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MSc Structure

The MathSys MSc year is structured to provide students with the mathematical training necessary to tackle key challenges facing science, business, and society.

It is dedicated to developing a broad portfolio of mathematical techniques through taught modules covering subjects such as Stochastic Modelling and Random Processes, Numerical Algorithms and Optimisation, Data Analysis and Machine Learning, and Topics in Mathematics Modelling. Alongside this, MSc students also undertake group and individual research projects, working on research problems that have a strong emphasis on applied questions and practical approaches, and that are linked to real-world problems and experiences from the CDT's external collaborative partners.

The "Key Dates and Deadlines" for current students can be found on Teams.

Term 1 taught core modules

  • MA933 Stochastic Modelling and Random Processes (15 CATS), weeks 1-10 (time spent in lectures/classes: 4 hours per week)
  • MA934 Numerical Algorithms and Optimisation (15 CATS), weeks 1-5 (time spent in lectures/classes: 8 hours per week)
  • MA930 Data Analysis and Machine Learning (15 CATS), weeks 6-10 (time spent in lectures/classes: 8 hours per week)

Term 2 taught core module

  • MA999 Topics in Mathematical Modelling (15 CATS), weeks 1-10 (time spent in lectures/classes: 4 hours per week)

Other core modules

  • MA932 MSc Research Study Group Project (40 CATS), begins in term 2 and runs through Easter vacation and into the start of term 3
  • MA931 MSc Individual Research Project (50 CATS), runs from mid-June to September

Other compulsory activities

  • MSc cohort meetings (usually weekly in term-time) with the CDT Administrator and MSc Coordinator to discuss general topics
  • Intro to Computing (during Welcome Week) and attendance at Computational Techniques classes
  • Attendance at the Complexity/MathSys Forum (weekly in term-time)
  • Transferable Skills activities including Responsible Research and Ethics training
  • Attendance at CDT Annual Conference, Summer School, and other events/activities as requested by the CDT and as outlined in the Terms and Conditions and the Student Handbook

Taught optional modules

Students will take at least two optional modules in the second term (Spring term, January to March). These must equate to a minimum of 30 CATS (credits). Some of the most popular/frequently taken optional modules for MathSys students are listed below. Students should check on the availability of these modules with the host department(s), noting that other departments may have their own registration processes that differ to those of the CDT.

MA4E7-15 Population Dynamics: Ecology and Epidemiology [taught by Dr Louise Dyson, a member of the MathSys Management Team]

MA4M1-15 Epidemiology by Example [taught by Dr Kat Rock, a member of the MathSys Management Team]

MA4M4-15 Topics in Complexity Science [taught by Dr Marya Bazzi, a member of the MathSys Management Team]

CS924-15 Agent Based Systems

CS929-15 Algorithmic Game Theory

CS939-15 Quantum Computing

CS904-15 Computational Biology

CS909-15 Data Mining

IM903-15 Complexity in the Social Sciences (runs as a one-week module after the end of term 2) not running in 2022

IM931-15 Interdisciplinary Approaches to Machine Learning not running in 2022

ST420-15 Statistical Learning and Big Data

Students may also choose other Masters-level optional modules subject to approval of the MSc Coordinator and with the agreement of the host department(s). A list of examples can be found here.