LFU:online

Ability to independently work out statistical problems based on applied
questions, using statistical software, and communicating the results.

Statistical modeling using supervised and unsupervised learning.

Presentation of lecture slides by lecturer.
Preparation, presentation, and discussion of exercises and empirical case studies by participants.

Online tests (30%), short presentations of empirical case studies (30%), written exam (40%).

see OLAT

DiSC: none

The course gives emphasis to applying statistical methods in practice by using statistical software (either in R or Python, see below) to analyze empirical data. Prior knowledge in statistical methods is not strictly necessary but helpful for making the materials more accessible. Similarly, prior experience with the software environment employed in the corresponding group of the course will greatly facilitate the practical exercises.

Group 0: In the exercises, the statistical software environment R (https://www.R-project.org/) is used for the empirical case studies. Even if prior knowledge in R is not strictly necessary, it is recommended to have some prior experience, e.g., from the DiSC course "Introduction to Programming: Programming in R".

Group 1: In the exercises, the Python programming language (https://www.python.org/) is used for the empirical case studies. Prior knowledge in Python is strongly recommended, e.g., from the DiSC course "Introduction to Programming: Programming in Python".

The acceptance is based on prioritized randomization based on advancement in the Minor "Digital Science" (DiSC). Completion of Module 1: Introduction to Programming (see above) will also be taken into account.

For students in the bachelor program "Management and Economics" ("Wirtschaftswissenschaften"): It is recommended to attend the module "Statistical Data Analysis" ("Statistische Datenanalyse") before this course which covers both the basic statistical methods and an introduction to R. In summer semesters enrollment in Group 0 is thus recommended (as a free elective module). In winter semesters it is better to enroll in the VU 403039 Data Analytics which is, in fact, hosted jointly with Group 0. But it is an elective module with an additional proseminar (PS).