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

This course ist provided within 403.039 VU Data Analytics.

Depending on the group, the module uses either the statistical software environment R (https://www.R-project.org/) or Python (https://www.python.org/) for the empirical case studies. Prior knowledge in R/Python and statistical methods, respectively, is not strictly necessary but helpful for making the materials more accessible.

Students in the bachelor programme "Management and Economics" are recommended to attend the module "Statistical Data Analysis" before. Students in the programme "Digital Science" are recommended to attend the module "Introduction to Programming with R" or "Introduction to Programming with Python", respectively, before.

The acceptance is based on prioritised randomisation. Active students of Complementary Subject Area Digital Science get precedence.