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Masters Degrees (Stochastic Process)

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Take advantage of one of our 100 Master’s Scholarships to study Stochastic Processes. Theory and Application at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Read more
Take advantage of one of our 100 Master’s Scholarships to study Stochastic Processes: Theory and Application at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Postgraduate loans are also available to English and Welsh domiciled students. For more information on fees and funding please visit our website.

The MRes in Stochastic Processes: Theory and Application is delivered through optional modules for the taught element followed by a large research project that contributes to the field in an explicit way, rather than merely applying existing knowledge.

The Department of Mathematics hosts one of the strongest research groups in probability theory, especially in stochastic processes, in the UK. The senior members of this group are world leaders in their fields.

The Department’s research groups include:

Algebra and Topology Group
Areas of interest include: Noncommutative geometry, Categorical methods in algebra and topology, Homotopy theory and homological algebra and others.

Analysis and Nonlinear Partial Differential Equations Group
Areas of interest include: Reaction-diffusion and reaction-diffusion-convection equations and systems, Navier–Stokes equations in fluid dynamic, Complexity in the calculus of variations and others.

Stochastic Analysis Group
Areas of interest include: Functional inequalities and applications, Lévy-type processes, Stochastic modelling of fractal, multi-fractal and multi-scale systems, Infinite dimensional stochastic analysis and others.

Mathematical Methods in Biology and Life Sciences Group
Areas of interest include: Mathematical pharmacology; heat and mass transfer models for plant cooling; modelling cellular signal transduction dynamics; mathematical oncology: multi-scale modelling of cancer growth, progression and therapies, and modelling-optimized delivery of multi-modality therapies; multi-scale analysis of individual-based models; spreading speeds and travelling waves in ecology; high performance computing.

Key Features

The Department of Mathematics hosts one of the strongest research groups in probability theory, especially in stochastic processes, in the UK. The senior members of this group are world leaders in their fields.

Course Content

As a student on the MRes Stochastic Processes programme you will study a range of topics for the taught element including:

Stochastic Calculus based on Brownian Motion
Levy processes and more general jump processes
The advanced Black-Scholes theory
Theory and numerics of parabolic differential equations
Java programming

The Stochastic Processes: Theory and Application course consists of a taught part (60 credits) and a research project (120 credits). Students will have a personal supervisor for their research project from the start of their studies.

Research projects could be of a theoretical mathematical nature, or they could be more applied, for example in financial mathematics or actuarial studies. Some of the research projects will be of an interdisciplinary character in collaboration with some of Swansea's world class engineers. For such projects it is likely that EPSRC funding would be available.

Facilities

The Aubrey Truman Reading Room, located in the centre of the Department of Mathematics, houses the departmental library and computers for student use. It is a popular venue for students to work independently on the regular example sheets set by their lecturers, and to discuss Mathematics together.

Our main university library, Information Services and Systems (ISS), contains a notably extensive collection of Mathematics books.

Careers

The ability to think rationally and to process data clearly and accurately are highly valued by employers. Mathematics graduates earn on average 50% more than most other graduates. The most popular areas are the actuarial profession, the financial sector, IT, computer programming and systems administration, and opportunities within business and industry where employers need mathematicians for research and development, statistical analysis, marketing and sales.

Some of our students have been employed by AXA, BA, Deutsche Bank, Shell Research, Health Authorities and Local Government. Teaching is another area where maths graduates will find plenty of career opportunities.

Research

The results of the Research Excellence Framework (REF) 2014 show that our research environment (how the Department supports research staff and students) and the impact of our research (its value to society) were both judged to be 100% world leading or internationally excellent.

All academic staff in Mathematics are active researchers and the department has a thriving research culture.

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Master in BIG DATA. Read more
Master in BIG DATA : Data Analytics, Data Science, Data Architecture”, accredited by the French Ministry of Higher Education and Research, draws on the recognized excellence of our engineering school in business intelligence and has grown from the specializations in Decision Support, Business Intelligence and Business Analytics. The Master is primarily going to appeal to international students, "free movers" or those from our partner universities or for high-potential foreign engineers who are looking for an international career in the domain of Business Analytics.

