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R Programme

Data Modeling with R

Learn linear and logistic regression, model diagnostics, and prediction techniques, and turn your data into models that explain and forecast real outcomes.

R Regression caret ggplot2

Course Overview

Statistics tells you whether a relationship in your data is real. Modeling lets you use that relationship to explain outcomes and make predictions on new data. This course takes you from your first regression model to a fitted, diagnosed, and validated model you can trust and present.

You'll work through linear regression for continuous outcomes and logistic regression for binary outcomes, learning to check the assumptions behind each model, interpret coefficients and odds ratios correctly, and evaluate how well a model actually performs before you rely on it.

This is the final course in our R track, building directly on Statistical Analysis with R. If you haven't taken that course, start with Data Analysis with R first.

Course at a Glance

LevelBeginner – Intermediate
FormatOnline & Practical
Best ForStudents & Researchers
Builds OnStatistical Analysis with R

What You'll Learn

A step-by-step path from your first regression to a diagnosed, validated model you can put in front of others.

1

Foundations of Modeling

Understand the modeling workflow, splitting data into training and test sets, and the basics of evaluating a model.

2

Linear Regression

Fit simple and multiple regression models, interpret coefficients, and assess model fit using R².

3

Model Diagnostics

Check model assumptions, analyse residuals, and identify multicollinearity and outliers before trusting a model.

4

Logistic Regression

Model binary outcomes, interpret odds ratios, and set classification thresholds for real-world decisions.

5

Model Evaluation & Prediction

Use confusion matrices and accuracy, precision, and recall to evaluate models, then generate predictions on new data.

6

Modeling Capstone

Build, diagnose, and present a predictive model on a real research or business dataset from start to finish.

Who This Course Is For

Built for people who need to move beyond describing data to explaining and predicting it.

Researchers building predictive or explanatory models for a thesis, dissertation, or publication.
Analysts and professionals who need to forecast outcomes or explain what's driving results in their data.
Students progressing beyond descriptive statistics into regression-based coursework or projects.
Anyone who has completed our Statistical Analysis with R course and is ready for the next step.

Prerequisites

Completion of Statistical Analysis with R, or an equivalent, working understanding of hypothesis testing and basic regression, is required before starting this course.

Ready to build these skills?

Reach out to our team to ask questions about this course or express your interest. We'll notify you as soon as the next intake opens.