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Cross-listed with DTSA 5505

Course Type: Computer Science Elective

Specialization: Data Mining Foundations and Practice

Instructor: Dr. Qin (Christine) Lv, Associate Professor of Computer Science

Prior knowledge needed: TBD

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Learning Outcomes

  • Identify the core functionalities of data modeling in the data mining pipeline.

  • Apply techniques that can be used to accomplish the core functionalities of data modeling and explain how they work.

  • Evaluate data modeling techniques, determine which is most suitable for a particular task, and identify potential improvements.

Course Content

Duration: 8 hours

This week starts with an overview of this course, Data Mining Methods, then focuses on frequent pattern analysis, including the Apriori algorithm and FP-growth algorithm for frequent itemset mining, as well as association rules and correlation analysis. 

Duration: 6 hours

This week introduces supervised learning, classification, prediction, and covers several core classification methods including decision tree induction, Bayesian classification, support vector machines, neural networks, and ensemble methods. It also discusses classification model evaluation and comparison. 

Duration: 6 hours

This week introduces you to unsupervised learning, clustering, and covers several core clustering methods including partitioning, hierarchical, grid-based, density-based, and probabilistic clustering. Advanced topics for high-dimensional clustering, bi-clustering, graph clustering, and constraint-based clustering are also discussed. 

Duration: 5 hours

This week discusses three different types of outliers (global, contextual, and collective) and how different methods may be used to identify and analyze such outliers. It also covers some advanced methods for mining complex data, as well as the research frontiers of the data mining field. 

Duration: 1.75 hours

This module contains materials for the final exam. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the introductory module and anywhere else they may be found.


  • Cross-listed Courses: Courses that are offered under two or more programs. Considered equivalent when evaluating progress toward degree requirements. You may not earn credit for more than one version of a cross-listed course.
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