Minor in Computational Science (30 or 60 ECTS)
The Minor in Computational Science (30 and 60 ECTS, starting in fall semester 2022) consists of the following topics:
0- Informatics (N0)
1- Data analysis for natural sciences (N1)
2- Simulations in natural sciences (N2)
3- Bioinformatics (N3)
4- Neuroinformatics (N4)
5- Electives (N5)
Overview Minor Computational Science (30 ECTS)
Graduates of the Minor in Computational Science 30 ECTS are able to write computer programs of moderate complexity in a higher programming language, and use these programs, for instance, to analyze data sets and/or to simulate models in the Natural Sciences. One of the following scopes has to be chosen: Data Analysis for Natural Sciences or Simulations in the Natural Sciences.
Students must complete a total of 30 ECTS including:
- 5 ECTS from N0
- either 15† ECTS from N1 (ESC403 must be chosen), or 15† ECTS from N2 (ESC201 must be chosen)
- Additional credits from all blocks N0, N1, N2, N3, N4, N5 such that a total of 30 ECTS is reached.
† Formerly 20 ECTS. Changed to 15 ECTS in 2026.
Overview Minor Computational Science (60 ECTS):
Graduates of the extended Minor in Computational Science 60 ECTS are able to write computer programs of moderate complexity in a higher programming language, and use these programs, for instance, to analyze biological data sets AND to simulate models in the Natural Sciences. Therefore, the minor in Computational Science is designed as a transdisciplinary program. In addition to covering fundamental topics in Applied Mathematics, Statistics and Informatics, students will be given insight into various important fields of application for scientific computing. They may select two field of application from the following list: Data Analysis for Natural Sciences, Simulations in the Natural Sciences, Bioinformatic, Neuroinformatics.
Students must complete a total of 60 ECTS including:
- 10 ECTS from N0
- 15 ECTS from 2 blocks of 4 possible blocks:
- N1 (ESC 403 must be attended if N1 is chosen)
- N2 (ESC 201 must be attended if N2 is chosen)
- N3 (BIO 390 must be attended if N3 is chosen)
- N4 (INI 401 must be attended if N4 is chosen)
- Additional credits from all blocks N0, N1, N2, N3, N4, N5 such that a total of 60 ECTS is reached.
Conditions for attending this (30 or 60 ECTS) minor: Some modules in the minor study program require prior knowledge of linear algebra and calculus (“MAT 111 Linear Algebra I” or “MAT 141 Linear Algebra for the Natural Sciences” or an equivalent module, as well as “MAT 121 Calculus I” or “MAT 182 Calculus for the Natural Sciences” or an equivalent module). Students who do not have this background but wish to take courses in the minor program that have the corresponding mathematical prerequisites must first complete MAT 141 and/or MAT 182 if necessary.
Structure:
*Please note: some modules are not offered any more or may not be offered each HS or each FS.
N0: Core elective modules 'Informatics' (Wahlpflichtmodule Informatik)
'Informatics I' is recommended for students with no prior training in programming or with no compulsory programming course within their major.