This program leads to a Master degree and a Diplôma accredited by the French Ministry of Higher Education and research.

Objectives

Business Intelligence and now Business Analytics have become key elements of all companies.

The objective of this Master is to train specialists in information systems and decision support, holding a large range of mathematic- and computer-based tools which would allow them to deal with real problems, analyzing their complexity and bringing efficient algorithmic and architectural solutions. Big Data is going to be the Next Big Thing over the coming 10 years.

The targeted applications concern optimization in the processing of large amounts of data (known as Big Data), logistics, industrial automation, but above all it’s the development of BI systems architecture. These applications have a role in most business domains: logistics, production, finance, marketing, client relation management.

The need for trained engineering specialists in these domains is growing constantly: recent studies show a large demand of training in these areas.

Distinctive points of this course

• The triple skill-set with architecture (BI), data mining and business resource optimization.
• This master will be run by a multidisciplinary group: statistics, data mining, operational research, architecture.
• The undertaking of interdisciplinary projects.
• The methods and techniques taught in this program come from cutting-edge domains in industry and research, such as: opinion mining, social networks and big data, optimization, resource allocation and BI systems architecture.
• The Master is closely backed up by research: several students are completing their end-of-studies project on themes from the [email protected] laboratory, followed and supported by members from the laboratory (PhD students and researcher teachers).
• The training on the tools used in industry dedicated to data mining, operational research and Business Intelligence gives the students a plus in their employability after completion.
• Industrial partnerships with companies very involved in Big Data have been developed:
• SAS via the academic program and a ‘chaire d’entreprise’ (business chair), allowing our students access to Business Intelligence modules such as Enterprise Miner (data mining) and SAS-OR (in operational research).

Practical information

The Master’s degree counts for 120 ECTS (European Credit Transfer System) in total and lasts two years. The training lasts 1252 hours (611 hours in M1 and 641 hours in M2). The semesters are divided as follows:
• M1 courses take place from September until June and count for a total of 60 ECTS
• M2 courses take place from September until mid-April and count for a total of 42ECTS
• A five-month internship (in France) from mid- April until mid- September for 9 ECTS is required and a Master thesis for 9 ECTS.

Non-French speakers will be asked to participate to a one week intensive French course that precedes the start of the program and allows students to gain the linguistic knowledge necessary for daily interactions.

[[Organization ]]
M1 modules are taught from September to June (60 ECTS, 611 h)
• Data exploration
• Inferential Statistics (3 ECTS, 30h, 1 S*)
• Data Analysis (2 ECTS, 2h, 1 S)
• Mathematics for Computer science
• Partial Differential Equations and Finite Differences (3 ECTS, 30h, 1 S)
• Operational Research: Linear Optimization (2 ECTS, 20h, 1 S)
• Combinatory Optimization (2 ECTS, 18h, 1 S)
• Complexity theory (1 ECTS, 9h, 1 S)
• Simulation and Stochastic Process (3 ECTS, 30h, 2 S**)
• Introduction to Predictive Modelling (2ECTS, 21h, 2 S)
• Deterministic and Stochastic Optimization (3 ECTS, 30h, 2 S)
• Introduction to Data Mining (2 ECTS, 21h, 2 S)
• Software and Architecture
• Object-Oriented Modelling (OOM) with UML (3 ECTS, 30h, 1 S)
• Object-Oriented Design and Programming with Java (2 ECTS, 30h, 1 S)
• Relational Database: Modelling and Design (3ECTS, 30h, 1 S)
• PLSQL (2 ECTS, 21h, 2 S)
• Architecture and Network Programming (3 ECTS, 30h, 2 S)
• Parallel Programming (3 ECTS, 30h, 2 S)
• Engineering Science
• Signal and System (3 ECTS, 21 h, 1 S)
• Signal processing (3 ECTS, 30h, 1 S)

• Research Initiation
• Scientific Paper review (1 ECTS, 9h, 1 S)
• Final research project on BIG DATA (5 ECTS, 50h, 2 S)
• Project Management
• AGIL Methods & Transverse Project (2 ECTS, 21h, 2 S)
• Languages and workshops
• French and Foreign languages (6 ECTS, 61h, 1&2 S)
• Personal and Professional Project (1 ECTS, 15, 1 S)
*1 S= 1st semester, ** 2 S= 2nd semester

M2 Program: from September to September (60 ECTS, 641h)
M2 level is a collection of modules, giving in total 60 ECTS (42 ECTS for the modules taught from September to April, plus 9 ECTS for the internship and 9 ECTS for the Master thesis).

