1147 Data Mining and Decision Support Systems
PD Dr. Christian Fikar, MSc.
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
01.09.2020 bis 12.11.2020
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Master
Wochentag Datum Uhrzeit Raum
Mittwoch 18.11.2020 09:00 - 14:00 Online-Einheit
Mittwoch 25.11.2020 09:00 - 14:00 Online-Einheit
Mittwoch 02.12.2020 09:00 - 14:00 Online-Einheit
Mittwoch 09.12.2020 09:00 - 14:00 Online-Einheit
Mittwoch 16.12.2020 09:00 - 14:00 Online-Einheit
Mittwoch 13.01.2021 09:00 - 14:00 Online-Einheit
Mittwoch 20.01.2021 09:00 - 14:00 Online-Einheit

Ablauf der LV bei eingeschränktem Campusbetrieb

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.

Inhalte der LV

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.

Lernergebnisse (Learning Outcomes)

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. 

Regelung zur Anwesenheit

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. 

Leistung(en) für eine Beurteilung


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%


1 Autor/in: Ledolter, Johannes

Data Mining and Business Analytics with R

Verlag: Wiley
Jahr: 2013
Prüfungsstoff: Nein
Diplomprüfungsstoff: Nein
Empfehlung: Referenzliteratur
Art: Buch

Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

Basic programming skills, statistics and linear algebra.

Erreichbarkeit des/der Vortragenden

! Important information !

The Covid19 pandemic causes several uncertainties for the upcoming winter semester starting on October 5th. If possible within the valid health regulations, the Information Systems program plans to fully return to the usual mode of classroom teaching with mandatory attendance. Nonetheless, an additional “plan B”-scenario for distance and/or hybrid teaching will be prepared for every class, which can be enabled if needed.

The program management will reevaluate the situation in September and decides then whether the classes will indeed start in the usual mode or in an alternative scenario. This decision will be announced in the syllabus until September 25 [for the classes starting in the mid of the semester until November 13].

We’re aware that the issue of visa is currently delayed and we’ll include this matter into our considerations. Should you be affected by such a delay, please inform us as soon as possible via

Zuletzt bearbeitet: 04.09.2020