310405 Multivariate Verfahren (V) (WiSe 2024/2025)

Contents, comment

This course gives a relatively superficial overview of various techniques for analysing complex multivariate data. The course content is as follows:

– Introduction
– Regression (in particular shrinkage methods)
– Cluster analysis
– Classification
– Dimensionality reduction

In a sense it's basically an introduction to data science, taking a mostly statistical perspective.

Teaching staff

Dates ( Calendar view )

Frequency Weekday Time Format / Place Period  
weekly Mo 12-14 V2-205 07.10.2024-31.01.2025
not on: 10/7/24 / 12/23/24 / 12/30/24

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Subject assignments

Module Course Requirements  
25-FS-EM Introductory Module Einführungsmodul E2: Einführende Veranstaltung aus den Fakultäten Student information
E3: Einführende Veranstaltung aus den Fakultäten Student information
25-FS-GM Basic Module Grundlagenmodul E2: Einführende Veranstaltung aus den Fakultäten Student information
E3: Einführende Veranstaltung aus den Fakultäten Student information
31-M-El1 Elective Courses 1 Elective Courses 1 Chosen course from the group "Specialisation and deepening of the knowledge on economic theory and/or quantitative methods" 4 LP Student information
31-M-El2 Elective Courses 2 Elective Courses 2 Chosen course from the group quantitative methods 4 LP Student information
Chosen course from the group quantitative methods 4 LP Student information
31-M-ISDA Introduction to Statistical Data Analysis Introduction to Statistical Data Analysis Multivariate Methods Student information
31-M23 Profile Module Statistical Methods Profilmodul Statistische Methoden Multivariate Verfahren Student information
31-SW-StaM Statistical Methods Statistische Methoden Multivariate Verfahren Student information

The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.

Degree programme/academic programme Validity Variant Subdivision Status Semester LP  
Studieren ab 50    
No more requirements
E-Learning Space
E-Learning Space
Moodle Courses
Moodle Courses
Address:
WS2024_310405@ekvv.uni-bielefeld.de
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Notes:
Additional notes on the electronic mailing lists
Last update basic details/teaching staff:
Thursday, May 23, 2024 
Last update times:
Thursday, July 18, 2024 
Last update rooms:
Thursday, July 18, 2024 
Type(s) / SWS (hours per week per semester)
lecture (V) / 2
Language
This lecture is taught in english
Department
Faculty of Business Administration and Economics
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474706234