and algorithms for machine learning methods. It will also introduce students to the ways in which data analytics is deployed in healthcare, marketing, political science, criminal justice, and other fields.
Content: By the end of this course students will be able to: Perform feature engineering; Articulate the risks and pitfalls inherent in feature engineering; Understand the basics of how to apply Data Analytics to a wide range of real-world fields; Learn how and when to use a range of regression analysis techniques; Understand how to deploy decision trees; Understand the concepts of Residuals, Multicollinearity, Inference, and Sampling Variability; Demonstrate improved skills in the principles and techniques of data analytics.
Prerequisite(s): ADA 201.
Credit: 3
Degree Level: Undergraduate