Factor Analysis and PCA are powerful tools, applicable in many common situations in business and data analysis. This course covers both the theory and implementation of factor analysis and PCA, in Excel (using VBA), Python, and R.
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.
An engineer and tinkerer, Vitthal has worked at Google, Credit Suisse, and Flipkart and
studied at Stanford and INSEAD. He has worn many hats, each of which has involved
writing code and building models. He is passionately devoted to his hobby of laughing at
his own jokes.
Hi everyone, my name is Vitthal Srinivasan and welcome to my course “Understanding and Applying Factor Analysis and PCA.”
I am a Co-Founder at a startup named Loonycorn, and I’ve worked at Google, and studied at Stanford.
What predicts success as a sales professional? Is it the number of years of experience selling? The size of your rolodex? Your comfort placing cold calls? All of the above? Or is it some deeper personality trait that explains all of these - like how extroverted you are?
Factor Analysis is a technique that helps cut through the clutter in questions like this one. It helps find hidden, latent underlying factors that drive an outcome. Principal Components Analysis or PCA is one specific technique that helps carry out factor analysis. PCA is powerful and versatile, and it also is an example of a machine-learning-based approach. That’s what this course is all about.
Some of the major topics that we will cover include:
Cutting through the clutter with factor analysis
Carrying out factor analysis using PCA, a powerful ML-based approach
Performing eigenvalue decomposition, a cookie-cutter linear algebra procedure
Implementing PCA to explain Google’s stock returns in Excel and VBA, R, and Python
By the end of this course, you will have a strong applied knowledge of factor analysis and PCA that will help you solve complex business problems. You’ll know how to build robust, well-designed regression models in any one of three powerful tools: Excel, R and Python. You will know how to interpret the outputs of the eigenvalue decomposition technique. You will apply this knowledge to explaining the performance of financial stocks.
Before beginning the course you should be familiar with at least one out of Excel, Python and R.
I hope you’ll join me on this journey to learn how cut through the clutter and choose the right technique with the “Understanding and Applying Factor Analysis and PCA” course, at Pluralsight.