Computer technologies
• Web Services (3 ECTS, 24h, 1 S)
• NOSQL (2 ECTS, 20h, 1 S)
• Java EE (3 ECTS, 24, 1S)
Data exploration
• Semantic web and Ontology (2 ECTS, 20h, 1 S)
• Data mining: application (2 ECTS, 20h, 1S)
• Social Network Analysis (2ECTS, 18h, 1S)
• Collective intelligence: Web Mining and Multimedia indexation (2 ECTS, 20h, 2 S)
• Enterprise Miner SAS (2 ECTS, 20h, 2 S)
• Text Mining and natural language (2 ECTS, 20h, 2 S)
Operations Research
• Thorough operational research: modelling and business application (2 ECTS, 21h, 1 S)
• Game theory (1 ECTS, 10h, 1 S)
• Forecasting models (2 ECTS, 20h, 1 S)
• Constraint programming (2 ECTS, 20h, 2 S)
• Multi-objective and multi-criteria optimisation (2 ECTS, 20h, 2 S)
• SAS OR (2 ECTS, 20h, 2 S)
Research Initiation Initiative
• Scientific Paper review (1 ECTS, 10h, 1 S)
• Final research project on BIG DATA (2 ECTS, 39, 2 S)
BI Architecture
• BI Theory (2 ECTS, 20h, 2 S)
• BI Practice (2 ECTS, 20h, 2 S)
Languages and workshops (4 ECTS, 105h, 1&2 S)
• French as a Foreign language
• CV workshop
• Personal and Professional Project
Internship
• Internship (9 ECTS, 22 weeks minimum)
Thesis
• Master thesis (9 ECTS, 150h)

Teaching

Fourteen external teachers (lecturers from universities, teacher-researchers, professors etc.), supported by a piloting committee, will bring together the training given in Cergy.

All the classes will be taught in English, with the exception of:
• The class of FLE (French as a foreign language), where the objective is to teach the students how to understand and express themselves in French.
• Cultural Openness, where the objective is to enrich the students’ knowledge of French culture.
The EISTI offers an e-learning site to all its students, which complements everything the students will learn through their presence and participation in class:
• class documents, practical work and tutorials online
• questions and discussions between teachers and students, and among students
• a possibility of handing work in online

All Master’s students are equipped with a laptop for the duration of the program that remains the property of the EISTI.

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Programme structure. The programme offers four "core" modules, taken by all students, along with a variety of elective modules from which students can pick and choose. Read more
Programme structure
The programme offers four "core" modules, taken by all students, along with a variety of elective modules from which students can pick and choose. There are examinations and coursework in eight modules altogether, including the four core modules. Additionally, all students complete a dissertation.

Core modules
0.Probability and stochastics. This course provides the basics of the probabilistic ideas and mathematical language needed to fully appreciate the modern mathematical theory of finance and its applications. Topics include: measurable spaces, sigma-algebras, filtrations, probability spaces, martingales, continuous-time stochastic processes, Poisson processes, Brownian motion, stochastic integration, Ito calculus, log-normal processes, stochastic differential equations, the Ornstein-Uhlenbeck process.


0.Financial markets. This course is designed to cover basic ideas about financial markets, including market terminology and conventions. Topics include: theory of interest, present value, future value, fixed-income securities, term structure of interest rates, elements of probability theory, mean-variance portfolio theory, the Markowitz model, capital asset pricing model (CAPM), portfolio performance, risk and utility, portfolio choice theorem, risk-neutral pricing, derivatives pricing theory, Cox-Ross-Rubinstein formula for option pricing.


