This course provides a foundational, but not too technical, understanding of how industry leaders can harness the power of data analytics to improve business decisions. We will take a holistic view of what it takes for to create a data-driven organization to compete in the digital age - technology infrastructure, data-informed strategy, data analytics expertise, processes for data governance and analytics project ideation, analytics impact evaluation and continuous improvements, etc. Through a mix of case studies, examples, and design sprints, students will learn to identify business problems that can be addressed with data analytics and create a roadmap for solving them. The course first discusses the topic of investing in digital infrastructures for data analytics and how to generate ROI from these by converting data into insights. It then focuses on how businesses can develop strategies using data insights from AI/ML through case studies and examples. This is followed by an overview of the pillars of analytics ? descriptive, predictive, causal, and prescriptive methods. The next topic focuses on implementing processes needed to support a data-driven organizational culture, including discussion of data governance, risk management, and responsible AI. We then discuss metrics and KPIs needed for measuring the impact of analytics initiatives. The students then learn about analytics project ideation process and apply their knowledge through a design sprint. Through this process, the students identify a business problem and analytics solution needed to address it and prepare a business case for it.
Gopher Grades is maintained by Social Coding with data from Summer 2017 to Spring 2026 provided by the University in response to a public records request
Not affiliated with the University of Minnesota
Privacy Policy