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This tutorial aims at introducing students to the programming language Python for data analysis. By the end of this module, students will be familiar with Python basic syntax and understand why Python serves well the purpose of data analysis.
In this module, you will learn how to package python modules using pypi. This will let you install some of your own code with pip, dealing cleanly with dependencies, as well as share publicly a package.
Learning the basics of machine learning using Jupyter Notebook.
Application of machine learning to fMRI data analysis. In this module, we will go over extracting features (X) and target (y), fitting the model to the data with cross-validation and tweaking our models.
Learning the basics of neuroimaging file format with Nibabel.
Learning the basics of open data and open resource discovery.
Learning about the importance and benefits of project management adhering to community standards to achieve shareable science.
Learning the basics of the DataLad version control system for research data. DataLad is a community project built on top of git and git-annex and a critical tool for reproducible cognitive neuroscience.
Introduction to testing practices for software development and in particular continuous integration, with a guided hands-on example.
Learning the basics of the brain imaging data structure, the pybids interface to interact with a BIDS-compliant dataset as well as the BIDS apps - a collection of software designed to operate on BIDS datasets.
This is the list of training modules for Brainhack School.
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