This course will provide a broad, deep, but not too technical, understanding of how executives can use data analytics to make better business decisions. The students will learn about all the different elements that it takes to create a data-driven organization to compete in the digital age, including analytics strategy, IT infrastructure, Machine Learning and AI, data governance, and Responsible AI. The course will use a mix of case studies, examples, and analytics design sprints to help students identify business problems that can be addressed with data analytics and create a roadmap for solving them. The course first gives an overview of the basic concepts in data analysis and focuses on how executives can develop strategies using insights from business data, including a discussion of A/B testing and experimentation for innovation. It then introduces students to the topic of investing in digital infrastructures for data analytics and how to generate ROI through examples in exploratory analytics. This is followed by an overview of the key methods in business analytics?descriptive, predictive, causal, and prescriptive methods, as well as an introduction to Generative AI. We conclude the course with a discussion of the need to implement processes to support a data-driven organizational culture, including discussion of data governance, risk management, and responsible AI. Throughout, we focus on basic concepts and the practical use of these methods in management environments. Students will work in small group to ideate and develop business questions that can be addressed using the analytics solutions learned in the course. This course provides the background in data analytics methods that are required for several other courses in the curriculum, such as marketing, operations, and finance.
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