1089 Data Analytics
Julian Amon, MSc (WU), PD Mag.Dr. Gertraud Malsiner-Walli, M.Stat.
Weekly hours
Language of instruction
09/05/22 to 10/03/22
Registration via LPIS
Notes to the course
Day Date Time Room
Tuesday 10/11/22 09:00 AM - 12:00 PM D5.0.002
Thursday 10/13/22 04:00 PM - 06:00 PM TC.-1.61
Tuesday 10/18/22 09:00 AM - 12:00 PM TC.3.01
Thursday 10/20/22 04:00 PM - 06:00 PM TC.-1.61
Tuesday 10/25/22 09:00 AM - 12:00 PM TC.3.01
Thursday 10/27/22 04:00 PM - 06:00 PM TC.-1.61
Tuesday 11/08/22 09:00 AM - 12:00 PM TC.3.01
Thursday 11/10/22 04:00 PM - 06:00 PM TC.-1.61
Tuesday 11/15/22 09:00 AM - 12:00 PM TC.3.01
Tuesday 11/22/22 11:30 AM - 01:00 PM TC.1.01 OeNB

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 Regression, Classification and Clustering 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. This regulation holds also for the online modus. At the final presentation of the project results you must be present. 

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: James, G., Witten, D., Hastie, T., & Tibshirani, R.

An Introduction to Statistical Learning with Applications in R

Publisher: Springer
Edition: 1
Remarks: Selected chapters, book available online at
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book
2 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
3 Author: Pang-Ning Tan, Michael Steinbach, Vipin Kumar
Title: Introduction to Data Mining

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

Publisher: Cambridge University Press
Edition: 2nd
Year: 2014
Availability of lecturer(s)

Last edited: 2022-04-20