This fast-paced class is intended for students interested business analytics from a data and algorithmic governance perspective.

The course focuses on gaining the fundamental knowledge necessary to enable fair, transparent, explainable, and accountable data analytics, with a particular emphasis on the academic, industrial and societal relevancy of the corresponding principles, tools, and technologies.

Students will understand the principles, tools, and technologies that are necessary to enable fair, transparent, explainable, and accountable data analytics.

This includes:

• The role of data and algorithmic governance in business analytics
• Findable, Accessible, Interoperable, and Reusable (FAIR) data management principles
• Ownership, control, and access
• Fake news and misinformation
• Bias and fairness
• Transparency, explainability, and accountability

At least 80% attendance.

A combination of academic papers and case studies will be used to demonstrate the academic, industrial and societal relevancy of the course content. The applied project will further reinforce knowledge gained in class by affording participants the opportunity to apply their knowledge by critically analysing existing proposals, by discussing challenges faced in practice and by brainstorming about potential solutions.

Paper review: 25% 

In-class participation: 25%

Project: 50% (including proposal, progress report, presentation, and  final submission)

The participants are expected to have some knowledge of data management and analytics.

During the lecture and based on individual appointments. To request an appointment send an email to the lecturer with the subject “[6120 Data Governance]”.