Introduction to R: Preparing Numeric Data 

Numeric data tends to be cleaner than text data, but there are still a variety of preprocessing steps to consider when working with numeric data to prepare it for analysis and modeling. In this lesson we consider four data preparation steps including: data normalization (centering and scaling), dealing with skewed data, dealing with highly correlated variables and imputing missing values.
This is lesson 15 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below:
Introduction to R: Preparing Numeric Data www.kaggle.com/hamelg/intro-t...
This guide does not assume any prior exposure to R, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn R.
I will create the videos for this guide such that you should be able to learn a lot just watching on RU-vid, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can fork and edit each lesson so that you can follow along and run code yourself.
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Introduction to R Playlist:
• Introduction to R

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