In this module, you’ll build regression models that explain relationships and forecast results, like how customer satisfaction might shift with service speed, or how multiple factors affect patient recovery. You’ll start simple, then move to more complex models, using R and GitHub Copilot to build, test, and troubleshoot your code efficiently. No fluff, just practical regression skills you’ll actually use.

This module gives you the tools to model decisions, like whether a customer will convert, a treatment will succeed, or a transaction might fail. You’ll learn how logistic regression works, when to use it, and how to interpret the results. Through hands-on labs and AI-assisted coding, you’ll build models that do more than guess, they explain. By the end, you’ll be able to evaluate model performance and make confident, probability-based predictions.

This module gives you the skills to break down time-based data and build forecasts you can trust. You’ll learn how to spot trends, understand seasonal shifts, and apply proven methods like moving averages and exponential smoothing. Whether you’re predicting sales, staffing needs, or web traffic, you’ll use R and GitHub Copilot to create models that support smarter, evidence-based decisions.

In this final module, you’ll apply your regression skills to a guided project that mirrors real analysis work. You’ll prepare data, build and validate a predictive model, and generate insights you can explain. You’ll also explore how R integrates with tools like Excel and Power BI, which are useful if you need to share results in business-friendly formats. This is your chance to practice end-to-end analysis and show what you can do with data.