0.Option pricing theory. The key ideas leading to the valuation of options and other important derivatives will be introduced. Topics include: risk-free asset, risky assets, single-period binomial model, option pricing on binomial trees, dynamical equations for price processes in continuous time, Radon-Nikodym process, equivalent martingale measures, Girsanov's theorem, change of measure, martingale representation theorem, self-financing strategy, market completeness, hedge portfolios, replication strategy, option pricing, Black-Scholes formula.


0.Financial computing I. The idea of this course is to enable students to learn how the theory of pricing and hedging can be implemented numerically. Topics include: (i) The Unix/Linux environment, C/C++ programming: types, decisions, loops, functions, arrays, pointers, strings, files, dynamic memory, preprocessor; (ii) data structures: lists and trees; (iii) introduction to parallel (multi-core, shared memory) computing: open MP constructs; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.


0.Interest rate theory. An in-depth analysis of interest-rate modelling and derivative pricing will be presented. Topics include: interest rate markets, discount bonds, the short rate, forward rates, swap rates, yields, the Vasicek model, the Hull-White model, the Heath-Jarrow-Merton formalism, the market model, bond option pricing in the Vasicek model, the positive interest framework, option and swaption pricing in the Flesaker-Hughston model.

Elective modules

0.Portfolio theory. The general theory of financial portfolio based on utility theory will be introduced in this module. Topics include: utility functions, risk aversion, the St Petersburg paradox, convex dual functions, dynamic asset pricing, expectation, forecast and valuation, portfolio optimisation under budget constraints, wealth consumption, growth versus income.


0.Information in finance with application to credit risk management. An innovative and intuitive approach to asset pricing, based on the modelling of the flow of information in financial markets, will be introduced in this module. Topics include: information-based asset pricing – a new paradigm for financial risk management; modelling frameworks for cash flows and market information; applications to credit risk modelling, defaultable discount bond dynamics, the pricing and hedging of credit-risky derivatives such as credit default swaps (CDS), asset dependencies and correlation modelling, and the origin of stochastic volatility.

0.Mathematical theory of dynamic asset pricing. Financial modelling and risk management involve not only the valuation and hedging of various assets and their positions, but also the problem of asset allocation. The traditional approach of risk-neutral valuation treats the problem of valuation and hedging, but is limited when it comes to understanding asset returns and the behaviour of asset prices in the real-world 'physical' probability measure. The pricing kernel approach, however, treats these different aspects of financial modelling in a unified and coherent manner. This module introduces in detail the techniques of pricing kernel methodologies, and its applications to interest-rete modelling, foreign exchange market, and inflation-linked products. Another application concerns the modelling of financial markets where prices admit jumps. In this case, the relation between risk, risk aversion, and return is obscured in traditional approaches, but is made clear in the pricing kernel method. The module also covers the introduction to the theory of Lévy processes for jumps and its applications to dynamic asset pricing in the modern setting.

0.Financial computing II: High performance computing. In this parallel-computing module students will learn how to harness the power of a multi-core computer and Open MP to speed up a task by running it in parallel. Topics include: shared and distributed memory concepts; Message Passing and introduction to MPI constructs; communications models, applications and pitfalls; open MP within MPI; introduction to Graphics Processors; GPU computing and the CUDA programming model; CUDA within MPI; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.


0.Risk measures, preference and portfolio choice. The idea of this module is to enable students to learn a variety of statistical techniques that will be useful in various practical applications in investment banks and hedge funds. Topics include: probability and statistical models, models for return distributions, financial time series, stationary processes, estimation of AR processes, portfolio regression, least square estimation, value-at-risk, coherent risk measures, GARCH models, non-parametric regression and splines.

Research project

Towards the end of the Spring Term, students will choose a topic to work on, which will lead to the preparation of an MSc dissertation. This can be thought of as a mini research project. The project supervisor will usually be a member of the financial mathematics group. In some cases the project may be overseen by an external supervisor based at a financial institution or another academic institution.

