MSci Physics with Data Science
Entry requirements
A Level: A*A*A /A*AAA. Details below.. IB: 7,7,6 at HL, to include Mathematics and Physics, with a minimum of 32 points overall. 7 must be in Mathematics and Physics.. BTEC: options considered when combined with other Maths and Physics qualifications.
About this course
Master in Science in Physics with Data Science, a physics and computer science degree combining core physical principles with computational data methods. Core topics include Data Analysis, Statistics, Research Methods, Project Management, and Artificial Intelligence & Machine Learning. Students complete a major project dissertation in the final year. The first year covers foundational physics and introductory computational topics through Classical Mechanics and Relativity 1, Introduction to Data Science, and Mathematics for Physicists 1. The second year progresses to advanced laboratory work and theoretical concepts via Physics Laboratory 2, Statistical Physics and Entropy, and Data Science Projects. The third year introduces specialized computational and physical study through Group Studies, Scientific Computing Laboratory 1, and Intelligent Data Analysis. The final year centers on independent research and advanced applications supported by modules such as Project Dissertation, Algorithms for Data Science, and Mathematical Foundations of Artificial Intelligence (AI) and Machine Learning (ML). Optional modules across later years allow students to explore diverse subjects including Biophysics, Machine Learning, Medical Imaging, Neural Networks and Deep Learning, and Advanced Particle Physics Techniques.
Modules
- Classical Mechanics and Relativity 1
- Classical Mechanics and Relativity 2
- Electromagnetism and Temperature and Matter
- Introduction to Data Science
- Introduction to Probability and Statistics
- Mathematics for Physicists 1
- Physics Laboratory 1
- Physics and Communication Skills 1
- Quantum Mechanics and Optics and Waves
- Data Science Projects
- Electromagnetism 2
- Foundations and Applications of Data Science
- Mathematics for Physicists 2
- Optics
- Particles and Nuclei & A Quantum Approach to Solids
- Physics Laboratory 2
- Physics and Communication Skills 2
- Quantum Mechanics 2
- Statistical Physics and Entropy
- Eigenphysics
- Lagrangian and Hamiltonian Mechanics
- Observational Astronomy
- Structure in the Universe
- Group Studies
- Quantum Mechanics 3
- Scientific Computing Laboratory 1
- Scientific Computing Laboratory 2
- Statistical Physics
- Biophysics
- Complex Variable Theory
- Fission and Fusion
- Intelligent Data Analysis
- Machine Learning
- Medical Imaging
- Neural Networks and Deep Learning
- Observational Cosmology
- Physical Principles of Radar
- The Life and Death of Stars
- Images and Communications
- Atomic Physics
- Chaos and Dynamical Systems
- Condensed Matter Physics
- Evolution of Cosmic Structure
- Exoplanets
- Natural Language Processing
- Nuclear Physics
- Particle Physics
- Physics Critique
- Physics Teaching in Schools
- Radiation and Relativity
- Project Dissertation
- Project Planning and Preliminary Report
- Project Seminar and Viva
- Advanced Particle Physics Techniques
- Algorithms for Data Science
- Complex Variable Theory
- Current Topics in Particle Physics
- Fission and Fusion
- Inference from Scientific Data
- Intelligent Data Analysis
- Machine Learning
- Mathematical Foundations of Artificial Intelligence (AI) and Machine Learning (ML)
- Nanophotonics
- Neural Networks and Deep Learning (Extended)
- Observational Cosmology
- Phase Transitions
- Quantum Mechanics 4
- Storing and Managing Data
- Superconductivity
- The General Theory of Relativity
- Ultracold Atoms and Quantum Gases
- Advanced Condensed Matter Physics
- Condensed Matter Physics
- Current Topics in Artificial Intelligence & Data Science
- Evolution of Cosmic Structure
- Exoplanets
- Images and Communications
- Many Particle and Quantum Field Theory
- Nuclear Physics
- Physics of Renewable Energy
- Quantum Optics
- Relativistic Astrophysics
- Visualisation