This repository contains materials for the UC Berkeley course Neuroscience 299, Applied Statistics for Neuroscientists.
The course is divided into three parts: setup and review, statistical testing, and statistical modeling. Within a part, materials are organized into folders that correspond to weeks of the semester. These folders contain Jupyter notebooks that serve as tutorial material and labs for the course. Tutorials should be completed before labs.
The course can be completed either totally online or on your own machine. Completing the course online means you don't have to install anything locally, but it means you'll have a harder time saving your work.
Though technically this course does not assume you have any background in computing or Python, it's highly recommended that you get familiar with the basics before starting. I recommend Codecademy's Python course up through section 8.
To run this class locally, i.e. on your own computer, start by downloading the materials. Click the green "Clone or Download" button and choose "Download ZIP". Unzip the resulting file into the location of your choosing.
Follow
the instructions here.
You'll need to install an appropriate computational environment, as described in
the installation instructions.
The notebooks in Part 00 - Setup and Review/00 - Setup will get you acquainted with
the Jupyter notebook format, Python, and the statistical libraries used in this course.
Alternatively, you can run the notebooks in this course on the cloud via the service binder by clicking the badge below.
This will create a Jupyter notebook server on a remote computer, then give you access to it via your web browser. This avoids you having to install anything on your machine. Any changes you make to the notebooks will not be saved, however, so this is better suited for quickly checking out just a single section or reading through the solutions.