Data Foundations for Machine Learning in R

Before you can build high-performing predictive models, you must understand the raw material that powers them: data. This text-based course guides you through the foundational data concepts, structures, and manipulation techniques required for machine learning using the R programming language. You will transition from simply writing code to deeply understanding how data is organized, cleaned, and prepared for modeling. What you'll learn: Understand core data structures in R including vectors, data frames, and matrices; Clean and preprocess raw data to handle missing values and outliers; Explore standard benchmark datasets used for training and testing machine learning models; Apply split-testing concepts to partition your data into training and validation sets; Use modern tidyverse workflows for efficient data transformation and feature engineering. You will begin by exploring basic terminology and the structural architecture of data in R, followed by step-by-step written tutorials on data manipulation, exploratory data analysis, and preparation techniques for machine learning algorithms. This course is designed specifically for beginners and aspiring data scientists who want a solid, code-first understanding of data preparation in R without any prior machine learning experience. Start building your data preparation skills today.