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.
Course Overview Hello everyone. My name is Vitthal Srinivasan. Welcome to my course Understanding and Applying Factor Analysis and PCA. I am co-founder at a startup named Loonycorn. Prior to this I worked at Google and studied at Stanford. What predicts success as a sales professional? Is it the number of years of experience spent selling? Is the size of your Rolodex? Your comfort placing cold calls maybe? All of the above? Or is it some deeper personality trait that predicts all of these for instance, maybe how extraordinary you are? Factor analysis is a technique that helps cut through the clutter with the questions like this one. It helps to find hidden, latent, underlying factors that drive outcomes and principle component analysis of PCA is one specific technique that helps to carry out factor analysis. PCA is powerful and versatile and is also an example of a machine learning-based approach. Some of the major topics that we will cover include dimensionality reduction using PCA, cutting through the clutter with factor analysis, performing eigenvalue decomposition using Visual Basic for Applications, that's VBA, a technology that's now almost retro chic. By the end of this course you'll be able to implement robust, well-designed regression models in any one of three powerful technologies, Excel, R and Python. I hope you'll join me on this journey to learn how to cut through the clutter and choose the right technique with Understanding and Applying Factor Analysis and PCA at Pluralsight.