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.

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.

This is a course in basic statistics. Statistical methodologies are increasingly used in various disciplines, e.g., data science, applied statistics, business, and computational social science. The main goal of this course is to provide students with an introduction to different types of quantitative research methods and statistical techniques. This course primarily covers two components: 1) methods for quantitative research, and 2) quantitative statistical methods for data analysis. With these, we hope you will obtain knowledge necessary to do high quality scholarly research and more efficient and effective practice in your daily tasks. In particular, it involves learning statistical inference under which you should be able to digest publications about new methods, implement these methods, apply them to your data, interpret results, and explain them to others. In addition, we also expect you to know how to write and publish your novel work in some journals.