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Department of Astrophysics

Minor in Data Analysis in Natural Sciences

This page describes the structure for students who started this minor before fall 2022 (from fall semester 2022, this minor will be integrated in the Computational Science minor, see here):

Graduates of a minor program in Data Analysis in the Natural Sciences (30 ECTS) can apply modern statistical methods and techniques of data analysis to understand big data in natural sciences. They use these methods to analyze big data and compare and visualize the outcome based on their knowledge of the corresponding natural science area (major at MNF).

Conditions for attending this minor: Most of the modules in the minor subject require knowledge of Linear Algebra and Analysis ('MAT 111 Linear Algebra I' or 'MAT 141 Linear Algebra for the Natural Sciences' or an equivalent module, as well as 'MAT 121 Analysis I' or 'MAT 182 Analysis for the Natural Sciences' or an equivalent module). If this knowledge has not been acquired beforehand, MAT 141 and MAT 182 must be completed at the beginning of the minor. MAT 141 can be credited as an elective module.

 

*Please note: some modules are not offered any more or may not be offered each HS or each FS.

1.) Compulsory modules (Pflichtmodule)

Semester

Module

ECTS

FS

ESC 403 Introduction to Data Science

6

HS

STA 121 Statistical Modelling1

5

1 Requirement: STA 120 or similar

2.) Core elective modules Informatics (Wahlpflichtmodule Informatik)

At least 5 ECTS must be chosen from the following Informatics modules. Modules which build on previously attended and which focus on object-oriented programming and programming of complex algorithms must be chosen.

AINF 1166 is recommended for students with no prior training in programming and with no compulsory programming course within their major.

Semester

Module

ECTS

HS

AINF 02 (AINF 1166) Informatik I

6

HS

MAT 101 Programming

4

HS

MAT 116 Programming MatLab

2

HS

BIO 134 Programming in Biology1

5
FS PHY 124 Scientific Computing 5

FS

PHY 225 Scientific Programming in Python

1

HS (findet nicht statt im HS21)

STA 470 Good Statistical Practice: Computational Skills

2

FS

AINF 06 (AINF 1169) Informatik II

6

FS

ESC 401 High Performance Computing

6

FS

CHE 103 Computer Applications in Chemistry

4

FS

PHY 224 Programming in C++

1

FS

BIO 394 Interdisciplinary Research Methods In Computational Biology2

4

FS

BIO 144 Data analysis in Biology

5

FS*

GEO 876 Introduction to Programming for Spatial Problems3

3

*HS

PHY 114 Scientific Computing I

2

*FS

PHY 125 Scientific Computing II

2

*HS

STA 260 Practical Introduction to the Statistical Computing

Environment R

1

1 Requirement is MAT 183

2 Requirement is BIO 134 or equivalent

3 Requirement: see course catalogue VVZ

3.) Core elective modules Statistics (Wahlpflichtmodule Statistik)

At least 10 ECTS must be chosen from the following:

Semester

Module

ECTS

FS

STA 110 Introduction to Probability

5

FS

STA 120 Introduction to Statistics**

5

HS

PHY 231 Datenanalyse**

3

HS

STA 402 Likelihood Inference1

5

HS

STA 406 Generalized Regression1

5

HS

STA 390 Statistical Practice2

4

FS (irregular)

STA 380 Selected Topics in Statistics

3

* Please note:some modules are not offered any more or may not be offered each HS or FS

** either STA 120 or PHY 231 can be chosen. If PHY 231 has already been completed, STA 120 can not be chosen (and vice versa).

1 Requirements: see course catalogue VVZ

2 Limited number of participants, requires STA 121

4.) Elective modules (Wahlmodule)

For the remaining ECTS: Free choice from compulsory (Pflichtmodule), core elective (Wahlpflichtmodule), and elective (Wahlmodule) modules of the Minor in Computational Science. (Modules from other programs only by agreement.)