DTSA 5504 Data Mining Pipeline

  • Specialization: Data Mining Foundations and Practice
  • Instructor: Dr. Qin (Christine) Lv, Associate Professor of Computer Science
  • Prior knowledge needed: Basic familiarity with Python, data structure and algorithms

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Learning Outcomes

  • B​y the end of this course, you will be able to identify the key components of the data mining pipeline ​and describe how they're related.
  • ​You will be able to identify particular challenges presented by each component of the data mining pipeline.
  • Y​ou will be able to apply techniques to address challenges in each component of the data mining pipeline.

Course Content

Duration:  5h 18m

This module provides an introduction to data mining and data mining pipeline, including the four views of data mining and the key components in the data mining pipeline. 

Duration: 5h 11m

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

Duration: 5h 17m

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

Duration: 4h 54m

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

Duration: 4h

You will complete a proctored exam worth 20% of your grade made up of multiple choice questions. You must attempt the final in order to earn a grade in the course. 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.

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