Understanding and Applying Factor Analysis and PCA
Pluralsight
Course Summary
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.
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Course Description
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.
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Course Syllabus
Course Overview- 1m 56s
—Course Overview 1m 56sIntroducing Factor Analysis and PCA- 38m 16s
—Cutting Through Clutter with Factor Analysis 4m 36s
—Linear Regression and Factor Analysis 2m 55s
—What and How: Factor Analysis and PCA 4m 36s
—Two Approaches to Factor Extraction 5m 27s
—Mean and Variance 5m 22s
—Covariance and Correlation 5m 41s
—Random Variables and Matrix Operations 4m 34s
—The Intuition Behind PCA 5m 3sUnderstanding Factor Analysis and PCA- 34m 2sImplementing Factor Analysis and PCA in Excel and VBA- 38m 32sImplementing Factor Analysis and PCA in R- 21m 53sImplementing Factor Analysis and PCA in Python- 20m 29s
