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Cross-listed with DTSA 5504

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 the data mining pipeline and describe how they're related

  • Apply techniques to address challenges in each component of the data mining pipeline.

  • Identify particular challenges presented by each component of the data mining pipeline.

Course Content

Duration: 7 hours

This week provides you with an introduction to the Data Mining Specialization and this course, Data Mining Pipeline. As you begin, you will get introduced to the four views of data mining and the key components in the data mining pipeline. 

Duration: 5.5 hours

This week covers data understanding by identifying key data properties and applying techniques to characterize different datasets. 

Duration: 5.25 hours

This week explains why data preprocessing is needed and what techniques can be used to preprocess data.  

Duration: 5 hours

This week covers the key characteristics of data warehousing and the techniques to support data warehousing. 

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.
  • Page Updates: This page is periodically updated. Course information on the Coursera platform supersedes the information on this page. Click the View on Coursera button above for the most up-to-date information.