|
Semester |
Module |
ECTS |
|
HS |
Informatics I (L+E) (Informatik I) (03SM22AINF02) |
6 |
|
HS |
MAT 101 Programming (07SMMAT101) |
4 |
|
HS |
BIO 134 Programming in Biology (07SMBIO134)1 |
5 |
|
FS |
PHY 124 Scientific Computing (07SMPHY124)2 |
5 |
|
FS |
PHY 225 Scientific Programming in Python (07SMPHY225) |
1 |
|
FS |
Informatics II (V+Ü) (Informatik II) (03SM22AINF06) |
6 |
|
HS |
ESC 401 High Performance Computing (07SMESC401)3 |
6 |
|
FS |
CHE 103 Computer Applications in Chemistry (07SMCHE103) |
4 |
|
FS |
PHY 224 Programming in C++ (07SMPHY224) |
1 |
|
FS |
BIO 144 Data Analysis in Biology (07SMBIO144) |
4 |
|
FS |
PHY 371 Machine Learning for the Sciences (07SMPHY371) |
6 |
1 Requirement: MAT 183
2 PHY 124 may be credited in either block N0 or N1
3 ESC 401 may be credited in either block N0 or N2
N1: Core elective modules 'Data analysis for natural sciences' (Wahlpflichtmodule 'Datenanalyse in den Naturwissenschaften')
|
Semester |
Module |
ECTS |
|
FS |
ESC 403 Introduction to Data Science (07SMESC403) |
6 |
|
FS |
STA 110 Introduction to Probability (07SMSTA110) |
5 |
|
FS |
STA 120 Introduction to Statistics (07SMSTA120)** |
5 |
| FS | PHY 124 Scientific Computing (07SMPHY124)1 | 5 |
| FS | PHY 241 Data Analysis II (07SMPHY241) | 2 |
|
HS |
STA 121 Statistical Modeling (07SMSTA121)2 |
5 |
|
HS |
PHY 231 Data Analysis (07SMPHY231)** |
3 |
| The following courses are accepted but have capacity restriction so you should not depend on being able to enroll. Priority is given to students in the Biostatistics Master and Applied Probability and Statistics. | ||
|
HS |
STA 402 Likelihood Inference (07SMSTA402) [until 2023] |
5 |
| HS | STA 402 Likelihood and Regression I (07SMSTA402a) [from 2024] | 7 |
|
HS |
STA 406 Generalized Regression II (07SMSTA406) [until 2023] |
5 |
| FS | STA 406 Likelihood & Regression II (07SMSTA406) [from 2025] | 4 |
| HS | STA 390 Statistical Practice (07SMSTA390)3 | 4 |
|
HS |
STA 472 Good Statistical Practice (07SMSTA472) |
4 |
1 PHY 124 may be credited in either block N0 or N1
2 Requirement: STA 120 or similar
3 Requirements: STA 121
** 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).
N2: Core elective modules 'Simulations in natural sciences' (Wahlpflichtmodule 'Simulationen in den Naturwissenschaften')
|
Semester |
Module |
ECTS |
|
HS |
ESC 201 Introduction to Computer Simulations I (07SMESC201) |
5 |
|
FS |
ESC 202 Simulations in the Natural Sciences II (07SMESC202) |
5 |
| HS | ESC 401 High Performance Computing (07SMESC401)1 | 6 |
|
HS |
AST 245 Computational Astrophysics |
6 |
|
HS,FS |
MAT 820 Practical training in numerics (07SMMAT820)2 |
3 |
|
FS |
Computer Graphics (L+E) (03SM22BMI007) |
6 |
| FS |
Data Visualization Concepts (L+E) (03SM22BI0008) |
3 |
1 ESC 401 may be credited in either block N0 or N2
2 Limited number of participants
N3: Core elective modules 'Bioinformatics' (Wahlpflichtmodule 'Bioinformatik')
|
Semester |
Module |
ECTS |
|
HS |
BIO 390 Introduction to Bioinformatics (07SMBIO390) |
3 |
|
FS |
BIO 334 Practical Bioinformatics (07SMBIO334)1 |
6 |
|
FS |
BCH 304 Protein Biophysics (07SMBCH304) |
6 |
|
HS |
BIO 445 Quantitative Life Sciences: from Infectious Diseases to Ecosystems (07SMBIO445)1 |
6 |
|
FS |
BIO 392 Bioinformatics of Molecular Sequence Variations (07SMBIO392)1 |
6 |
|
HS |
EEE 326 Principles of Evolution (07SMEEE326)2 | 6 |
1Limited number of participants. Requirement: basic studies in Biology, Biomedicine of Biochemistry completed, including BIO 134 Programming in Biology or similar
