Our Courses

Building Robust Neuroscience Experiments with Python and PsychoPy

Learn to build modular experiments with Python and PsychoPy using software engineering best practices like automated testing, data validation, and continuous integration.

Crash Course on Python

Compact one-day course covering data analysis with Numpy and Pandas, visualization with Matplotlib, and statistical tests using real neuroscience data.

Essential Computing Tools for Scientists

Tools for reproducible computational research: VSCode and Jupyter for interactive coding, Conda and Pixi for environment management, and Git and GitHub for version control and collaboration.

File and Data Management

Explore database management with SQL, DuckDB, HDF5, and JSON to seamlessly integrate and analyze complex neuroscience datasets.

Intro to Neural Spike Analysis in Python

Analyze neural spiking data with Pandas, Seaborn, and Elephant, from spike sorting with SpikeInterface to advanced statistical inference methods.

Intro to Python for Scientists

Detailed introduction to programming with Python including data analysis with Numpy and Pandas, visualization with Matplotlib, and statistical tests using real neuroscience data.

Introduction to Calcium Imaging Analysis

Analyze calcium imaging data from TIFF stacks to neuronal activity using trace extraction, spike inference, and tools like CaImAn and Suite2P.

LFP Analysis in Python

Introduction to local field potential (LFPs) analysis and signal processing using Numpy, Xarray, Scipy, and specialized tools like Elephant and Neo.

Notebook Driven Development

Learn to create reproducible research with data science notebooks, hvPlot visualizations, and automated multi-notebook pipelines using PyDoIt and Papermill.

Introduction to Arduino

Learn C++ programming with Arduino microcontrollers for neuroscience experiments, from sensors to efficient real-world code and version control.

Neuroscience Data Analysis Pipelines with Python, Git, and Snakemake

Build robust, reproducible analysis pipelines with Snakemake, Conda, and Git for scalable computational neuroscience projects.

Research Data Management with DataLad

Learn to create datasets with version control, track computational provenance and use open science repositories to create more open and reproducible science