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DS4VS

repository for course materials: Data Science Methods for Vision Science Applications

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Data Science Methods for Vision Science Applications will provide students with an overview of current data science tools and methodology using datasets from various fields of vision science for context. Data Science is a fast growing inter-disciplinary collection of algorithms, tools and technology used to gain insights from the increasingly large and complex data generated in this digital age. Vision science and visual neuroscience have also seen a rapid increase in the volume of data generated. (e.g. neural recording arrays, imaging data, etc.) This creates a new challenge for today's graduate student: learning the field is complicated by increasingly complex data. The aim of this course is to introduce students to some of the tools and techniques from data science to meet the growing data demands of today's vision science.

Course Outline

Module Objectives
Data Access & Manipulation loading a variety of data into the python environment, numpy arrays & pandas dataframes, and an into to version control with git
Exploratory Data Analysis & Visualization data inspection, data cleaning, summary statistics and basic visualizations with Matplotlib & Seaborn
Probability Basics & Simulating Data random numbers in python, probability review & simulating data
Linear & GLM Regression Methods sklearn linear regression, multiple regression and logistic regression models, evaluation and validation
Machine Learning: Supervised basic supervised learning approaches for numeric data: regression & decision trees and categorical data: classification/categorization (e.g. kNN)
Machine Learning: Unsupervised basic unsupervised learning approaches for numeric data: dimensionality reduction (e.g PCA) and categorical data: clustering (e.g. K-mean)
Data Naratives students will organize a project, prepare a report and give a brief talk to present thier findings

Dreamteam Fall Semester 2021:

Sabina Poudel Akihito Maruya Farzaneh Olianezhad Ashwin

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