Data Modeling and Prediction with R

Overview

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Learn how to move from exploring data to modeling it with confidence. In this course, you’ll build and interpret linear and logistic regression models in R to uncover relationships, make predictions, and quantify uncertainty. You’ll begin by learning how to fit and interpret simple and multiple linear regression models, then advance to modeling categorical outcomes with logistic regression. Finally, you’ll explore bootstrapping and hypothesis testing to understand and communicate the uncertainty in your results. By the end of this course, you’ll be able to use statistical modeling to make and explain data-driven decisions – an essential skill for data scientists, analysts, and anyone working with real-world data.

Syllabus

Building and Interpreting Simple Linear Models

In this module, you will learn how to describe relationships between variables using simple linear regression. You’ll practice fitting models, interpreting coefficients, and visualizing patterns to uncover meaningful insights from data. By the end of this module, you’ll know how to make predictions and identify when your model might not fit as well as you think.

Expanding to Multiple Linear Regression

Real-world data is rarely simple. In this module, you’ll extend regression modeling to include multiple predictors and interaction effects. You’ll explore how adding variables improves model accuracy, how to interpret complex relationships, and how to avoid overfitting as your models become more sophisticated.

Modeling Categorical Outcomes with Logistic Regression

Not all outcomes are numerical. In this module, you’ll learn how to model categorical outcomes (e.g., “yes/no” or “spam/not spam”) using logistic regression. You’ll discover how to calculate probabilities, classify outcomes, and assess the performance of your models. Along the way, you’ll explore how overfitting affects classification and reflect on how to interpret and communicate model predictions responsibly.

Quantifying and Communicating Uncertainty

Every model comes with uncertainty and understanding and communicating that uncertainty is what makes you a thoughtful data scientist. In this final module, you’ll explore bootstrapping and randomization methods to measure confidence in your results, conduct hypothesis tests, and communicate findings transparently. By the end, you’ll bring together your modeling and inference skills to draw clear, data-driven conclusions.