Data Science 221
Modern Data Applications
This is a multidisciplinary graduate course that synthesizes data management, data economy, and machine learning & AI strategy and research, product innovation, business and enterprise technology strategy, industry analysis, organizational decision-making and data-driven leadership into one course offering. The course provides strategic thinking tools, analytical frameworks, and real-world case examples to help students explore and investigate modern data applications and opportunities in multiple domains and industries. Students are required to participate in weekly sessions and write response pieces as well as a final paper and presentation evaluating one defining application or emerging technology in machine learning/AI end-to-end.
Student Learning Outcomes
- Anticipate the opportunities and problems likely to be encountered in building and working with any given data application as business and technology requirements as well as secular trends evolve.
- Create a strategic business case for a new or emerging data application or data science / machine learning use case.
- Develop strategic and business thinking in various data science domains.
- Evaluate data science applications and opportunities across a number of situations and domains.
- Learn a set of qualitative models and analytical frameworks to evaluate any modern data application and emerging trends in machine learning and AI.
- Understand “modern data stacks” and how to manage and use data as an asset in an organization for responsible decision making.