======Data Analysis in Neuroscience 2024======
**Time**: 10am to 12am, Wednesdays\\
**Place**: Room 839, Library, Information and Research Building\\
**Instructor**: Chun-Chung Chen\\
**Office hour**: Friday 10am to 11am by [[mailto:cjj@uw.edu|appointment]]

[[2024dan:content|Schedule & Materials]]
=====Prerequisites=====
Basic calculus, linear algebra, and programming experience will be helpful.
=====Textbook=====
  - //Advanced Data Analysis in Neuroscience: Integrating Statistical and Computational Models//, Durstewitz, 2017. (Main)
    - https://doi.org/10.1007/978-3-319-59976-2
    - https://link.springer.com/book/10.1007%2F978-3-319-59976-2
    - MATLAB code for the book https://github.com/DurstewitzLab/DataAnaBook
  - //Analysis of Neural Data//, Kass, 2014. (Supplementary)
    - https://link.springer.com/book/10.1007%2F978-1-4614-9602-1

This course is intended to provide an overview on the statistical, analytic, and computational tools that are commonly used in the research field of neuroscience. It will focus on characterizing the strength and applicability of various data analytical approaches in some more intuitive than formal ways. Through practical, simplified exercises, it aims to initiate students with tools that are likely to be useful for their future research in the field.

Following the textbook the tentative outline is as following
  * Statistical inference: What is the data telling us?
  * Regression problems: Possible trends of the data.
  * Classification problems: Asking questions and finding answers in the data.
  * Model complexity and selection: Are we reading too much from the data?
  * Clustering and density estimation: Characterize the “shapes” of the data.
  * Dimensionality reduction: Cut out irrelevance, finding the most important.
  * Linear time series analysis: Things that add up to the changing data.
  * Nonlinear concepts in time series analysis: Chemistry between the causes of change.
  * Time Series from a Nonlinear Dynamical Systems Perspective: The ways that moving things can mingle and what we will see as results.

Demonstration and implementation will be done in the Python programming language. Prior experience in Python will be helpful but not required.

=====Homework=====
Homework will generally be assigned at the conclusion of each textbook chapter. You will have a week's time to complete your homework. Each homework should be submitted in [[notes:ipynb2pdf|a single PDF file]] through the [[https://e3.nycu.edu.tw/|E3 digital learning platform (E3 數位教學平台)]].