R for Data Science: Foundations for Associate Data Scientists

Data is the driving force behind modern decision-making, and R is one of the most powerful languages designed specifically to analyze it. If you want to transition into data science, establishing a strong foundation in statistical computing and data manipulation is your critical first step. This text-based course guides you from absolute beginner to a confident practitioner ready for associate-level data science challenges. You will read clear explanations, study structured code examples, and learn how to import, clean, visualize, and model data using industry-standard R workflows. What you'll learn: 1. Understand fundamental R data structures, including vectors, lists, and data frames. 2. Wrangle and clean messy datasets using modern tidyverse packages. 3. Create clear, informative data visualizations with ggplot2 to communicate insights. 4. Apply core statistical concepts, hypothesis testing, and regression analysis to real-world scenarios. 5. Build and evaluate foundational machine learning models for classification and prediction. 6. Practice writing efficient, reusable R functions and managing projects with modern workflow best practices. You will start with core programming concepts and basic terminology before advancing to data manipulation, visualization, and introductory machine learning. Through detailed written walkthroughs and code-focused exercises, you will develop the practical skills required for associate-level data roles. This course is designed for beginners who are completely new to programming or data science, requiring no prior technical background. Start reading today and build a solid foundation for your career in data science.