Foundation learner
Start with Statistics Foundation if you want a clear route through statistical thinking, descriptive statistics, probability, inference and regression.
Open Statistics Foundation →Learning Hub
A structured learning hub for quantitative subjects.
Start with a pathway, read focused resources, build intuition with visual demos and submit an enquiry when you need guided academic support.
Hub model
Learn first through resources and courses. Explore ideas visually. Then use enquiry review if you need support matched to your subject, level and topic.
Start here
Choose the route that matches your current need.
Applied learner
Use resources and interactive demos if you are learning through examples, visual intuition, interpretation and applied problem-solving.
Explore resources →Research / project learner
Use research-method resources and enquiry review if you need help clarifying a question, method choice, interpretation or project plan.
Submit enquiry →Subject pathways
Find your subject area.
These pathways are starting points, not fixed limits. Students can move between subjects depending on their module, project or learning goal.
Statistics
Statistical thinking, probability, inference, regression, uncertainty and interpretation.
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Mathematics
Algebra, calculus, linear algebra, probability and quantitative reasoning foundations.
Open pathway →
Data Science
Data preparation, exploratory analysis, modelling ideas, validation and responsible reporting.
Open pathway →
Biostatistics
Health data, study design, clinical interpretation, medical statistics and evidence reasoning.
Open pathway →
Bioinformatics
Biological data interpretation, omics ideas, computational biology concepts and analytical thinking.
Open pathway →
Research Methods
Research questions, study design, analysis planning, reporting and critical interpretation.
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Featured resources
Read focused guides before asking for support.
View all resources →Statistics · Foundation
How to choose the correct statistical test
6 min read · Updated 5 June 2026
A detailed guide for students deciding between t-tests, ANOVA, chi-square tests, correlation, regression, logistic regression and non-parametric methods.
Data analysis · Foundation
How to prepare your data before analysis
5 min read · Updated 5 June 2026
A detailed guide for students learning how to clean, check, structure and document data before running statistical analysis.
Statistics · Foundation
Understanding p-values, confidence intervals and effect sizes
6 min read · Updated 5 June 2026
A detailed guide explaining statistical significance, uncertainty, effect size, practical importance and how students should interpret results responsibly.
Regression · Foundation
Choosing between correlation and regression
6 min read · Updated 5 June 2026
A detailed guide helping students understand when to use correlation, when to use regression, and why the research question matters more than the software menu.
Regression · Intermediate
Linear regression assumptions and diagnostics
5 min read · Updated 5 June 2026
A detailed guide to the assumptions behind linear regression, why they matter, how students should think about diagnostics and how to report limitations clearly.
Biostatistics · Intermediate
Logistic regression explained for health and social science students
6 min read · Updated 5 June 2026
A detailed guide to logistic regression for binary outcomes, including odds, odds ratios, interpretation, adjustment, limitations and common reporting mistakes.
Research methods · Intermediate
Common mistakes in dissertation data analysis
5 min read · Updated 5 June 2026
An advanced guide to the most common statistical, methodological and reporting mistakes students make in dissertation data analysis, with practical ways to avoid them.
Regression · Intermediate
How to report regression results in a dissertation
5 min read · Updated 5 June 2026
An advanced guide to reporting linear, logistic and adjusted regression results clearly in dissertation chapters, including interpretation, tables, confidence intervals and limitations.
Interactive demos
Build intuition visually.
Use demos to explore statistical ideas before moving into formal definitions, formulas or applied examples.
Course pathways
Follow a structured course route.
Statistics Foundation
Full course open nowA zero-coding foundation pathway with 5 modules and 26 theoretical lessons.
Machine Learning in Biostatistics
Preview open nowStart with Lesson 1.1: What is machine learning in biostatistics? The lesson introduces machine learning as a biostatistical prediction workflow: define the clinical question, identify the outcome, choose predictors, separate training and test data, interpret output and avoid causal overclaiming. All remaining lessons open in July 2026.
Other subject routes
From September 2026Biostatistics, epidemiology, regression, survival analysis and other routes will release gradually.
Need guidance?
Submit enquiry → review → match.
If resources and demos are not enough, submit an enquiry. The request is reviewed for subject area, education level, topic and academic-integrity suitability before support is suggested.