Data Mining

Course Code :2030TEWMHI
Study domain:Statistics
Academic year:2018-2019
Semester:1st semester
Contact hours:30
Study load (hours):84
Contract restrictions: No contract restriction
Language of instruction:English
Exam period:exam in the 1st semester
Lecturer(s)David Martens

3. Course contents *

In the past decade we’ve witnessed a huge increase in the amount of data being captured and stored. In these large datasets very useful knowledge is present, though often concealed in the vastness of the data. With data mining techniques patterns are automatically revealed from such large datasets.

First, data mining techniques and applications are discussed. Next we will go into popular predictive and descriptive data mining techniques, with applications in marketing and risk management. Also the latest trends in data mining will be looked at, such as social network analysis, text mining and Big Data. Finally, the learned concepts and techniques will be applied and evaluated with a real-life case, in Matlab.