• Specialization: Data Mining Foundations and Practice
  • Instructor: Dr. Qin (Christine) Lv, Associate Professor of Computer Science
  • Prior knowledge needed: Concepts of probability, basic algorithms and data structures, and implementation of algorithms in Python

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Learning Outcomes

  • Identify the key components of and propose a real-world data mining project.
  • Design and develop real-world solutions across the full data mining pipeline. 
  • Summarize and present the key findings of the data mining project.
  • Analyze the overall project process and identify possible improvements.  

Course Content

Duration 5h 0m

This module provides a general introduction of data mining project from the architect's perspective, focusing on the the initial brainstorming of project ideas.

Duration 11h 0m

This module discusses in detail what should be included in the project proposal.

Duration 11h 0m

This module focuses on checking the status of the project, identifying the progress so far and any changes to the initial proposal.

Duration 11h 0m

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

Duration 2h 20m

This module contains materials for the final assessment for MS-DS degree students. 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.