2 Limited number of participants.
N4: Core elective modules 'Neuroinformatics' (Wahlpflichtmodule 'Neuroinformatik')
|
Semester |
Module |
ECTS |
|
HS |
INI 401 Introduction to Neuroinformatics (07SMINI401) |
6 |
|
HS |
INI 415 Systems Neuroscience (07SMINI415) |
6 |
|
HS |
INI 404 Neuromorphic Engineering I (07SMINI404) |
6 |
|
HS |
INI 507 Learning in Deep Artificial and Biological Neuronal Networks (07SMINI507) |
4 |
|
FS |
INI 405 Neuromorphic Engineering II (07SMINI405) |
6 |
|
FS |
INI 402 Computational Vision (07SMINI402) |
6 |
|
FS |
INI 427 Models of Computation (07SMINI427) |
6 |
|
FS |
INI 508 Neuromorphic Intelligence (07SMINI508) |
6 |
N5 Elective modules: choice from the following block or all other blocks N0, N1, N2, N3, N4
The following is the list of approved elective modules. In addition, all modules from all blocks (N0, N1, N2, N3, N4) are available for selection. Courses with a substantial computer-based component that are not included in this list may be approved by the program director upon a justified request.
|
Semester |
Module |
ECTS |
|
HS |
STA 404 Clinical Biostatistics1 |
5 |
|
FS |
STA 408 Statistical Methods in Epidemiology2 |
5 |
|
HS |
STA 426 Statistical Methods for the Analysis of Microarray and Short-Read Sequencing Data |
5 |
| FS | ESC 412 Advanced High Performance Computing | 5 |
|
HS |
MAT 141 Lineare Algebra für die Naturwissenschaften |
5 |
|
FS |
03SM22AINF05 Foundations of Computing I (Formale Grundlagen der Informatik I) |
6 |
|
HS |
03SM22BI0001 Foundations of Computing II |
6 |
|
FS |
BINF 2160 Datenbanksysteme |
6 |
|
FS |
BINF 4244 Software Engineering |
3 |
|
HS |
06SM521-001 Einführung in die Computerlinguistik I |
6 |
|
FS |
521005 Einführung in die Computerlinguistik II |
3 |
|
HS |
06SM521-004 Programmiertechniken in der Computerlinguistik |
9 |
|
FS (irregular) |
STA 330 Analysis of longitudinal and spatial data |
5 |
| HS | Software Construction (L+E) (Softwarekonstruktion) (03SM22BI0004) | 6 |
|
HS |
BMINF005 Software Maintenance and Evolution |
3 |
|
HS |
MINF 4557 Advanced Computer Graphics |
6 |
| HS | PHY 252 Computer-assisted Experimentation I | 3 |
| FS | PHY 253 Computer-assisted Experimentation II | 3 |
| FS | CHE 327 Advanced concepts of Physical Chemistry II (07SMCHE327) | 4 |
| FS | BIO 296 Microbial Bioinformatics3 (block course) | 6 |
| HS | BIO 325 Systems Dynamics in Cell and Developmental Biology3 (block course) | 6 |
| FS | BIO 330 Modelling in Biology3 (block course) | 6 |
| FS | BIO 394 Interdisciplinary Research Methods in Computational Biology3 | 4 |
| HS | BIO 444 Quantitative Biosciences | 3 |
| HS | BME 330 Quantitative Biomedicine3 (block course) | 6 |
| HS | BME 338 Introduction to Machine Learning (ML) in Biomedicine | 3 |
| FS | BME 339 Biomedical Informatics | 3 |
| HS | BME 342 Deep Learning in Biomedicine3 (block course) | 6 |
| FS | BME 351 Biomedical Data Mining3 (block course) | 6 |
| HS | EEE 240 The Physics of Life | 3 |
| HS | EEE 338 Evolutionary and Ecological Functional Genomics3 (block course) | 6 |
| HS | Quantitative Methods in Sports (03SM22BO0115) | 3 |
1 Requirement: see course catalogue VVZ
2 Requirement: STA 402 and STA 406
3 Limited number of participants.