Introduction to Timeseries Analysis in Python#
Overview#
We are pleased to offer an introductory course for timeseries analysis and data presentation in Python. This course will include:
An overview of python’s scientific libraries (e.g. NumPy. SciPy, Pandas)
Loading and saving timeseries data
Display of raw timeseries data (Matplotlib)
Processing and analysis of timeseries data (Fourier transform, filtering, peak detection)
Display for group analysis results (Seaborn)
The course will start with the analysis of univariate timeseries data (e.g. a patch clamp recording, recording from a single extracellular ephys channel). Time permitting, will then explore processing and of multi-variate data (e.g. a full probe recording).
This is an introductory course that does not require prior signal processing knowledge. The emphasis will be on implementing these processing in code — signal processing ideas will be explored at a high level with little reference to mathematical treatment of the theory, though this can be discussed during the day for those interested.
The only requirement is a working python environment in your coding environment of choice. Please get in contact prior to the course if you don’t have one, we would be happy to help you set this up.