This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below. 

This lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling. 

This lecture covers the three big questions: What is the universe?, what is life?, and what is consciousness?

This lecture outlines various approaches to studying Mind, Brain, and Behavior. 

This lecture covers the history of behaviorism and the ultimate challenge to behaviorism. 

This lecture covers various learning theories.

Introduction of the Foundations of Machine Learning in Python course - Day 01.

High-Performance Computing and Analytics Lab, University of Bonn

In this lesson, you will learn about how genetics can contribute to our understanding of psychiatric phenotypes.

This lesson gives an introductory presentation on how data science can help with scientific reproducibility.

This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.

This lesson gives an introduction to high-performance computing with the Compute Canada network, first providing an overview of use cases for HPC and then a hands-on tutorial. Though some examples might seem specific to the Calcul Québec, all computing clusters in the Compute Canada network share the same software modules and environments.