Syllabus

Titel
1290 Data Mining and Decision Support Systems
LV-Leiter/innen
PD Dr. Christian Fikar, MSc.
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    3
  • Unterrichtssprache
    Englisch
Anmeldung
02.09.2019 bis 31.10.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Master
Termine
Wochentag Datum Uhrzeit Raum
Mittwoch 06.11.2019 08:30 - 13:00 LC.-1.022 Übungsraum
Mittwoch 13.11.2019 08:30 - 13:00 LC.-1.022 Übungsraum
Mittwoch 20.11.2019 08:30 - 13:00 TC.3.02
Mittwoch 27.11.2019 08:30 - 13:00 TC.3.02
Mittwoch 04.12.2019 08:30 - 13:00 LC.-1.022 Übungsraum
Mittwoch 11.12.2019 08:30 - 13:00 LC.-1.022 Übungsraum
Mittwoch 08.01.2020 08:30 - 13:00 LC.-1.022 Übungsraum
Mittwoch 15.01.2020 09:00 - 11:30 TC.5.15

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%).

Lehr-/Lerndesign

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

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%

Literatur

1 Autor/in: Ledolter, Johannes
Titel:

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

christian.fikar@wu.ac.at

Zuletzt bearbeitet: 25.04.2019



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