Data Visualisation and Analysis Lab.
|Type||Practical course (basic level)|
This course provides participants with a comprehensive and versatile toolbox of data visualisation and analysis methods, which can be transferred to a vast number of applications.
How much impact did a marketing campaign have on our product's revenue? Did the football championship influence peoples' search behaviour? How can we increase player retention in our online game? Has our new user interface design really helped our customers? And what type of products are the customers of our online shop likely to be interested in?
As more and more processes move to the digital world, data on numerous aspects of our daily lives becomes increasingly more abundant. The ability to make sense of this data and to successfully employ it to understand and improve services is becoming a relevant key skill to have in most industries.
This course provides participants with a comprehensive and versatile toolbox of data visualisation and analysis methods, which can be transferred to a vast number of applications. The seminar consists of 20% lectures, which provide a theoretical background, and 80% applied exercises, in which participants apply this knowledge using real-world data sets.
The lectures in the first part provide a comprehensive introduction into descriptive statistics and demonstrate how to approach the analysis of complex data sets using the free and versatile software package R. The seminar participants apply the methods shown in the lecture to analyse, visualise and discuss a variety of interesting real world datasets.
In the second part of the seminar participants analyse data sets of their own choice. Students organise into small groups and develop different visualisation methods. The lecturer provides assistance and more in-depth techniques as required.
- The course takes place on Fridays.
- SWS: 4+1
- Number of ECTS Credits: 10
- Speech: Englisch
- Location: Fraunhofer Institute for Applied Information Technology, Schloss Birlinghoven, 53754 Sankt Augustin
- Bachelor degree.
- Interest in descriptive statistics and visualisation. Prior knowledge of statistics is not required.
- Creativity and teamwork will prove useful.
- Basic programming experience is recommended.