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

Learning Outcomes

  • B鈥媦 the end of this course, you will be able to identify the key components of the data mining pipeline 鈥媋nd describe how they're related.
  • 鈥媃ou will be able to identify particular challenges presented by each component of the data mining pipeline.
  • Y鈥媜u will be able to apply techniques to address challenges in each component of the data mining pipeline.

Course Content

Duration: 听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.听

Duration: 5h 11m

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

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听View on Coursera听button听above for the most up-to-date information.