Instructors: John Serences and Timothy Sheehan
Time/Location: Weds 9-11:50am, Crick Conference Room (Mandler Hall Rm 3545, on the Muir Campus).
This course will cover basic data analysis methods implemented in python using jupyter notebooks.
We'll start with a basic overview of Git and the python programming language, followed by an intro to various packages/libraries that we'll be using (NumPy, SciPy, etc).
Then we'll learn how to implement some common analysis techniques such as bootstrapping and randomizaton tests, FFT, multivariate decoding, KDE, mutual information and more.
At the end of the course you should have a good working understanding of python, a set of notebooks that cover each of the methods, and a Git repository that you can develop as you apply these methods to your own data in the lab.
Each week class time will be divided into an interactive lecture where we go over a topic and write some code together to demonstrate the functionality of a package (e.g. NumPy) or to learn an analysis technique (e.g. FFT). Then you will have the rest of class to work on a problem set related to the topics covered in the course. Most of these problem sets will be centered on analyzing real timeseries and behavioral data, much like you will encounter in a typical neuro/psych lab.
Tim and I will be in class to help you when you get stuck and hopefully by the end of class you'll have a good understanding of what we covered and the problem set will be completed.
At the end of the quarter, you should have a GitHub repo with all of your work from the quarter. This repo will be your 'final project' and will form the basis for your grade. Our expectation is that you will have all problem sets answered and organized.