Data Mining Without Coding: Using Rapid Miner in the Context of Education

About

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

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

You will understand the basics of data mining methods and learn how to implement them in a free open-source software. You will learn how to use bottom-up methods to find existing patterns and relationships in datasets, helping you to improve your decision-making skills.

Fee info