Data Mining and Analytics
Data Mining and Analytics introduces students to practical fundamentals of data mining and emerging paradigms of data mining and machine learning with enough theory to aid intuition building. The course is project-oriented, with a project beginning in class every week. The in-class portion of the project is meant to be collaborative and a time for the instructor and GSIs to work closely with project groups to understand the objectives, help work through software logistics, and connect project work to lecture. Lectures will introduce theories, concepts, practical contexts, and algorithms. Students should expect to leave the class with hands-on, contemporary data mining skills they can confidently apply in research and industry.
Topics
- Foster critical thinking about real-world actionability from analytics Develop intuition in various machine learning classification algorithms (e.g. decision trees, feed-forward neural networks, recurrent neural networks, skip-grams) and clustering techniques (e.g. k-means, spectral) Conduct manual feature engineering (from domain knowledge) vs. machine induced featurization (representation learning) Provide an overview of issues in research and practice that will affect the practice of data science in a variety of domains
- Develop capabilities in a range of data mining techniques Use data mining and analytics in a domain of application Think critically about how to assess analytics Gain the ability to solve problems in data mining research and practice
Official catalogue course objectives and student learning outcomes
Sources
- Course catalogue ↗2026-2027