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Keep shoppingConnect the Dots: Factor Analysis
7,700 students
Connect the Dots: Factor Analysis
Factor extraction using PCA in Excel, R and Python
Created byLoony Corn
Last updated 3/2017
English
What you'll learn
- Use Principal Components Analysis to Extract Factors
- Build Regression Models with Principal Components in Excel, R, Python
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Course content
7 sections • 19 lectures • 1h 36m total length
Requirements
- No statistics background required. Everything is built up from basic math
- The models are implemented in Excel, R and Python. Install these environments to follow along with the demos
Description
Factor analysis helps to cut through the clutter when you have a lot of correlated variables to explain a single effect.
This course will help you understand Factor analysis and it’s link to linear regression. See how Principal Components Analysis is a cookie cutter technique to solve factor extraction and how it relates to Machine learning .
What's covered?
Principal Components Analysis
- Understanding principal components
- Eigen values and Eigen vectors
- Eigenvalue decomposition
- Using principal components for dimensionality reduction and exploratory factor analysis.
Implementing PCA in Excel, R and Python
- Apply PCA to explain the returns of a technology stock like Apple
- Find the principal components and use them to build a regression model
Who this course is for:
- Yep! Data analysts who want to move from summarizing data to explaining and prediction
- Yep! Folks aspiring to be data scientists
- Yep! Any business professionals who want to apply Factor analysis and Linear regression to solve relevant problems