University
Work through formal theory, prerequisites, proofs and substantial problem sets.
Definitions → reasoning → proof → problems
- Formal theory
- References
- Proof pathways
- Problem sheets
How to use the university pathway
- Check the stated prerequisites before starting a course.
- Read formal definitions and reproduce each argument without notes.
- Attempt the full problem set before opening hints or solution steps.
- Use the tutor to test reasoning, not to replace a proof.
Choose your course
Year 1
Chemistry Year 1
University foundations in structure and bonding, thermodynamics, kinetics, equilibrium and analytical measurement.
Life Sciences Year 1
Cell and molecular biology, genetics, evolution and introductory biostatistics.
Computer Science Year 1
University foundations in programming, discrete structures, computer organisation and data structures.
Engineering Foundations: Design, Systems and Safety
A first-year introduction to responsible engineering practice, from defining a need and modelling forces to choosing materials, prototyping circuits and communicating a tested design.
Linear Algebra: Structure, Space and Transformation
A rigorous first course in linear algebra for university study and serious self-directed learners: systems and elimination, vector spaces and linear maps, orthogonality and least squares, determinants, eigenvalues and the singular value decomposition — with computation, geometry and proof given equal weight.
Year 2
Chemistry Year 2
Organic mechanisms and synthesis, coordination chemistry and spectroscopy.
Life Sciences Year 2
Biochemistry, comparative physiology, microbiology and molecular genetics.
Computer Science Year 2
Algorithms, databases, operating systems and software engineering at second-year depth.
Year 3
Chemistry Year 3
Advanced physical chemistry, organic synthesis, and materials and polymer chemistry.
Life Sciences Year 3
Advanced molecular biology, ecology and conservation, development and introductory bioinformatics.
Computer Science Year 3
Theory of computation, introductory machine learning, computer networks and distributed systems.