Data Science in R: Practical Capstone Project

Transitioning from learning individual R commands to executing a full-scale data project can be a major hurdle. This course bridges that gap by guiding you through a realistic data science scenario from start to finish. You will start by establishing the core terminology of the data lifecycle before diving into hands-on implementation. By reading step-by-step explanations and studying clear code snippets, you will learn how to gather, tidy, analyze, and model data using modern R conventions. What you'll learn: - Configure a structured data science workflow using modern R libraries - Clean and transform raw data using tidyverse principles for efficient analysis - Conduct exploratory data analysis and basic hypothesis testing to find patterns - Build and evaluate regression and classification models to make predictions - Structure your analysis into a cohesive, portfolio-ready data project The material progresses naturally from initial data ingestion to final modeling, ensuring you understand the purpose behind every line of code. You will practice applying these concepts through written exercises designed to reinforce your learning. This course is designed for beginners who have a basic understanding of R syntax and want to apply their knowledge to a structured, practical project. No advanced data science or statistical experience is required. Begin reading today to assemble your R skills into a complete data science workflow.