Data mining is the discovery of patterns and hidden information in large datasets. This course aims at the understanding of the data mining concepts and techniques. The course provides students with the detail about most aspects of data mining and knowledge discovery, focusing on techniques and algorithms in respect to how they are used to solve business problems.

After this course the student will be able to:

1. Understand what is data mining;
2. Differentiate between knowledge discovery in database and data mining;
3. Describe data mining as a process;
4. Explain the CRISP-DM process;
5. Describe the different applications where data mining is used;
6. Understand the different data mining techniques and algorithms;
7. Analyze the performance of data mining techniques and algorithms;
8. Evaluate the mining outcomes;
9. Explain the relationship between data mining and big data [analytics];
10. Understand how to formulate and solve business problems using data mining.

The data mining course will cover a number of topics, including data to be mined and data mining strategies. The techniques will be studied in association with the algorithms needed to implementing them. The course will also rely on business cases. That is, each technique will be studied in association with a business scenario. This will enhance understanding of the techniques and equip the learner with the necessary knowledge and skills required to formulate and solve mining problems.

During the course, students will work on individual task and a group task. For group work, students will collaborate with each other using a variety of collaboration tools. Also, students will be provided access to Rapid Miner, once of the world’s leading mining tools in order to solve business problems and cases.

Teaching is in English and on Internet for distance students or at campus for the students living here. IT support: Learning management system, e-mail and phone.

A learning management system is used for delivering course material, information and submissions. Knowledge is shared and created within the course through virtual meetings with teachers and other students for discussions, supervision, teamwork and seminars. For student on campus there will be meetings on campus.