Structured pathways
Courses are organised into modules and lessons so students can move from foundations to interpretation step by step.
Course catalogue
Structured quantitative courses for university students.
Explore guided courses in statistics, biostatistics, machine learning, software support and academic data analysis. The first two flagship courses are being built as full learning pathways.
Current priority
Statistics Foundation and ML in Biostatistics.
These two courses form the core launch pathway: one builds the statistical foundation, and the other applies prediction modelling to health-data questions.
Theory with application
The platform combines mathematical reasoning, examples, interactive demos and applied academic interpretation.
Responsible support
Courses support learning, confidence and understanding while maintaining academic integrity.
Available and planned courses
Start with the flagship learning pathways.
Courses are being developed in phases. Available pages open directly. Preparing pathways currently link to resources and previews until full course pages are released.
01
AvailableBeginner → intermediate
Statistics Foundation
A zero-coding, theory-first course covering statistical thinking, descriptive statistics, probability, inference and regression.
5 modules26 lessonsZero codingTheory first
02
Module 1 availableIntermediate
Machine Learning in Biostatistics
A health-data machine learning course focused on prediction, validation, overfitting, leakage, calibration and responsible reporting.
5 modules25 planned lessonsR labsCase studies
03
PreparingProject support
Research Methods & Data Analysis
A future pathway for dissertation planning, variables, analysis strategy, interpretation and reporting limitations.
Study designVariablesAnalysis planningReporting
04
PreparingApplied skills
R, Python and Software Support
A planned practical pathway for academic data analysis using R, Python, SPSS, SAS, Stata and reproducible workflows.
R basicsPython basicsSoftware choiceReproducibility
05
PreparingHealth research
Biostatistics and Medical Statistics
A planned pathway covering clinical research, epidemiology, survival analysis, regression modelling and interpretation for health data.
Clinical dataSurvival analysisEpidemiologyMedical interpretation
Course routes
Choose the route that matches your current level.
Some students need foundations first. Others already know the basics and need applied modelling, interpretation or research support.
Start from foundations
Choose Statistics Foundation if you want a careful route into statistical thinking before coding or advanced modelling.
Open Statistics Foundation →
Move into health-data prediction
Choose Machine Learning in Biostatistics if you want prediction, validation and clinical modelling interpretation.
Open ML in Biostatistics →
Browse supporting resources
Use resources if you need short guides, checklists and interpretation support before choosing a full course.
Open Resources →
Learning route
New to quantitative subjects? Start with Statistics Foundation.
The foundation course gives the language needed for later biostatistics, machine learning, research methods and data analysis.
Start Statistics Foundation →Applied route
Ready for prediction modelling? Open ML in Biostatistics.
This route focuses on medical machine learning, validation, leakage, calibration, thresholds and responsible reporting.
Open ML in Biostatistics →Need help choosing?
Use the Start Here page or send a support request.
If you are unsure whether you need a course, a resource guide or personal support, start with the route selector.