4963 Applications of Data Science
ao.Univ.Prof. Dr. Andreas Mild
Contact details
Weekly hours
Language of instruction
02/02/24 to 02/11/24
Registration via LPIS
Notes to the course
Day Date Time Room
Wednesday 03/13/24 11:30 AM - 03:30 PM D4.0.019
Wednesday 03/20/24 11:30 AM - 03:30 PM D2.0.326
Wednesday 04/10/24 11:30 AM - 03:30 PM TC.3.07
Wednesday 04/17/24 11:30 AM - 03:30 PM D4.0.019
Wednesday 04/24/24 11:45 AM - 03:45 PM D2.0.326
Wednesday 05/08/24 11:00 AM - 02:00 PM TC.3.03

The course consists of several blocks:

In the first block, an introduction into applications of data science in the field of sensor data will be given. We will use data collected by everyday devices like smartphones, but the methods apply to all machine generated data. The following topics will be covered:  Basics of sensory data, Handling noise and missing values, feature engineering, learning based on sensory data.

In the second block, we will look at methods and models to make use of such data.

All practical examples will be done in R language.

Learning outcomes

After completing this course students will have knowledge about different areas of application for data science. Students will have a basic understanding of area-specific challenges and algorithms. Besides an understanding of the problem structure, students will learn to apply mathematical and statistical tools to support decision making. Apart from that, completing this course will contribute to the students’ ability to efficiently work and communicate in a team, work on solutions for complex practical problems by using modern statistical software.

Attendance requirements

The rules on the attendance of a Continuous Assessment Course (PI) apply.

Pursuant to the general guidelines issued by the Vice-Rector for Academic Programs and Student Affairs, the attendance requirement is met if a student is present at least 80% of the time. Students who fail to meet the attendance requirement will be de-registered from the continuous assessment course with a “fail” grade.

Teaching/learning method(s)
The course will combine alternative ways to deliver the different topics to the students. On the one hand, a classical lecture style approach where the instructor presents the software and the content will be used; on the other hand, students will have to solve hands-on problems in class and as homework.

The final grade will be computed on the basis of two assignments and a presentation.


First assignment: 35 %

Second assignment: 35%

Presentation: 30%



1: =>90%

2: =>80%

3: =>70%

4: =>60%


Prerequisites for participation and waiting lists

Successful conclusion of the course 1 of SBWL Data Science.

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.

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Recommended previous knowledge and skills

A working knowledge of the R programming language is expected.

Last edited: 2023-11-15