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.
IK_PCA_tutorial_ACM_DC_2014.pdf