Stud.IP Uni Oldenburg
Universität Oldenburg
08.08.2020 23:31:20
neu780 - Introduction to Data Analysis with Python (Vollständige Modulbeschreibung)
Originalfassung Englisch PDF Download
Modulbezeichnung Introduction to Data Analysis with Python
Modulcode neu780
Kreditpunkte 6.0 KP
Workload 180 h
2 SWS Lecture
total workload 90h: 30h contact / 60h individual reading
2 SWS Supervised exercise
total workload 90h: 45h contact / 45h solving programming exercises
Fachbereich/Institut Department für Neurowissenschaften
Verwendet in Studiengängen
  • Master Biologie (Master) > Skills Modules
  • Master Biology (Master) > Skills Modules
  • Master Neuroscience (Master) > Skills Modules

+ Neurosci. knowlg.
++ Maths/Stats/Progr.
+ Data present./disc.

The ojective of the module is the acquistion of programming skills with focus on analysis of neurobiological datasets, using the programming language python. Python is available for any computer platform (PC, Mac, Linux) and is open source (for free), see

Students will learn how to write effective scripts for data processing and visualisation, making use of pre-existing program libraries for various generic purposes (maths, statistics, plotting, image analysis).

Typical applications will be analysis of time series (e.g., electrophysiological recordings, movement data), images (e.g. immunohistochemical images, MRI slices), and spatio-temporal correlations in volume data.
Students will also learn how to produce synthetica data from various noise models to assess signal-to-noise ratio in instrumental datasets.
Data types and data structures, control structures, functions, modules, file input/output

Standard libraries and SciPy libraries (Matplotlib, NumPy,...), scikit-image, VPython, ...
Unterrichtssprache Englisch
Dauer in Semestern 1 Semester
Angebotsrhythmus Modul semester break, annually
Aufnahmekapazität Modul 20
Shared course components with (cannot be credited twice): pb328 "Einführung in Datenanalyse mit Python" (Professionalisierungsmodul im Bachelorstudiengang Biologie)
Modullevel ---
Modulart Wahlpflicht / Elective
Lern-/Lehrform / Type of program
Vorkenntnisse / Previous knowledge No prior knowledge in programming required, but useful.
Lehrveranstaltungsform Kommentar SWS Angebotsrhythmus Workload Präsenzzeit
Vorlesung 2.00 WiSe 28 h
Übung 2.00 WiSe 28 h
Präsenzzeit Modul insgesamt 56 h
Prüfung Prüfungszeiten Prüfungsform
term break, immediately after the course (2 weeks in February)
assignment of programming exercises, 4 out of 5 exercises to be assessed