Statistical Data Analytics with R
A Quarto-style statistical report with data preparation, uncertainty, tests, effects, regression, diagnostics, and cautious conclusions.
What you will be able to do
A Quarto-style statistical report with data preparation, uncertainty, tests, effects, regression, diagnostics, and cautious conclusions.
Use a repeatable workflow instead of disconnected tips.
Check assumptions, evidence, output quality, and limitations before using results.
Create a capstone artifact that can be inspected, improved, and reviewed.
What you need before starting
Learners who know R basics and want a careful statistics workflow in R.
R Foundations for Data Analysis and Basic Statistics for Data Analysis are recommended.
Static lessons, activities, knowledge checks, learner templates, and a downloadable text-first pack.
12-module course sequence
The sequence follows the approved detailed syllabus and is implemented as a complete text-first shell.
R Statistical Workflow
- Set up the workflow for reproducible statistical analysis in R.
- Text-first module with units, activity, knowledge check, and summary.
Preparing Data For Statistical Analysis
- Make sure analysis data matches the statistical question.
- Text-first module with units, activity, knowledge check, and summary.
Descriptive Statistics And Statistical Visualisation
- Summarise evidence before testing.
- Text-first module with units, activity, knowledge check, and summary.
Simulation, Bootstrap, And Confidence Intervals
- Use R to make uncertainty visible.
- Text-first module with units, activity, knowledge check, and summary.
Hypothesis Testing And Test Selection
- Teach a repeatable test-selection workflow in R.
- Text-first module with units, activity, knowledge check, and summary.
Comparing Groups And Categorical Data
- Apply common comparison methods carefully.
- Text-first module with units, activity, knowledge check, and summary.
Effect Sizes And Practical Significance
- Teach magnitude, not only significance.
- Text-first module with units, activity, knowledge check, and summary.
Correlation And Simple Linear Regression
- Teach relationship analysis without causal overclaiming.
- Text-first module with units, activity, knowledge check, and summary.
Multiple Regression And Diagnostics
- Add predictors while keeping interpretation honest.
- Text-first module with units, activity, knowledge check, and summary.
Logistic Regression And Binary Outcomes
- Teach binary outcome modelling separately from linear regression.
- Text-first module with units, activity, knowledge check, and summary.
Experiments, A/B Tests, And Causal Caution
- Apply statistical analysis to experiments without overclaiming.
- Text-first module with units, activity, knowledge check, and summary.
Capstone Statistical Report
- Bring the full workflow together.
- Text-first module with units, activity, knowledge check, and summary.
Course status: outline published, lessons in development
The full syllabus, module structure, activities, and download pack are published and free to use now. Detailed lesson writing is still in progress, so lesson pages currently give the shape of each topic rather than the final teaching depth. Per-module assessments, worked examples, instructional diagrams, and external specialist review are planned next.