This lesson provides instruction regarding the various factors one must consider when preprocessing data, preparing it for statistical exploration and analyses. 

This tutorial outlines, step by step, how to perform analysis by group and how to do change-point detection.

This tutorial walks through several common methods for visualizing your data in different ways depending on your data type.

This tutorial illustrates several ways to approach predictive modeling and machine learning with MATLAB.

This brief tutorial goes over how you can easily work with big data as you would with any size of data.

In this tutorial, you will learn how to deploy your models outside of your local MATLAB environment, enabling wider sharing and collaboration.

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

This tutorial provides instruction on how to interact with and leverage Python packages to get the most out of Python's suite of available tools for the manipulation, management, analysis, and visualization of neuroscientific data. 

This tutorial teaches users how to use Pandas objects to help store and manipulate various datasets in Python. 

In this lesson, users can follow along as a spaghetti script written in MATLAB is turned into understandable and reusable code living happily in a powerful GitHub repository.

This lesson gives a quick walkthrough the Tidyverse, an "opinionated" collection of R packages designed for data science, including the use of readr, dplyr, tidyr, and ggplot2.

This lesson gives a general introduction to the essentials of navigating through a Bash terminal environment.  The lesson is based on the Software Carpentries "Introduction to the Shell" and was given in the context of the BrainHack School 2020.

This lesson covers Python applications to data analysis, demonstrating why it has become ubiquitous in data science and neuroscience. The lesson was given in the context of the BrainHack School 2020.

This lecture covers the linking neuronal activity to behavior using AI-based online detection. 

This lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity. 

This lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity. 

This lesson gives an introduction to the central concepts of machine learning, and how they can be applied in Python using the scikit-learn package. 

This lesson provides an overview of self-supervision as it relates to neural data tasks and the Mine Your Own vieW (MYOW) approach.

As a part of NeuroHackademy 2020, Elizabeth DuPre gives a lecture on "Nilearn", a python package that provides flexible statistical and machine-learning tools for brain volumes by leveraging the scikit-learn Python toolbox for multivariate statistics.  This includes predictive modelling, classification, decoding, and connectivity analysis.

This video is courtesy of the University of Washington eScience Institute.

This lesson provides a conceptual overview of the rudiments of machine learning, including its bases in traditional statistics and the types of questions it might be applied to. The lesson was presented in the context of the BrainHack School 2020.