Data Analysis and Modeling in R: From Statistics to Machine Learning

To make confident, data-driven decisions, you need more than just basic spreadsheets; you need the power of statistical modeling and machine learning. This text-based course guides you step-by-step through the process of analyzing complex datasets using R. You will transition from understanding fundamental data structures to executing advanced statistical tests and building predictive models. By reading clear explanations and studying curated code snippets, you will master the modern R ecosystem for data science. What you'll learn: - Understand foundational R syntax, data structures, and the modern tidyverse workflow. - Apply key statistical tests to validate hypotheses and uncover patterns in your data. - Build and evaluate predictive machine learning models using contemporary framework conventions. - Clean and preprocess messy, real-world datasets for reliable analysis. - Implement data visualization techniques to communicate insights clearly. - Practice writing reproducible, clean, and efficient R code for data workflows. The course starts with essential terminology and data manipulation basics before progressing to statistical modeling, regression analysis, and machine learning fundamentals. You will progress through structured written explanations and practical code-based exercises designed to solidify your understanding. This course is designed for aspiring data analysts, researchers, and beginners who want to develop strong programming and statistical modeling skills in R, with no prior programming experience required. Start reading today to unlock the potential of your data with R.