Module Number INF3223 
Module Title Applied Statistics I 
Type of Module Elective Compulsory 

ECTS  6  
Work load  Contact time  Self study 
Workload:
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  Exam 

Lecture type(s)  Lecture, Tutorial  
Content  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. 

Objectives  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
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%) 

Prerequisite for participation 
INFM1010 Mathematics for Computer Science 1: Analysis, INFM1020 Mathematics for Computer Science 2: Linear Algebra 

Lecturer / Other  Wannek  
Literature  Fahrmeir, Künstler, Pigeot, Tutz: Statistik; SpringerVerlag. / 

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