This class will be held in distance mode only in the winter semester if restrictions still apply.

Therefore, synchronous sessions will be combined with asynchronous ones. For each date, a Q&A session is held introducing the new content and answering questions from the previous ones. Sample codes are provided which then need to be investigated by the students in detail during the self-studying part of the lecture. Attendance during these Q&A is mandatory according to WU rules.

If the class is held in distance mode, the final exam is done orally at the final date of the class. Points and grading do not change compared to the regular format.

The course provides an introduction to data mining and model-driven decision support systems. Concepts, methods and examples are provided with a focus on acquiring hands-on experience with widely used methods, libraries and software systems.

After completing the course, students will know how to handle a number of basic data mining methods and how to provide computer-aided decision support. 

Continuous assessment courses (PI) requiring attendance according to the rule set of the Master’s program (80%).

Each session combines classic lectures with interactive examples, which provides students with an immediate hands-on experience of various methods. Students are required to prepare material for each lecture and complete homework exercises.  Alongside the course, students will work in groups on a data mining project of their choice, for which separate coaching sessions will be offered and which will be presented to class for peer discussion at the end of the course. 

Grading:

Exercises: 30%
Group Project: 30%
Final Exam: 40%

Grading key:

1: >=87.5%
2: >=75% to <87.5%
3: >=62.5% to <75%
4: >=50% to <62.5%
5 (fail): < 50%

christian.fikar@wu.ac.at