5568 Data Analytics
PD Dr. Ronald Hochreiter
Contact details
Weekly hours
Language of instruction
02/14/19 to 02/17/19
Registration via LPIS
Notes to the course
Day Date Time Room
Tuesday 03/05/19 03:30 PM - 06:00 PM TC.4.15
Thursday 03/07/19 03:00 PM - 05:30 PM TC.5.16
Tuesday 03/12/19 03:30 PM - 06:00 PM TC.4.15
Thursday 03/14/19 03:00 PM - 05:30 PM TC.5.16
Tuesday 03/19/19 03:30 PM - 06:00 PM TC.4.15
Thursday 03/21/19 03:00 PM - 05:30 PM TC.5.18
Tuesday 03/26/19 03:30 PM - 06:00 PM TC.4.15
Thursday 03/28/19 03:00 PM - 05:30 PM TC.5.16
Tuesday 04/02/19 03:30 PM - 06:00 PM TC.4.15
Thursday 04/04/19 03:00 PM - 05:30 PM TC.5.16


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.

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.

Attendance requirements

You are allowed to skip one unit at maximum.

Teaching/learning method(s)

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. 


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

Prerequisites for participation and waiting lists

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.


1 Author: Pang-Ning Tan, Michael Steinbach, Vipin Kumar
Title: Introduction to Data Mining

Year: 2005
2 Author: Jure Leskovec, Anand Rajaraman, Jeff Ullman
Title: Mining of Massive Datasets

Publisher: Cambridge University Press
Edition: 2nd
Year: 2014
3 Author: Peter Bruce, Andrew Bruce

Practical Statistics for Data Scientists - 50 Essential Concepts

Publisher: O'Reilly Media
Year: 2017
Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book

Availability of lecturer(s)

Last edited: 2018-11-22