Predictive & Causal Analytics

2.5 credits

This course is fundamentally about how to learn from data. We will demonstrate the stark differences in the goals and implementations of descriptive, predictive, and causal analysis. Students will be expanding their analytic toolkit in each of these areas and learning how to select the right tool for the job. Particular attention will be paid to the usage of these analysis to drive business decisions, and how to respond to data analytics questions in job interviews. This course emphasizes a hands-on approach. We will apply data analysis in six unique cases and four assignments which span a wide variety of topics and feature real world data. The course uses the R programming language and RStudio. While some background in R is helpful, materials are provided for those who are new to the language or need a refresher. The ultimate objective of this class is to prepare you to be a producer and consumer of data analytics, and to enhance your ability to correctly utilize data when making business decisions. Whether working with data directly or communicating with people who do, these skills are essential for the more sophisticated and successful business decision-maker.