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Course Description
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).
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Principal component analysis (PCA)
FreeAs 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.
The curse of dimensionality50 xpWhy reduce dimensionality?50 xpExploring multivariate data100 xpGetting PCA to work with FactoMineR50 xpPCA with FactoMineR100 xpExploring PCA()100 xpPCA with ade4100 xpInterpreting and visualising PCA models with factoextra50 xpPlotting cos2100 xpPlotting contributions100 xpBiplots and their ellipsoids100 xpProximity of variables in a 2-D PCA plot50 xp - 2
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.
Determining the right number of PCs50 xpThe Kaiser-Guttman rule and the Scree test100 xpParallel Analysis with paran()100 xpAdvanced PCA: performing PCA on datasets with missing values50 xpWhy is mean imputation problematic?50 xpEstimating missing values with missMDA100 xpN-NMF and topic detection with nmf()50 xpTopic detection with N-NMF: Part I100 xpTopic detection with N-NMF: Part II100 xpTrying different N-NMF algorithms100 xp - 3
Exploratory factor analysis (EFA)
Become familiar with exploratory factor analysis (EFA), another dimensionality reduction technique that is a natural extension to PCA.
Intro to EFA and data factorability50 xpEFA's model50 xpThe Humor Styles Questionnaire dataset100 xpIntro to EFA: data factorability50 xpHow Factorable is our Dataset?100 xpMove on or step out of EFA?50 xpExtraction methods50 xpEFA with MinRes and MLE100 xpEFA with Principal Axis Factoring100 xpChoosing the right number of factors50 xpDetermining the number of factors100 xpTaking the actual decision50 xp - 4
Advanced EFA
Round out your mastery of dimensionality reduction in R by extending your knowledge of EFA to cover more advanced applications.
Interpretation of EFA and factor rotation50 xpRotating the extracted factors100 xpWhich rotation method to choose?50 xpInterpretation of EFA and path diagrams50 xpInterpreting humor styles and visual aid100 xpHumor Styles and hidden factors50 xpEFA: Case study50 xpFactorability check100 xpExtracting and choosing the number of factors100 xpFactor rotation and interpretation100 xpCongratulations!50 xpWhen do we use each method?50 xp
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prerequisites
Unsupervised Learning in R
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