AEM 4600 - Predictive Analytics for Business Strategy

Spring. 3 credits. Student option grading (no audit).

Prerequisite: AEM 2100 or equivalent.

C. Forman.

The purpose of this course is to teach students how data analysis can inform strategy, within a framework centered on logical reasoning and practical communication. In doing so, we will develop the analytical tools and hands-on experience with data and economic models to optimally utilize information in decision-making, often in the context of economic consulting. The focus of the material will be on a subdivision of predictive analytics, called active prediction, which is most appropriate when evaluating business strategies. We will explore the implications and interrelationships between the use of prediction, business processes, and firm strategy. In addition, students will develop presentation and communication skills, particularly with regard to quantitative outputs, and learn valuable, targeted computer programming skills. Finally, students will learn the basics of identification in order to better understand which data is most useful to collect when answering a given empirical question.

Outcome 1: Perform and explain both deductive and inductive reasoning in the context of data analysis.

Outcome 2: Describe the business analytics model for a firm and the analyst’s place in it.

Outcome 3: Be able to distinguish data mining from causal analysis, as well as passive prediction from active prediction.

Outcome 4: Be able to use statistical software to perform data analysis.

Outcome 5: Write clearly and concisely about data analysis utilizing regression techniques to establish causal relationships among strategic variables and outcomes.

Outcome 6: Be able to explain (in a team setting) to a non-technical audience the key components of data analysis establishing causality and its implications.