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 more introductory concepts, 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 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.
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 machine learning.

Forms of teaching and learning	Contact hours	Independent study time
Lecture with intergrated exercise	4 SWS	17 SWS

ECTS credits	6
Graded	yes
Workload	180h
Language	English
Form of assessment	Written exam, between 60 & 90 minutes
Restricted admission	yes
Further information	https://www.bwl.uni-mannheim.de/strohmaier/teaching

Examiner Performing lecturer

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. WiPäd, M.Sc. VWL, 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.