MN4128 DATA ANALYTICS AND VISUALIZATION FOR TALENT MANAGEMENT

The Department of Defense routinely evaluates the feasibility and potential consequences of a wide range of talent-management policy proposals. This course prepares students to conduct credible, data-driven policy analysis in this context by developing both technical and communication skills. Students will learn to frame and analyze policy-relevant research questions; merge, manage, and interpret diverse datasets; compute descriptive statistics; estimate foundational econometric models; and design clear, decision-relevant data visualizations. The course also emphasizes effective communication of empirical findings through written products and oral briefings tailored to defense decision makers.

Prerequisite

Course is moved earlier in the MSA curriculum econometric sequence. Students should be familiar with basic statistics (MN3040) only.

Corequisite

Course is moved earlier in the MSA curriculum econometric sequence. Students should take together with MN4110 to concurrently learn econometric modeling of manpower data.

Lecture Hours

Lab Hours

Course Learning Outcomes

· Within a typical military staff setting, the student will create and execute a plan to analyze real world manpower policy problems in order to provide a decision maker evidence with which to make a decision.

· Given a properly coded and cleaned dataset, the student will employ R to estimate basic statistical models for the purpose of manpower policy evaluation.

· Given datasets in a variety of formats, the student will demonstrate the ability to merge, manipulate, and properly code data using R in order to perform appropriate statistical analyses.

· Given a properly coded and cleaned dataset, the student will employ R to generate effective data visualization techniques for the purpose of performing statistical analyses and/or real-time policy evaluation.

· Given a typical real-world policy problem and set of information, the student will generate a quick turn-around manpower policy analysis in R and present managerially relevant results.