MPhys Physics with Data Science
Entry requirements
A level: AAA IB: 34 points
About this course
MPhys in Physics and Computer Science, combining physical sciences with computational methods and analytical techniques. Core topics include Microelectronics, Data Analysis, Statistics, Mathematical Modelling, and Artificial Intelligence & Machine Learning. Students complete a final-year project working alongside faculty researchers.
Modules
- Foundations of Data Analysis
- Introduction to Astrophysics
- Mathematical Methods for Physics 1
- Mechanics
- Physics Study Success
- Data Structures & Algorithms
- Mathematical Methods for Physics 2
- Physics Year 1 Laboratory
- Waves and Fields
- Applying Physics Skills
- Electrodynamics
- Mathematical Methods for Physics 3
- Physics Year 2 Laboratory
- Applied Machine Learning
- Quantum Mechanics 1
- Scientific Computing
- Thermal and Statistical Physics
- Advanced Physics Laboratory A
- Atomic Physics
- Condensed State Physics
- Linear Statistical Models (L6)
- Advanced Physics Laboratory B
- Quantum Mechanics 2
- Statistical Inference (L.6)
- Financial Derivatives
- Lasers and Photonics
- Neural Networks
- Particle Physics
- Semiconductors and Nanomaterial Devices (L6)
- Stellar and Planetary Physics
- MPhys Final Year Project
- Algorithmic Data Science
- Atom Light Interactions
- Cosmology
- Data Analysis Techniques
- Data Science Methods Autumn (L7)
- Galactic Astrophysics
- General Relativity
- Quantum Computing
- Quantum Field Theory
- Quantum Optics and Quantum Information
- Advanced Cosmology
- Advanced Natural Language Processing
- Advanced Quantum Field Theory
- Astrophysical Processes
- Beyond the Standard Model
- Electrons, Cold Atoms & Quantum Circuits
- Frontiers in Particle Physics
- Image Processing
- Machine Learning
- Monte Carlo Simulations (L7)
- Practical Quantum Technologies
- Wider Topics in Data Science (L7)