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).
Principal component analysis (PCA)Free
As a data scientist, you'll frequently have to deal with messy and high-dimensional datasets. In this chapter, you'll learn how to use Principal Component Analysis (PCA) to effectively reduce the dimensionality of such datasets so that it becomes easier to extract actionable insights from them.
Advanced PCA & Non-negative matrix factorization (NNMF)
Here, you'll build on your knowledge of PCA by tackling more advanced applications, such as dealing with missing data. You'll also become familiar with another essential dimensionality reduction technique called Non-negative matrix factorization (NNMF) and how to use it in R.
Exploratory factor analysis (EFA)
Become familiar with exploratory factor analysis (EFA), another dimensionality reduction technique that is a natural extension to PCA.
Round out your mastery of dimensionality reduction in R by extending your knowledge of EFA to cover more advanced applications.
Assistant Professor, Aristotle University of Thessaloniki
Alexandros is an Assistant Professor of Text and Computational Linguistics at the Aristotle University of Thessaloniki. His main interests include the application of deep learning algorithms for improving our understanding of how a first/second language is processed and acquired by native speakers, data analysis and programming in R.