Syllabus

Titel
5568 Data Analytics
LV-Leiter/innen
PD Dr. Ronald Hochreiter
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
14.02.2019 bis 17.02.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Termine
Wochentag Datum Uhrzeit Raum
Dienstag 05.03.2019 15:30 - 18:00 TC.4.15
Donnerstag 07.03.2019 15:00 - 17:30 TC.5.16
Dienstag 12.03.2019 15:30 - 18:00 TC.4.15
Donnerstag 14.03.2019 15:00 - 17:30 TC.5.16
Dienstag 19.03.2019 15:30 - 18:00 TC.4.15
Donnerstag 21.03.2019 15:00 - 17:30 TC.5.18
Dienstag 26.03.2019 15:30 - 18:00 TC.4.15
Donnerstag 28.03.2019 15:00 - 17:30 TC.5.16
Dienstag 02.04.2019 15:30 - 18:00 TC.4.15
Donnerstag 04.04.2019 15:00 - 17:30 TC.5.16

Inhalte der LV

One core element of modern Data Science are computational methodologies from the field of Machine Learning as well as Statistical Learning. The main methods will be discussed to allow for handling Classification, Clustering as well as Association Analysis and Collaborative Filtering tasks. Real-life examples and data sets will be used. The statistical programming language R will be used to solve problems numerically.

Lernergebnisse (Learning Outcomes)

Students are able to identify a data science problem and choose the appropriate technology to solve the problem. Furthermore, the students are able to implement the respective algorithms using the statistical programming language R by selecting useful extension packages. Upon completion of the course participants will be able to:

1. Analyze data science problems structurally and find the appropriate method to solve the respective problem.
2. Solve data science problems using R.

Regelung zur Anwesenheit

You are allowed to skip one unit at maximum.

Lehr-/Lerndesign

At the beginning theoretical foundations of Machine Learning technologies will be presented. Furthermore, an introduction to R for Data Science will be given. Over the course of the lecture student presentations will be a central part. 

Leistung(en) für eine Beurteilung

  • Homework (30%)
  • Project (40%)
  • Final Exam (30%)

Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

Please be aware that for all courses in this SBWL registration is only possibly for students who successfully have completed the entry course (Einstieg in die SBWL: Data Science).

Note that for courses within the SBWL "Data Science" we can only accept students enrolled in one of WU's bachelor programmes who qualify for starting an SBWL; particularly, we cannot accept students from other courses and programmes enrolled at WU as 'Mitbeleger' only.

Literatur

1 Autor/in: Pang-Ning Tan, Michael Steinbach, Vipin Kumar
Titel: Introduction to Data Mining

Jahr: 2005
2 Autor/in: Jure Leskovec, Anand Rajaraman, Jeff Ullman
Titel: Mining of Massive Datasets

Verlag: Cambridge University Press
Auflage: 2nd
Jahr: 2014
3 Autor/in: Peter Bruce, Andrew Bruce
Titel:

Practical Statistics for Data Scientists - 50 Essential Concepts


Verlag: O'Reilly Media
Jahr: 2017
Prüfungsstoff: Nein
Diplomprüfungsstoff: Nein
Empfehlung: Stark empfohlen (aber nicht absolute Kaufnotwendigkeit)
Art: Buch

Erreichbarkeit des/der Vortragenden


Zuletzt bearbeitet: 22.11.2018



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