About

Neuroscientists increasingly rely on openly-accessible data and on advanced methodological procedures for their investigations. Data science offers a key set of tools and methods to efficiently analyse, visualize, and interpret neuroscience data. Concurrently, there is a growing concern in the life sciences that many results produced are difficult or even impossible to reproduce, commonly referred to as the “reproducibility crisis.”

This course—QLSC 612, Foundations of Neuro Data Science—brings together software and analytical tools and methods in order to teach students how to best use the fundamentals of data science in their daily work to produce reproducible results. We will take examples in neuroimaging and see how to use computational tools, statistical and machine learning techniques, and collaborative, open science methodologies to generate results that are statistically robust and computationally reproducible. While parts of this course are agnostic to specific computer languages, we will teach and use Python throughout much of the course. Students will start devising projects that they will continue during the following weeks of the BrainHack School.

The course assumes that you have basic programming experience and have taken one or more undergraduate course(s) in statistical analysis (or have equivalent experience). It is intended for researchers in the life sciences (neurologists, psychiatrists, pyschologists, neuroscientists) who wish to improve their research practices, or other researchers who want an introduction to data science with examples in neuroscience and neuroimaging.

The first week can be conceptually divided into three sections:

Part I: Introduction and Motivation
Part II: Reproducibility and Data Management Tools
Part III: Data analysis: concept and tools

There will be two brief assessments during the week (on Tuesday and Friday) that will contribute to your grade for the course.

For more information on what we'll cover during the week, please take a look at the schedule.