Module Number

Module Title

Applied Statistics I
Type of Module

Elective Compulsory
Work load
- Contact time
- Self study
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German
Type of Exam


Lecture type(s) Lecture, Tutorial

Introduction and motivation of basic statistical methods using practical examples from neuroscience, perception research and image processing. The focus lies on the practical application of statistical methods and their implementation in Python. The following topics are covered: Discrete and continuous probability distributions, descriptive statistics (e.g., measures of location, dispersion, and correlation), inductive statistics (e.g., regression, generalized linear model (GLM)), and exploratory statistics are covered. Furthermore, the lecture will cover the introduction and application of probability distributions. Finally, a short introduction into Python and the use of notebooks will given in order to facilitate the use of the required statistical packages.


Students learn basic statistical methods, apply them and implement them in software. They are able to plan and evaluate experiments themselves and to avoid typical errors in experimental design. Furthermore, they can critically evaluated results presented in the literature.

Allocation of credits / grading
Type of Class
Type of Exam
Exam duration
of Module (%)
Prerequisite for participation INFM1010 Mathematics for Computer Science 1: Analysis,

INFM1020 Mathematics for Computer Science 2: Linear Algebra
Lecturer / Other Wannek

Fahrmeir, Künstler, Pigeot, Tutz: Statistik; Springer-Verlag. /
Stahel: Statistische Datenanalyse; Vieweg & Sohn. /
Zusatzliteratur wird in der Vorlesung bekannt gegeben.

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas BIOINFM2510, INFM2510, INFM3110, MDZINFM2510, MEINFM3210