Exploratory Data Analysis and Feature Profiling in R

Before building any machine learning model, you must deeply understand your data. Raw datasets are often messy, incomplete, and full of anomalies that can ruin your predictive performance if left unaddressed. This text-based course guides you through the essential steps of exploratory data analysis (EDA) and data profiling using the R programming language, helping you transition from looking at raw tables to systematically extracting key statistical insights. What you'll learn: - Understand foundational EDA concepts, statistical summaries, and basic data distributions. - Identify and handle missing values, outliers, and structural errors using modern R packages. - Profile dataset features to evaluate their readiness for machine learning algorithms. - Apply modern tidyverse workflows to clean, transform, and reshape your variables. - Analyze relationships and correlations between features to prevent multicollinearity. You will start with core definitions and basic data structures before progressing through written explanations, code walkthroughs, and practical exercises designed to build your data diagnostics skills. This course is designed for beginner data analysts, aspiring data scientists, and developers looking to establish a strong foundation in data preparation. No prior machine learning experience is required. Start reading today to master the critical first step of the data science lifecycle.