GitXplorerGitXplorer
c

AppliedStatisticsForNeuroscience

public
53 stars
18 forks
19 issues

Commits

List of commits on branch master.
Unverified
7d8075013df314244807075a3f827152d1108a5e

to_student for 6d57476

ccharlesfrye committed 6 years ago
Unverified
6d57476168d3f34fbc4d140003e5cf38494e07a4

#69 and #70 for 11

ccharlesfrye committed 6 years ago
Unverified
446f680a3a66eda6d0d825347c9b4bd98931d01a

to_student for 53df26

ccharlesfrye committed 6 years ago
Unverified
7684c4b01ae8fb2500b47dee97f735e5124a25aa

adds back scripts directory to simplify workflow

ccharlesfrye committed 6 years ago
Unverified
53df26d1db6c2e03cbd7197159c13763f09d8e1e

#69 and #70 for 10, cleans up some text and code cells

ccharlesfrye committed 6 years ago
Unverified
48ede0e922d7caf6fa33e1ae9c87d0a8d5602d91

#69 and #70 for 09

ccharlesfrye committed 6 years ago

README

The README file for this repository.

Applied Statistics For Neuroscience

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.

Local Version

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.

Online Version - Binder

Alternatively, you can run the notebooks in this course on the cloud via the service binder by clicking the badge below.

Binder

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.