|Module label||Introduction to Data Analysis with Python|
|Credit points||6.0 KP|
2 SWS Lecture total workload 90h: 30h contact / 60h individual reading 2 SWS Supervised exercise total workload 90h: 45h contact / 45h solving programming exercises)
|Faculty/Institute||Department of Neurosciences|
|Used in course of study||
|Skills to be acquired in this module||
+ Neurosci. knowlg.
+ 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.
Data types and data structures, control structures, functions, modules, file input/output Standard libraries and SciPy libraries (Matplotlib, NumPy,...), scikit-image, VPython, ...
|Language of instruction||English|
|Duration (semesters)||1 Semester|
|Module frequency||semester break, annually|
Shared course components with (cannot be credited twice): pb328 "Einführung in Datenanalyse mit Python" (Professionalisierungsmodul im Bachelorstudiengang Biologie)
|Modulart||Wahlpflicht / Elective|
|Lern-/Lehrform / Type of program|
|Vorkenntnisse / Previous knowledge||No prior knowledge in programming required, but useful.|
|Course type||Comment||SWS||Frequency||Workload attendance|
|Total time of attendance for the module||56 h|
|Examination||Time of examination||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