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Explore focused courses across Statistics, Mathematics, Data Science, Bioinformatics and Computer Science. Learn one topic deeply or combine courses into a broader academic or career pathway.
Popular courses across the platform.
These courses represent useful entry points into some of our most important quantitative and computational learning areas.
Statistics Foundations
A clear and intuitive introduction to data, probability, distributions, sampling, confidence intervals and statistical reasoning.
Regression & Statistical Modelling
Move from simple linear regression to multivariable models, interactions, diagnostics and practical interpretation.
Survival Analysis
Learn time-to-event analysis from Kaplan–Meier curves to Cox regression and modern survival modelling.
Calculus Foundations
Build an intuitive understanding of limits, derivatives, integrals and the idea of continuous change.
Linear Algebra for Modern Science
Understand vectors, matrices, linear transformations, eigenvalues and the geometry behind statistics and data science.
Mathematics for Data Science
Learn the essential linear algebra, calculus and probability needed to understand modern data science and machine learning.
Study at the level that matches you.
The same discipline can require very different depth depending on whether you are preparing for school exams, studying at university, conducting research or learning independently.
High School
14 coursesClear explanations, exam-ready practice and curriculum-aware learning for GCSE, A-Level, AP, IB and equivalent programmes.
02Undergraduate
21 coursesStructured learning for lectures, problem sheets, assignments, examinations and deeper conceptual understanding at university level.
03Postgraduate
17 coursesAdvanced methods, specialist topics, research workflows and technical support for MSc, MRes, doctoral and professional study.
04Learn for Yourself
14 coursesCareer development, curiosity, refreshers and practical projects—learn useful quantitative and computational skills at your own pace.
Explore courses by subject.
Every subject has its own progression from foundations through university study to specialist and practical applications.
Statistics
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.
ANOVA & Experimental Design
Learn how experiments are designed, compared and analysed using analysis of variance and related methods.
Statistical Computing with R
Learn R for statistical analysis, reproducible workflows, data visualisation and applied modelling.
Bayesian Statistics
Develop intuition for priors, likelihoods, posterior distributions, Bayesian modelling and decision making.
Survival Analysis
Learn time-to-event analysis from Kaplan–Meier curves to Cox regression and modern survival modelling.
Longitudinal Data & Mixed Models
Analyse repeated and clustered data using mixed-effects models and longitudinal modelling strategies.
Causal Inference
Move beyond association using causal diagrams, potential outcomes, adjustment strategies and modern causal methods.
Statistics for Everyday Life
Learn how to interpret percentages, risk, averages, polls, medical claims and statistics in everyday life.
Practical Statistics with Excel
Use Excel to summarise data, create visualisations, perform statistical tests and understand basic regression.
Mathematics
Algebra Foundations
Build fluency with expressions, equations, inequalities, functions and the algebraic reasoning needed for advanced mathematics.
Geometry & Trigonometry
Understand shapes, angles, coordinate geometry and trigonometric relationships through visual reasoning.
Calculus Foundations
Build an intuitive understanding of limits, derivatives, integrals and the idea of continuous change.
A-Level Mathematics
A structured route through major pure mathematics topics used across A-Level study.
University Calculus
A deeper treatment of single-variable and multivariable calculus with analytical and applied perspectives.
Linear Algebra for Modern Science
Understand vectors, matrices, linear transformations, eigenvalues and the geometry behind statistics and data science.
Differential Equations
Learn how differential equations model dynamic systems across science, engineering and quantitative research.
Discrete Mathematics
Explore logic, proof, combinatorics, relations, graphs and discrete structures central to computer science.
Real Analysis
Develop rigorous foundations in limits, continuity, differentiation, integration and convergence.
Optimisation
Study unconstrained and constrained optimisation with applications across statistics, machine learning and operations research.
Probability Theory
Build a rigorous understanding of random variables, convergence, expectation and foundational probability theory.
Mathematics for Data Science
Learn the essential linear algebra, calculus and probability needed to understand modern data science and machine learning.
Mathematical Thinking
Develop logic, pattern recognition and problem-solving habits that make advanced quantitative subjects easier to learn.
Data Science
Data Literacy
Learn how data are collected, cleaned, visualised and interpreted in science, society and everyday decision making.
Python Foundations
Learn Python programming through small data-focused exercises and projects.
R for Data Analysis
Learn R through practical workflows involving data wrangling, visualisation, statistical summaries and reporting.
Exploratory Data Analysis & Visualisation
Learn how to explore datasets systematically and communicate patterns through clear visualisation.
Machine Learning
Understand supervised and unsupervised learning through intuition, code, validation and responsible model evaluation.
