neu780 - Biological Data Analysis with Python (Complete module description)

neu780 - Biological Data Analysis with Python (Complete module description)

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Module label Biological Data Analysis with Python
Modulkürzel neu780
Credit points 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
)
Institute directory Department of Neurosciences
Verwendbarkeit des Moduls
  • Master's Programme Biology (Master) > Skills Modules
  • Master's Programme Biology (Master) > Skills Modules
  • Master's Programme Neuroscience (Master) > Skills Modules
Zuständige Personen
  • Winklhofer, Michael (module responsibility)
  • Winklhofer, Michael (Prüfungsberechtigt)
Prerequisites
Skills to be acquired in this module

+ 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 https://www.python.org/.

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.
Module contents
Data types and data structures, control structures, functions, modules, file input/output Standard libraries and SciPy libraries (Matplotlib, NumPy,...), scikit-image, VPython, ...
Literaturempfehlungen
Links
Language of instruction English
Duration (semesters) 1 Semester
Module frequency semester break, annually
Module capacity 20
Reference text
Shared course components with (cannot be credited twice): pb328 "Einführung in Datenanalyse mit Python" (Professionalisierungsmodul im Bachelorstudiengang Biologie)
Type of module Wahlpflicht / Elective
Previous knowledge No prior knowledge in programming required, but useful.
Form of instruction Comment SWS Frequency Workload of compulsory attendance
Lecture 2 WiSe 28
Exercises 2 WiSe 28
Präsenzzeit Modul insgesamt 56 h
Examination Prüfungszeiten Type of examination
Final exam of module
term break, immediately after the course (2 weeks in February)
assignment of programming exercises, 4 out of 5 exercises to be assessed