This course introduces students to principles of scientific programming with the Python programming language. Aside from more introductory concepts, more advanced programming concepts and important scientific libraries essential for data analysis and research are introduced.
On completion of the course students should be familiar with the Python programming language and able to solve more 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.
Basic knowledge about programming languages, statistics, and machine learning.
|Forms of teaching and learning||Contact hours||Independent study time|
|Lecture with intergrated exercise||4 SWS||17 SWS|
|Form of assessment||Written exam, between 60 & 90 minutes|
Prof. Dr. Markus Strohmaier
M. Strohmaier & Ivan Smirnov
|Frequency of offering||Spring semester & fall semester|
|Duration of module||1 semester|
|Range of application||M.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Econ., MMDS|
|Preliminary course work||Successful completion of the corresponding exercises|
|Program-specific Competency Goals||CG 2|
|Literature||Beispielsweise: 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.|