Big data offers immense potential for insights and wealth creation, generating vast data volumes. This data is often unstructured and real-time, challenging traditional data management and presenting both challenges and opportunities. Advanced techniques, especially AI, are essential for harnessing data value. This course emphasizes hands-on learning about Big Data and AI, including strategic implications and technological underpinnings with tools like deep learning and AWS. Through practical examples and assignments, it aims to build familiarity with these technologies and foster strategic business thinking. The evolving tech landscape necessitates a flexible learning approach, ensuring manageable student workloads.

Artificial Intelligence (AI), in an unprecedented and transformational manner, has recently opened up new possibilities for firms that significantly affect how humans aided with technology make decisions. This course will focus on understanding AI capabilities (including predictive and generative models), designing effective decision-making frameworks that incorporate AI and humans, building a deep appreciation and understanding of some key issues surrounding AI in organizations, and helping develop governance structures for AI in organizations.

This course provides an introduction to research in the foundational marketing and management topics. Designed for working professionals beginning their journey into research, it covers the fundamental concepts and methodologies used to investigate research questions in marketing and management. Students will learn how to formulate research questions, how to use key frameworks in marketing and management to investigate those research questions, as well as what methods are used in marketing and management research.

Investigating the implications of the major findings of behavioral economics for policymakers. Analysis of the ways in which individuals make decisions that systematically depart from the standard model, which assumes perfect rationality, perfect selfishness, and perfect willpower. Students will develop an understanding of how models are constructed, and how this forms the basis for empirical estimation strategies. We first discuss concepts such as endowment effect, loss aversion, and status-quo bias. Then we introduce uncertainty. The standard model of decision-making under uncertainty is the expected utility model. We then go on to describe some empirical evidence from both economics and psychology to illustrate violations of the expected utility model. Next, we will discuss intertemporal choice. After introducing the standard model of discounting, we will again discuss some observed violations of the standard model in the data.