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Cross-listed with DTSA 5506

  • 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 key components of and propose a real-world data mining project.

  • Summarize and present the key findings of the data mining project.

  • Design and develop real-world solutions across the full data mining pipeline.

  • Analyze the overall project process and identify possible improvements. 

Course Content

Duration: 6.5 hours

This week provides you with a general introduction of the Data Mining Project course from the architect's perspective, focusing on the initial brainstorming of project ideas which will prepare you for the rest of the course. 

Duration: 4.5 hours

This week discusses in detail what should be included in your project proposal and ends with an opportunity to craft your own. 

Duration: 3.5 hours

This week focuses in on checking the status of your project. After reviewing your project, you will take some time to incorporate the progress you've made with updates to your initial proposal. 

Duration: 5 hours

This week discusses in detail the final project report, highlighting the importance of summarizing the key findings and analyzing the overall project process.

Duration: 1.25 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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