Module 31-M-ASM1 Advanced Statistical Methods I

Faculty

Person responsible for module

Regular cycle (beginning)

Every winter semester

Credit points and duration

8 Credit points

For information on the duration of the modul, refer to the courses of study in which the module is used.

Competencies

Non-official translation of the module descriptions. Only the German version is legally binding.

Students acquire advanced skills of theory and application of statistical methods in a range of application areas.

The goal of this module is to acquire competences for the specification, estimation and simulation of empirically validated models. The focus of quantitative methods in economics lies on the provision and analysis of data based on an underlying economical research question, where typically the numerical application of methods is in the center of attention. Students learn to use statistical and econometrical models as a method to extract information from the growing amount of data. Hereby a special emphasis is given to acquire competences in the usage of generally applicable modeling paradigms and methods in order to allow for a widespread application to a large number of application areas.

Content of teaching

In this module students acquire advanced competences in the area of statistical modeling including structural issues, scientific tools and methods.

The courses included in this module discuss data analytic and data base oriented methods and models. The courses extend material acquired in introductory courses and offer insights into the areas that also qualify for a deeper understanding of the methods.

Recommended previous knowledge

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Necessary requirements

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Explanation regarding the elements of the module

Two courses on different subject areas must be taken.

Module structure: 1 bPr 1

Courses

Courses in the field of statistics and/or in (a) methodologically related field(s) (I.)
Type lecture o. lecture with exercises
Regular cycle WiSe
Workload5 120 h (30 + 90)
LP 4
Courses in the field of statistics and/or in (a) methodologically related field(s) (II.)
Type lecture o. lecture with exercises
Regular cycle WiSe
Workload5 120 h (30 + 90)
LP 4

Examinations

written examination o. e-oral examination o. oral examination
Allocated examiner Person responsible for module examines or determines examiner
Weighting 1
Workload -
LP2 -

Written examination of 60 to 90 minutes or oral (e-) examination of 15 to 20 minutes.

The module is used in these degree programmes:

Degree programme Profile Recom­mended start 3 Duration Manda­tory option 4
Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] Variante 1 1. one semester Compul­sory optional subject
Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] Variante 2 1. one semester Compul­sory optional subject

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Legend

1
The module structure displays the required number of study requirements and examinations.
2
LP is the short form for credit points.
3
The figures in this column are the specialist semesters in which it is recommended to start the module. Depending on the individual study schedule, entirely different courses of study are possible and advisable.
4
Explanations on mandatory option: "Obligation" means: This module is mandatory for the course of the studies; "Optional obligation" means: This module belongs to a number of modules available for selection under certain circumstances. This is more precisely regulated by the "Subject-related regulations" (see navigation).
5
Workload (contact time + self-study)
SoSe
Summer semester
WiSe
Winter semester
SL
Study requirement
Pr
Examination
bPr
Number of examinations with grades
uPr
Number of examinations without grades
This academic achievement can be reported and recognised.

Sidebar

Elements of the module

Courses

Examinations

Programme of lectures (eKVV)

Programme of lectures (eKVV)

Show lists of modules

Data Science / Master of Science // Variante 1

Data Science / Master of Science // Variante 2