Factor Analysis and PCA are key techniques for dimensionality reduction, and latent factor identification. In this course, Understanding and Applying Factor Analysis and PCA, you’ll learn how to understand and apply factor analysis and PCA. First, you’ll explore how to cut through the clutter with factor analysis. Next, you’ll discover how to carry out factor analysis using PCA, a powerful ML-based approach. Then, you’ll learn how to perform eigenvalue decomposition, a cookie-cutter linear algebra procedure. Finally, you’ll learn how to implement PCA to explain Google’s stock returns in Excel and VBA, R, and Python. By the end of this course, you’ll have a strong applied knowledge of factor analysis and PCA that will help you solve complex business problems.
Author Name: Vitthal Srinivasan
Author Description:
Vitthal has spent a lot of his life studying – he holds Masters Degrees in Math and Electrical Engineering from Stanford, an MBA from INSEAD, and a Bachelors Degree in Computer Engineering from Mumbai. He has also spent a lot of his life working – as a derivatives quant at Credit Suisse in New York, then as a quant trader, first with a hedge fund in Greenwich and then on his own, and finally at Google in Singapore and Flipkart in Bangalore. In all these roles, he has written a lot of code, and b… more

Table of Contents

• Course Overview
  1min
• Introducing Factor Analysis and PCA
  38mins
• Understanding Factor Analysis and PCA
  34mins
• Implementing Factor Analysis and PCA in Excel and VBA
  38mins
• Implementing Factor Analysis and PCA in R
  21mins
• Implementing Factor Analysis and PCA in Python
  20mins