Time Series & Forecasting
Analyse time-dependent data, identify structure and build practical forecasting models.
Advanced Machine Learning
Go deeper into model selection, regularisation, ensembles, feature engineering and advanced predictive workflows.
Deep Learning
Understand neural networks, optimisation, representation learning and modern deep learning architectures.
Natural Language Processing
Learn how computers represent, analyse and model human language using classical and modern NLP methods.
Python for Data Science
Learn Python by working with real datasets, progressing from programming fundamentals to pandas, visualisation and modelling.
SQL for Data Analysis
Query, join, summarise and analyse structured data confidently using modern SQL workflows.
Excel for Data Analysis
Turn spreadsheets into useful analytical tools using formulas, tables, pivot tables, charts and structured workflows.
Data Analyst Foundations
A practical foundation in Excel, SQL, statistics, visualisation and Python for aspiring data analysts.
Python for Data Analysis
Build a rigorous, reproducible Python data-analysis workflow with NumPy, pandas, cleaning, joins, visualisation, statistical analysis and a complete capstone project.
Bioinformatics
Genomics & Bioinformatics Foundations
Explore DNA, genes, genomes and how computers help scientists investigate biological information.
Sequence Analysis
Learn how biological sequences are represented, compared and interpreted computationally.
Linux & Biological Data
Develop the command-line skills needed to work efficiently with biological datasets and computational workflows.
Genomics & Next-Generation Sequencing
Understand sequencing technologies, genomic data formats, alignment, variant calling and core NGS workflows.
Bulk RNA-seq Analysis
Move from count matrices and experimental design to differential expression, interpretation and publication-ready figures.
Single-cell RNA-seq
Analyse single-cell transcriptomics from quality control and dimensionality reduction to clustering, annotation and differential expression.
Spatial Transcriptomics
Understand modern spatial transcriptomics technologies, spatial data structures, visualisation and biological interpretation.
Statistical Genomics & GWAS
Learn the statistical foundations and analytical workflow behind genome-wide association studies.
Epigenomics
Explore DNA methylation, chromatin regulation, epigenomic assays and statistical approaches to epigenetic data.
Bioinformatics from Zero
A friendly bridge from biology into sequence data, databases, command-line tools and reproducible computational analysis.
R for Biologists
Learn R using biological examples, from data frames and visualisation to reproducible analysis.
Python for Biologists
Learn Python through biological examples involving sequences, files, data manipulation and automation.
Computer Science
Computer Science Foundations
Understand computation, programming, algorithms, data representation and the core ideas behind computer science.
Python Programming
Learn programming from scratch with clear explanations, coding challenges and small practical projects.
Web Development Foundations
Learn how websites work and build your first responsive pages using HTML, CSS and JavaScript.
Data Structures & Algorithms
Understand core data structures, algorithm design and computational complexity through visual examples and coding practice.
Object-Oriented Programming
Learn classes, objects, abstraction, inheritance, composition and maintainable software design.
Database Systems
Understand relational databases, SQL, schema design, transactions, indexing and database architecture.
Operating Systems
Understand processes, memory, files, concurrency and the core abstractions managed by modern operating systems.
Computer Networks
Learn how information moves across networks, from protocols and routing to the modern internet.
Advanced Algorithms
Study advanced algorithm design, complexity, graph methods, dynamic programming and optimisation techniques.
Distributed Systems
Understand communication, consistency, fault tolerance and distributed architectures used in modern computing.
AI Foundations
Understand what modern artificial intelligence systems do, how they learn and how to use them thoughtfully.
Git & GitHub
Learn version control, branching, collaboration and professional GitHub workflows.
Web Development from Zero
Build modern websites from scratch using HTML, CSS, JavaScript and practical development workflows.
A course is one step. A pathway gives it direction.
Rather than collecting unrelated certificates, combine courses deliberately so each one contributes to a larger academic, technical or career goal.
Choose your goal
Know what you are building towardsStart from an exam, university module, skill, research need or career objective.
Build foundations
Fill the gaps firstLearn the mathematical, statistical or programming foundations required for more advanced work.
Go deeper
Progress deliberatelyMove from foundational courses into advanced methods and specialist applications.
Apply it
Turn learning into capabilityUse what you learn in exams, coding tasks, projects, data analyses and research.
Learn difficult ideas by interacting with them.
Use interactive labs, visual demonstrations and applied exercises alongside courses to build intuition rather than relying only on passive content.
Don't choose a random course.
Find the right starting point.
Tell us your subject, current level and goal and use the Learning Path Finder to identify a sensible route through the platform.