Read less
Programme structure. The programme offers five "core" modules, taken by all candidates, along with a variety of elective modules from which students can pick and choose. Read more
Programme structure

The programme offers five "core" modules, taken by all candidates, along with a variety of elective modules from which students can pick and choose. There are lectures, examinations and coursework in eight modules altogether, including the five core modules. Additionally, all students complete an individual research project on a selected topic in financial mathematics, leading to the submission of a dissertation.

Core modules

Probability and stochastics. This course provides the basics of the probabilistic ideas and mathematical language needed to fully appreciate the modern mathematical theory of finance and its applications. Topics include: measurable spaces, sigma-algebras, filtrations, probability spaces, martingales, continuous-time stochastic processes, Poisson processes, Brownian motion, stochastic integration, Ito calculus, log-normal processes, stochastic differential equations, the Ornstein-Uhlenbeck process.

Financial markets. This course is designed to cover basic ideas about financial markets, including market terminology and conventions. Topics include: theory of interest, present value, future value, fixed-income securities, term structure of interest rates, elements of probability theory, mean-variance portfolio theory, the Markowitz model, capital asset pricing model (CAPM), portfolio performance, risk and utility, portfolio choice theorem, risk-neutral pricing, derivatives pricing theory, Cox-Ross-Rubinstein formula for option pricing.

Option pricing theory. The key ideas leading to the valuation of options and other important derivatives will be introduced. Topics include: risk-free asset, risky assets, single-period binomial model, option pricing on binomial trees, dynamical equations for price processes in continuous time, Radon-Nikodym process, equivalent martingale measures, Girsanov's theorem, change of measure, martingale representation theorem, self-financing strategy, market completeness, hedge portfolios, replication strategy, option pricing, Black-Scholes formula.


Interest rate theory. An in-depth analysis of interest-rate modelling and derivative pricing will be presented. Topics include: interest rate markets, discount bonds, the short rate, forward rates, swap rates, yields, the Vasicek model, the Hull-White model, the Heath-Jarrow-Merton formalism, the market model, bond option pricing in the Vasicek model, the positive interest framework, option and swaption pricing in the Flesaker-Hughston model.

Financial computing I. The idea of this course is to enable students to learn how the theory of pricing and hedging can be implemented numerically. Topics include: (i) The Unix/Linux environment, C/C++ programming: types, decisions, loops, functions, arrays, pointers, strings, files, dynamic memory, preprocessor; (ii) data structures: lists and trees; (iii) introduction to parallel (multi-core, shared memory) computing: open MP constructs; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.

Elective modules

Portfolio theory. The general theory of financial portfolio based on utility theory will be introduced in this module. Topics include: utility functions, risk aversion, the St Petersburg paradox, convex dual functions, dynamic asset pricing, expectation, forecast and valuation, portfolio optimisation under budget constraints, wealth consumption, growth versus income.

Information in finance with application to credit risk management. An innovative and intuitive approach to asset pricing, based on the modelling of the flow of information in financial markets, will be introduced in this module. Topics include: information-based asset pricing – a new paradigm for financial risk management; modelling frameworks for cash flows and market information; applications to credit risk modelling, defaultable discount bond dynamics, the pricing and hedging of credit-risky derivatives such as credit default swaps (CDS), asset dependencies and correlation modelling, and the origin of stochastic volatility.


Mathematical theory of dynamic asset pricing. Financial modelling and risk management involve not only the valuation and hedging of various assets and their positions, but also the problem of asset allocation. The traditional approach of risk-neutral valuation treats the problem of valuation and hedging, but is limited when it comes to understanding asset returns and the behaviour of asset prices in the real-world 'physical' probability measure. The pricing kernel approach, however, treats these different aspects of financial modelling in a unified and coherent manner. This module introduces in detail the techniques of pricing kernel methodologies, and its applications to interest-rete modelling, foreign exchange market, and inflation-linked products. Another application concerns the modelling of financial markets where prices admit jumps. In this case, the relation between risk, risk aversion, and return is obscured in traditional approaches, but is made clear in the pricing kernel method. The module also covers the introduction to the theory of Lévy processes for jumps and its applications to dynamic asset pricing in the modern setting.


