Data Mining Without Coding: Using RapidMiner in the Context of Education

This course is designed to give an overview of applying data mining in an educational context to students without a computer science or programming background. Some examples of the data mining methods that will be learnt include supervised machine learning algorithms like Decision Trees, Rule Induction, Support Vector Machines, and Artificial Neural Networks. In addition to that, unsupervised learning methods like clustering and association rule mining will be practised.

Course leader

Danial Hooshyar, Associate Professor of Educational Data Mining

Target group

Anyone interested in understanding the basics of data mining. Anyone interested in mining data using machine learning algorithms.
Anyone interested in learning RapidMiner.

Course aim

By the end of this course, the learners will have more knowledge of different data mining techniques and should be able to employ these for data-driven decision-making in their area of interest.