Data Mining with Weka Course - The University of Waikato

Discover practical data mining and learn to mine your own data using the popular Weka workbench.

Learn how to mine your own data

Today’s world generates more data than ever before! Being able to turn it into useful information is a key skill. This course introduces you to practical data mining using the Weka workbench. We’ll dispel the mystery that surrounds the subject. We’ll explain the principles of popular algorithms. We’ll show you how to use them in practical applications. You’ll get plenty of experience actually mining data during the course, and afterwards, you’ll be well equipped to mine your own. Weka originated at the University of Waikato in NZ, and Ian Witten has authored a leading book on data mining.

When would you like to start?

Start straight away and learn at your own pace. If the course hasn’t started yet you’ll see the future date listed below.

• Available now

What software or tools do you need?

You will download the free Weka software during Week 1. It runs on any computer, under Windows, Linux, or Mac. It has been downloaded millions of times and is being used all around the world.

(Note: Depending on your computer and system version, you may need admin access to install Weka.)

Who will you learn with?

Ian Witten

I grew up in Ireland, studied at Cambridge, and taught computer science at the Universities of Essex in England and Calgary in Canada before moving to paradise (aka New Zealand) 25 years ago.

Who developed the course?

The University of Waikato

Sitting among the top 3% of universities worldwide, The University of Waikato prepares students to think critically and to show initiative in their learning.

Learn on FutureLearn

Who is the course for?

This course is aimed at anyone who deals in data. It involves no computer programming, although you need some experience with using computers for everyday tasks. High school maths should be more than enough and you’ll need an understanding of some elementary statistics concepts (means and variances).

What topics will you cover?

• What is data mining?
• Where can it be applied?
• How do simple classification algorithms work?
• What are their strengths and weaknesses?
• In what ways are real-life classification methods more complex?
• How should you evaluate a classifier’s performance?
• What is “overfitting” and how can you combat it?
• How can ensemble techniques combine the result of different algorithms?
• What ethical considerations arise when mining data?

What will you achieve?

By the end of the course, you‘ll be able to:

• Demonstrate use of Weka for key data mining tasks;
• Evaluate the performance of a classifier on new, unseen, instances;
• Explain how data miners can unwittingly overestimate the performance of their system;
• Identify learning methods that are based on different flavours of simplicity;
• Apply many different learning methods to a dataset of your choice;
• Interpret the output produced by classification methods;
• Describe the principles behind many modern machine learning methods;
• Compare the decision boundaries produced by different classification algorithms;
• Debate ethical issues raised by mining personal data.