Prepares students to apply economic analysis to public policy problems. Topics include externalities, industry regulation, and policy problems of allocating resources over time. Examples will show a broad range of substantive applications to domestic and international policymaking.

Builds on the methods and concepts introduced into Research Methods and Data Analysis I to prepare students to gather, analyze, criticize, and interpret complex data in public policy settings. Tools stressed may include multiple regression and experimentation.

This experiential course pairs students from the Schools of Law, Engineering, and Public Policy with U.S. Department of Defense (DoD) problem sponsors to address real-world national security challenges. Through student research, stakeholder interviews, and engagements with outside experts, each team develops policy recommendations for its sponsor's organization. Recent sponsors include the National Security Agency, U.S. Strategic Command, and the U.S. Air Force's Office of Operational Energy.

Learning tools and concepts for computing on big data. Learn how to use Spark for large-scale analytics and machine learning. Spark is an open-source, general-purpose computing framework that is scalable and blazingly fast. Fundamental data types and concepts will be covered (e.g., resilient distributed datasets, DataFrames) along with Tools for data processing, storage, and retrieval, including Amazon Web Services (AWS).

This course is an introduction to consumer, product, and market analytics. You will learn key concepts in brand positioning, customer segmentation, and consumer behavior. The course will expose students to basic market research methods and measurement considerations in designing and conducting market research. The course will also cover basic statistics techniques using SPSS and R-Studio.

Business Analytics is useful to the extent that it impacts decision making in an enterprise. Critical to having impact is a thorough understanding of its strategy. This course develops frameworks and tools to analyze the strategy of an organization and develops the skills to evaluate decisions inside an organization. This course will connect your analytical skills with the ability to influence the enterprise as it executes its strategy.

In this course, we will explore approaches for structuring, quantifying, and analyzing common business decisions. We will also explore the various uses of data and statistical inference to drive better decision making. This course aims, in part, to improve your analytical skills by gaining insight into risk and uncertainty. Tools and techniques to support this objective will include decision trees, simulation, hypothesis testing, and regression.

You will be introduced to leading analytical techniques in marketing and how marketers use marketing data to design cutting-edge marketing strategies. We will use data analysis to support marketing decisions and explore techniques for product line pricing, demand forecasting, customer segmentation, and customer lifetime valuation. We will use a hands-on approach, and the topics covered in this course are relevant to current marketing practice.

In this course, students will gain exposure to and practice with the concepts and tools used to leverage data at scale and create value. The concepts and tools covered include data visualization, machine learning, and cloud computing. Through materials designed for the novice, students will be introduced to coding in Python and learn to develop predictive models from large datasets. Students will also learn data visualization in Tableau.

Data visualizations weave information into insights that add business-relevant value. Visual storytelling creates compelling arguments that inspire people to act. Analyzing data sets, finding insights, and turning insights into persuasive storytelling are critical to persuading audiences. We will explore constructing data visualizations using storytelling, visuals, key language, callouts, BLUFs, and other related data visualization elements.