IS 557: Introduction to Scientific Programming with Python
Contents
This course introduces students to principles of scientific programming with the Python programming language. Aside from basic Python syntax, more advanced programming concepts and important scientific libraries essential for data analysis and research are introduced.
Learning outcomes
On completion of the course students should be familiar with the Python programming language and able to solve scientific and complex problems in Python. This covers the application of scientific libraries, some machine learning techniques, and the collection of data with web mining.
Acquired skills:
- Handling of scientific programming projects
- Independent choice of data-structures and methods to solve a given problem
- Knowledge about the different scientific libraries and their advantages
- Data preprocessing, analysis and visualization
Necessary prerequisites
–
Recommended prerequisites
Basic knowledge about programming languages, statistics, and linear algebra.
| Forms of teaching and learning | Contact hours | Independent study time |
|---|---|---|
| Lecture with integrated exercise | 4 SWS | 17 SWS |
| ECTS credits | 6 |
| Graded | yes |
| Workload | 180h |
| Language | English |
| Form of assessment | Written exam (60 min) |
| Restricted admission | yes |
| Further information | https://www.bwl.uni-mannheim.de/en/information-systems/chairs/prof-dr-strohmaier/teaching/ |
Examiner Performing lecturer |  | Prof. Dr. Markus Strohmaier Georg Ahnert |
| Frequency of offering | Fall semester |
| Duration of module | 1 semester |
| Range of application | M.Sc. MMM, M.Sc. WiPäd, M.Sc. VWL, MMDS |
| Preliminary course work | Students have to submit home assignments and collect at least 50% of the available points to be admitted to the exam. |
| Program-specific Competency Goals | CG 2 |
| Literature | For instance: Introduction to Machine Learning with Python: A Guide for Data Scientists, Sarah Guido, O’Reilly |
| Course outline | This course starts with the fundamental syntax and concepts of the Python programming language. Afterwards, we study selected state-of-the-art libraries for scientific applications, including data pre-processing and exploratory analysis. While we provide theoretical background where necessary, we strongly focus on the implementations to solve practical problems. |