Syllabus

Titel
1636 Data Analytics
LV-Leiter/innen
PD Dr. Ronald Hochreiter
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
03.09.2018 bis 30.09.2018
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Termine
Wochentag Datum Uhrzeit Raum
Donnerstag 11.10.2018 16:30 - 19:30 TC.4.12
Dienstag 16.10.2018 16:00 - 19:00 TC.4.13
Donnerstag 18.10.2018 16:00 - 19:00 TC.4.15
Donnerstag 25.10.2018 16:00 - 19:00 TC.4.15
Dienstag 30.10.2018 16:00 - 19:00 TC.4.13
Dienstag 06.11.2018 17:30 - 19:00 TC.4.13
Donnerstag 08.11.2018 16:00 - 19:00 D2.0.342 Teacher Training Raum
Dienstag 13.11.2018 16:00 - 19:00 TC.4.13
Montag 14.01.2019 08:00 - 10:00 TC.4.16

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%)

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

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.

Erreichbarkeit des/der Vortragenden


Zuletzt bearbeitet: 20.06.2018



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