Practical Data Science: Reducing High Dimensional Data in R

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Course Description
In this R course, we’ll see how PCA can reduce a 5000+ variable data set down to 10 variables and barely lose accuracy! We’ll look at different ways of measuring PCA’s effectiveness and other ways of reducing wide data sets (those with lots of features/variables). We’ll also look at the advantages and disadvantages with different ways of reducing data.

What are the requirements?

• Some understanding and interest in the R programming language

What am I going to get from this course?

• Understand various ways of reducing wide data sets
• Understand Principal Component Analysis (PCA)
• Control, tune and measure the effects of PCA
• Use GBM modeling to measure the effectiveness of PCA
• Reducing dimensionality with classic GBM & GLMNET Variable Selection
• Use ensembling techniques to find the most stable variables

Who is the target audience?

• Some understanding and interest in the R programming language
• Interest in reducing large data sets