Build the skills
to understand,
analyse and create.
Learn Statistics, Mathematics, Data Science, Bioinformatics and Computer Science through rigorous courses, interactive practice and expert academic support.
From first principles
to advanced work.
Learn difficult ideas from first principles with clear, structured explanations.
Move through worked examples, guided exercises and independent problems.
Use interactive labs, code and real data to turn knowledge into practical skill.
Build towards university study, research, technical work and deeper learning.
Five disciplines. One place to learn them deeply.
Start with your subject, then choose the level, pathway and course that match where you are and where you want to go.
Statistics
Build statistical intuition from first principles and progress to modern modelling, causal inference, survival analysis, epidemiology and research applications.
Mathematics
Develop the mathematical language behind science, statistics, engineering and computing through visual explanations, rigorous reasoning and purposeful practice.
Data Science
Learn the complete data workflow—from cleaning and exploration to modelling, visualisation, machine learning, communication and reproducible portfolio projects.
Bioinformatics
Bridge biology, statistics and computing with practical workflows for sequencing data, genomics, transcriptomics, single-cell analysis and modern multi-omics research.
Computer Science
Learn to program, reason about algorithms and understand the systems that power modern computing—from beginner foundations to advanced software and artificial intelligence.
Learning that meets you where you are.
Study for school, strengthen university understanding, specialise at postgraduate level or simply learn because you want to.
High School
Foundations, exams and confidenceBuild strong fundamentals and prepare for GCSE, A-Level, AP, IB and other major international curricula.
02Undergraduate
University modules and skillsMaster core university topics through structured explanations, worked examples and practical exercises.
03Postgraduate
Advanced methods and researchGo deeper into advanced statistics, machine learning, bioinformatics, computing and research methods.
04Learn for Yourself
Skills, curiosity and career growthLearn Python, statistics, mathematics, data analysis and computational skills at your own pace.
Find your learning path.
Tell us what you want to learn, where you are now and what you want to achieve. We'll point you towards a sensible starting route.
Currently exploring Statistics at Undergraduate level.
Start with a course that moves you forward.
Each course is designed as part of a broader learning journey, so you always know what to learn next.
Statistics Foundations
A clear and intuitive introduction to data, probability, distributions, sampling, confidence intervals and statistical reasoning.
Probability & Data
Develop confidence with probability rules, conditional probability, random variables and data interpretation.
AP Statistics
A structured AP Statistics pathway covering exploratory analysis, probability, sampling, inference and regression.
A-Level Statistics
A focused route through the statistical ideas commonly encountered within A-Level Mathematics.
Statistical Inference
Understand estimation, uncertainty, likelihood, confidence intervals and hypothesis testing from first principles.
Regression & Statistical Modelling
Move from simple linear regression to multivariable models, interactions, diagnostics and practical interpretation.
Don't just watch. Understand what changes when you interact.
Explore statistical concepts, mathematical ideas, algorithms and data workflows through interactive labs designed to make abstract concepts visible.
See a distribution change in real time.
Interactive learning helps move concepts from memorisation to intuition.
Know where you are going, not just what lesson comes next.
Follow curated pathways that connect foundations, technical skills and applied work into a coherent route.
Data Scientist
Build a complete foundation in mathematics, statistics, Python, SQL, visualisation and machine learning.
7 structured stagesData Analyst
Develop practical skills in spreadsheets, SQL, statistics, visualisation and Python for real-world data analysis.
7 structured stagesBiostatistician
Progress from statistical inference and regression to epidemiology, survival analysis and reproducible health-data analysis.
7 structured stagesStatistical Researcher
Develop advanced statistical reasoning for research, modelling, causal questions and reproducible scientific analysis.
7 structured stagesLearn independently when you can.
Get expert help when you need it.
Get support with difficult concepts, exam preparation, university modules, programming, data analysis, dissertations and research methods.
Built for learners across major English-speaking education systems.
Core concepts stay universal, while curriculum and exam pathways can be aligned to the system you study in.
Learning works better when you can see the path, understand the idea and practise it until it becomes yours.The learning philosophy behind My Academic Tutor