Advanced Data Mining with Weka

Online Course

Future Learn
Advanced Data Mining with Weka

What is the course about?

Advanced Data Mining with Weka
The course Advanced Data Mining with Weka is an online class provided by The University of Waikato through Future Learn. It may be possible to receive a verified certification or use the course to prepare for a degree.

Learn how to use popular packages that extend Weka’s functionality and areas of application. Use them to mine your own data!

Course description
  • Extend your repertoire of data mining scenarios and techniques
  • 5 weeks
  • This course will bring you to the wizard level of skill in data mining, following on from Data Mining with Weka and More Data Mining with Weka, by showing how to use popular packages that extend Weka’s functionality. You’ll learn about forecasting time series and mining data streams. You’ll connect up the popular R statistical package and learn how to use its extensive visualisation and preprocessing functions from Weka. You’ll script Weka in Python – all from within the friendly Weka interface. And you’ll learn how to distribute data mining jobs over several computers using Apache SPARK.
  • Time series analysis
  • Data stream mining
  • Incremental classifiers
  • Evolving data streams
  • Support vector machines
  • Accessing data mining in R
  • Distributed data mining
  • Map-reduce framework
  • Scripting data mining in Python and Groovy
  • Applications: Soil analysis, Sentiment analysis, Bioinformatics, MRI neuroimaging, Image classification
  • Discuss the use of lagged variables in time series forecasting
  • Explore the use of overlay data in time series forecasting
  • Identify several different applications of data mining with Weka
  • Compare incremental and non-incremental implementations of classifiers
  • Evaluate the performance of classifiers under conditions of concept drift
  • Classify tweets using various techniques
  • Calculate optimal parameter values for non-linear support vector machines
  • Demonstrate the use of R classifiers in Weka
  • Develop R commands and R scripts from Weka
  • Explain how distributed Weka runs Weka on a cluster of machines
  • Experiment with distributed implementations of Weka classifiers and clusterers
  • Explain how “map” and “reduce” tasks are used to distribute Weka
  • Design Python and Groovy scripts for Weka operations
  • Apply Python libraries to produce sophisticated visualizations of Weka output
  • Describe how Weka can be invoked from within a Python environment
  • This course is aimed at anyone who deals in data. You should have completed Data Mining with Weka and More Data Mining with Weka – or be an experienced Weka user. Although the course includes some scripting with Python, you need no prior knowledge of the language. You will have to install and configure some software components; we provide full instructions.
  • The University of Waikato
  • Sitting among the top 3% of universities world-wide, The University of Waikato prepares students to think critically and to show initiative in their learning.

Prerequisites & Facts

Advanced Data Mining with Weka

Course Topic

Data Science, IT & Computer Science, Science, Engineering & Maths

University, College, Institution

The University of Waikato

Course Skill Level

Course Language


Place of class

Online, self-paced (see curriculum for more information)



Degree & Cost

Advanced Data Mining with Weka

To obtain a verified certificate from Future Learn / The University of Waikato you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.
The University of Waikato
Advanced Data Mining with Weka
provided by Future Learn


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Advanced Data Mining with Weka
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School: The University of Waikato
Topic: Data Science, IT & Computer Science, Science, Engineering & Maths