Principal Component Analysis with Applications in R and Python

Goals

• Introduce attendees not familiar with Principal Component Analysis (PCA) to the commonly used dimension reduction approach.

• Focus on real data analysis with R and Python.

Prerequisites

• Working knowledge of statistics (e.g. correlation matrices) and linear algebra.

Links

• GitHub repository: GitHub.

• Review of tutorial by Joseph Rickert (of Revolution Analytics) here.

• Video excerpt from the tutorial: YouTube.

• Course slides

Keywords

• R, Python, Principal Component Analysis (PCA), Singular Value Decomposition (SVD), meetup.com, dimension reduction, ACM Data Camp 2014.