Develop your intuition for when to reduce dimensionality in your data, and master the fundamentals of how to do so in R.
Real-world datasets often include values for dozens, hundreds, or even thousands of variables. Our minds cannot efficiently process such high-dimensional datasets to come up with useful, actionable insights. How do you deal with these multi-dimensional swarms of data points? How do you uncover and visualize hidden patterns in the data? In this course, you'll learn how to answer these questions by mastering three fundamental dimensionality reduction techniques - Principal component analysis (PCA), non-negative matrix factorisation (NNMF), and exploratory factor analysis (EFA).
Real-world datasets often include values for dozens, hundreds, or even thousands of variables. Our minds cannot efficiently process such high-dimensional datasets to come up with useful, actionable insights. How do you deal with these multi-dimensional swarms of data points? How do you uncover and visualize hidden patterns in the data? In this course, you'll learn how to answer these questions by mastering three fundamental dimensionality reduction techniques - Principal component analysis (PCA), non-negative matrix factorisation (NNMF), and exploratory factor analysis (EFA).
