pb379 - Data Science with Python (Complete module description)

Module label	Data Science with Python
Modulkürzel	pb379
Credit points	6.0 KP
Workload	180 h
Institute directory	Institute for Biology and Environmental Sciences
Verwendbarkeit des Moduls	Area of Specialisation (Bachelor) > Area of Specialisation Bachelor's Programme Biology (Bachelor) > Area of Specialisation Bachelor's Programme Business Administration and Law (Bachelor) > Area of Specialisation Bachelor's Programme Business Informatics (Bachelor) > Area of Specialisation Bachelor's Programme Chemistry (Bachelor) > Area of Specialisation more... Bachelor's Programme Comparative and European Law (Bachelor) > Area of Specialisation Bachelor's Programme Computing Science (Bachelor) > Area of Specialisation Bachelor's Programme Economics and Business Administration (Bachelor) > Area of Specialisation Bachelor's Programme Education (Bachelor) > Area of Specialisation Bachelor's Programme Engineering Physics (Bachelor) > Area of Specialisation Bachelor's Programme Environmental Science (Bachelor) > Area of Specialisation Bachelor's Programme Mathematics (Bachelor) > Area of Specialisation Bachelor's Programme Physics, Engineering and Medicine (Bachelor) > Area of Specialisation Bachelor's Programme Social Studies (Bachelor) > Area of Specialisation Bachelor's Programme Sustainability Economics (Bachelor) > Area of Specialisation Fach-Bachelor Pädagogisches Handeln in der Migrationsgesellschaft (Bachelor) > Area of Specialisation
Zuständige Personen	Winklhofer, Michael (module responsibility)
Prerequisites	Stud.IP Registration
Skills to be acquired in this module	In-depth understanding of programming concepts in python Ability to write effective scripts for data analysis Application of machine learning for predictive modelling and efficient processing of big data Understanding of concepts of numerical mathematics, Application of python to computer simulation of physical problems
Module contents	Programming concepts in python; scientific modules numpy, scipy etc Machine learning: Regression, decision trees, random forests, neuronal networks Analysis of time series data and noise models Elements of numerical mathematics, numerical solution of differential equations
Literaturempfehlungen	J.Grus, Data Science from Scratch – First principles with python. (O’Reilly) A.Geron, Hands on Machine Learning with scikit-learn and tensor flow (O’Reilly) A. Scopatz & K.D. Huff, Effective Computation in Physics – Field guide to research with Python (O’Reilly)
Links	http://scipy-lectures.org/intro/index.html
Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	annually
Module capacity	30
Type of module	Ergänzung/Professionalisierung
Module level	PB (Professionalisierungsbereich / Professionalization)
Teaching/Learning method	Lectures and supervised exercises
Previous knowledge	Basic knowledge in mathematics (e.g., algebra, analysis) and physics

Lehrveranstaltungsform	SWS	Frequency	Workload of compulsory attendance
Lecture	4	WiSe	56
Exercises	3	WiSe	42
Präsenzzeit Modul insgesamt			98 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	Nach Ankündigung	Programming assignments / Fachpraktische Übungen (max. 12 Programmieraufgaben)