Data Modeling with the Tidyverse in R

Organizations rely on data-driven insights to make strategic decisions, but raw data is only valuable when you know how to model it. Understanding how to ask the right questions and translate them into robust statistical models is a critical skill for any aspiring data analyst or scientist. This text-based course guides you through the process of structuring, analyzing, and modeling data using R and the modern Tidyverse ecosystem. You will transition from writing basic queries to constructing structured predictive pipelines that reveal hidden patterns and relationships within your datasets. What you will learn: Understand the fundamental terminology of statistical modeling and how to map business questions to analytical approaches; Prepare and preprocess raw data using modern tidyverse tools like dplyr and recipes; Build and evaluate predictive models using the standardized parsnip interface; Analyze model performance and interpret key metrics to ensure accuracy and reliability; Identify hidden trends and relationships in complex datasets through structured exploratory analysis; Apply modern workflows to keep your modeling code clean, reproducible, and easy to maintain. The course begins with foundational concepts of data questions and exploratory analysis before introducing you to structured modeling frameworks. You will read through clear explanations, examine practical R code snippets, and complete written exercises to reinforce your learning. This course is designed for beginners who have a basic familiarity with R and want to learn structured data modeling. No prior advanced statistics or machine learning experience is required. Start learning how to turn raw data into predictive insights today.