neu715 - Neuroscientific Data Analysis in Python

Goals of this module:

upon completion of this module, students…

• understand basic programming concepts.
• have good knowledge about the most important aspects of the programming language Python and are able to write their own programs.
• have basic knowledge in statistical testing.
• have developed and applied programs for the analysis of neuroscientific data.
• have practiced the interpretation of data analysis results in a neuroscience context.
• have learned about and practiced data sharing and version control.

Skills to be acquired/ competencies:

+             Neuroscience knowledge
+             Social skills
++          Maths/Stats/Programming
+             Data presentation/discussion
+             Scientific English
+             Ethics

In each of the seven weeks, one or two specific topics are introduced in the lecture, practiced in the exercises and applied to electrophysiological data in a programming task:

• Python basics: jupyter notebooks; code environments; scripts and functions; loading and saving data; plotting
• Data types: numerical, logical, text, lists, dictionaries, tuples
• Control flow: if statements, loops (for, while)
• Software development: Testing, debugging, version control, sharing code and data, reproducibility
• Working with data: Searching & sorting, logical indexing
• Advanced data structures: Tables; image and video data
• Statistics: random numbers, probability distributions, descriptive statistics, inferential statistics
• Application data analysis: Implementation of spike train analysis methods and graphics, function handles
• Application Modelling: curve fitting, simulation of time series

With completing the seven tasks, each participant programs a set of common analysis methods for neuroscientific data. In addition to writing and commenting code, the programs are applied to experimental data. The tasks include questions about the interpretation of these analysis results.

Hence, the goal of this module is two-fold: Learning the programming language Python and analysis methods for neuroscientific data.