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Register for More Data Mining with Weka MOOC

By: , Posted on: April 16, 2014


The University of Waikato is now offering an advanced MOOC on data mining! More Data Mining with Weka will commence in late April and run for five weeks. It follows on from the entry-level MOOC, Data Mining with Weka, which the university is currently running for a second time.

Taught by University of Waikato Department of Computer Science Professor, Ian H. Witten, this new course follows on from Data Mining with Weka and provides a deeper account of data mining tools and techniques.

In this second MOOC — even more than the first — you will do most of your learning in the Activities, and you should allow extra time for them because they’re a bit more challenging than before. Otherwise the format, and time commitment, is the same as the earlier course. Again, you do not have to complete the Activities to get a Statement of Completion: that’s based solely on your performance in the mid-class and end-of-class assessments.


There’s more information about the course in the trailer video: it’s informative, entertaining, and only about 3 minutes long:



The 5-week course will begin on 28 April 2014. Students should have completed Data Mining with Weka, or have equivalent knowledge of the subject.

A detailed syllabus is available. Click here for more information from the Weka course page.

register now

Course features:

Subscribe to the Announcements forum for updates and reminders.

Please read the Terms of Service and Participant Information Sheet before registering.

Course Schedule:

  • Pre-course survey Open
  • Class 1 – Exploring Weka’s interfaces, and working with big data April, 28 2014
  • Class 2 – Discretization and text classification May 5, 2014
  • Mid-course assessment Opens May 9, closes June 8, 2014Data Mining
  • Class 3 – Classification rules, association rules, and clustering May 12, 2014
  • Class 4 – Selecting attributes and counting the cost May 19, 2014
  • Class 5 – Neural networks, learning curves, and performance optimization May 26, 2014
  • Post-course assessment Opens May 28, closes June 8, 2014
  • Post-course survey Opens May 28, closes June 8, 2014

Course Book:

The course will cover chapters contained within Data Mining: Practical Machine Learning Tools and Techniquesco-written by Ian Witten, Eibe Frank and Mark Hall. You can purchase your own copy of the book on the Elsevier Store and save 30% using discount code “STC3014” at checkout.

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Computer Science

Computing functionality is ubiquitous. Today this logic is built into almost any machine you can think of, from home electronics and appliances to motor vehicles, and it governs the infrastructures we depend on daily — telecommunication, public utilities, transportation. Maintaining it all and driving it forward are professionals and researchers in computer science, across disciplines including:

  • Computer Architecture and Computer Organization and Design
  • Data Management, Big Data, Data Warehousing, Data Mining, and Business Intelligence (BI)
  • Human Computer Interaction (HCI), User Experience (UX), User Interface (UI), Interaction Design and Usability
  • Artificial intelligence (AI)
Morgan Kaufmann companion resources can be found here You can also access companion materials and instructor’s resources for all our new books on the Elsevier Store. Search by author, title or ISBN, then look for the “Resources” tab on any book page. Looking for companion materials or instructor’s resources for these titles? Connect below: