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Mathematics, MSci (Hons), with industry placement

City, University of London, United Kingdom

 
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Subject ranking

Overall
UK / Guardian 2025
38th
Overall
UK / QS 2025
39th
Overall
UK / CUG 2025
40th

Costs

Course feesS$38.5K / year
Entertainment, books
food & rent
S$25.1K / year
Beer S$10
MacDonalds S$13
Cinema S$21
Coffee S$6
TotalS$63.5K / year

Entry requirements

A Level ABB
Diploma 3.0
International Baccalaureate 31

Scholarships

British Chevening Scholarships
100% for tuition and living expenses
Limited quantity

Information

Course
Code
G105
Upcoming
Intakes
Sep 2025
Course
Website (External)
Pathway
Programmes
See pathways
University
Information

Duration

5 years
Graduate
2030
About the course

This four-year MSci degree in Mathematics focuses on pure mathematics with real-world applications, offering flexibility in module choices as students advance. It explores topics like calculus, probability, linear algebra, and mathematical physics, equipping graduates for careers in finance, technology, and beyond. Students gain skills in applying abstract methods to practical problems, with options for paid work placements at companies such as Barclays or Microsoft, and career development modules to enhance employability. Research projects allow exploration of diverse areas, from medical imaging to patterns in nature.The program includes core modules in the first two years, such as Functions, Vectors and Calculus, and electives in later years like Differential Equations and Machine Learning. Assessment combines examinations, coursework, and projects, with marks weighted 1:3:6:6 across the years. Feedback is typically provided within three weeks, supporting student progress.

What you will learn

This four-year MSci maths degree is focused on pure mathematics with real-world applications. As you progress, you will have increasing choice and flexibility about what you choose to study. Year 1 consists of modules that make up 125 credits. All modules are core. -Functions, Vectors and Calculus (30 credits) -Algebra (15 credits) -Linear Algebra (15 credits) -Introduction to Probability and Statistics (15 credits) -Logic and Set Theory (15 credits) -Number Theory and Cryptography (15 credits) -Introduction to Modelling (15 credits) -Skills, Careers and Employability Analysis for Mathematics students (5 credits) Year 2 consists of modules that make up 125 credits. There are seven core modules and 15 credits of elective modules. -Programming and Data Science for the Professions (15 credits) -Real and Complex Analysis (30 credits) -Vector Calculus (15 credits) -Sequences and Series (15 credits) -Decision Analysis (15 credits) -Applied Mathematics (15 credits) -Numerical Mathematics (15 credits) -Professional Development and Employability (5 credits) -Applications of Probability and Statistics (15 credits) Year 3 consists of modules that make up 120 credits. There are two core modules, a core Group Project and 60 credits of elective modules. -Differential Equations (30 credits) -Codes (15 credits) -Group Project (15 credits) -Advanced Complex Analysis (15 credits) -Stochastic Models (15 credits) -Operational Research (15 credits) -Probability and Statistics 2 (30 credits) -Graph Theory (15 credits) -Game Theory (15 credits) -Dynamical Systems (15 credits) -Introduction to the Mathematics of Fluids (15 credits) -Introduction to Mathematical Physics (15 credits) -Mathematical Processes for Finance (15 credits) -Groups and Symmetry (15 credits) -Mathematical Biology (15 credits) For an MSci (Honours) degree student to progress from year 3 to year 4, year 3 requirements must have been satisfied, and in addition an overall aggregate of 50% achieved in year 3. -MSci Project (30 credits) -The Mathematics of Information (15 credits) -Forecasting (15 credits) -Perturbation Methods (15 credits) -Mathematics for Quantum Computing (15 credits) -Game Theory (15 credits) -Graph Theory (15 credits) -Mathematics: algorithms, computation and experimentation (15 credits) -Dynamical Systems (15 credits) -Machine Learning (15 credits) -Data Visualisation (15 credits) -Deep Learning (15 credits) -Principles of Data Science (15 credits) -Introduction to Artificial Intelligence (15 credits)