Financial computing II: High performance computing. In this parallel-computing module students will learn how to harness the power of a multi-core computer and Open MP to speed up a task by running it in parallel. Topics include: shared and distributed memory concepts; Message Passing and introduction to MPI constructs; communications models, applications and pitfalls; open MP within MPI; introduction to Graphics Processors; GPU computing and the CUDA programming model; CUDA within MPI; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.

Risk measures, preference and portfolio choice. The idea of this module is to enable students to learn a variety of statistical techniques that will be useful in various practical applications in investment banks and hedge funds. Topics include: probability and statistical models, models for return distributions, financial time series, stationary processes, estimation of AR processes, portfolio regression, least square estimation, value-at-risk, coherent risk measures, GARCH models, non-parametric regression and splines.

Research project

Towards the end of the Spring Term, students will choose a topic for an individual research project, which will lead to the preparation and submission of an MSc dissertation. The project supervisor will usually be a member of the Brunel financial mathematics group. In some cases the project may be overseen by an external supervisor based at a financial institution or another academic institution.

Read less
Take advantage of one of our 100 Master’s Scholarships to study Mathematics at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Read more
Take advantage of one of our 100 Master’s Scholarships to study Mathematics at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Postgraduate loans are also available to English and Welsh domiciled students. For more information on fees and funding please visit our website.

As an MSc by Research in Mathematics student you will be guided by internationally leading researchers and will carry out a large individual research project.

You will be fully integrated into one of our established research groups and participate in research activities such as seminars, workshops, laboratories, and field work.

Key Features

Swansea is a research-led University and the Mathematics Department makes a significant contribution, meaning that as a postgraduate Mathematics student you will benefit from the knowledge and skills of internationally renowned academics.

In the Department of Mathematics at Swansea you will find friendly teaching staff that are fully committed to providing you with a supportive teaching and learning environment. This includes outstanding student support.

All postgraduate Mathematics programmes at Swansea will equip you with skills relevant for a rewarding career in a range of diverse fields. You will also further develop your communication, presentation and analytical skills.

The Mathematics Department’s research groups include:

Algebra and Topology Group

Areas of interest include: Noncommutative geometry, Categorical methods in algebra and topology, Homotopy theory and homological algebra and others.

Analysis and Nonlinear Partial Differential Equations Group

Areas of interest include: Reaction-diffusion and reaction-diffusion-convection equations and systems, Navier–Stokes equations in fluid dynamic, Complexity in the calculus of variations and others.

Stochastic Analysis Group

Areas of interest include: Functional inequalities and applications, Lévy-type processes, Stochastic modelling of fractal, multifractal and multiscale systems, Infinite dimensional stochastic analysis and others.

Mathematical Methods in Biology and Life Sciences Group

Areas of interest include: Mathematical pharmacology; heat and mass transfer models for plant cooling; modelling cellular signal transduction dynamics; mathematical oncology: multi-scale modelling of cancer growth, progression and therapies, and modelling-optimized delivery of multi-modality therapies; multi-scale analysis of individual-based models; spreading speeds and travelling waves in ecology; high performance computing

Employability

The ability to think rationally and to process data clearly and accurately are highly valued by employers. Mathematics graduates earn on average 50% more than most other graduates. The most popular areas are the actuarial profession, the financial sector, IT, computer programming and systems administration, and opportunities within business and industry where employers need mathematicians for research and development, statistical analysis, marketing and sales.

Facilities

The Aubrey Truman Reading Room, located in the centre of the Department of Mathematics, houses the departmental library and computers for student use, and is a popular venue for students to work independently on the regular exercise sheets set by their lecturers, and to discuss mathematics together.

The main university library, the Learning and Information Centre (LIC), contains a notably extensive collection of mathematics books.

As part of our expansion, we are building the Computational Foundry on our Bay Campus for computer and mathematical sciences. This development is exciting news for Swansea Mathematics who are part of the vibrant and growing community of world-class research leaders drawn from computer and mathematical sciences.

Research

The results of the Research Excellence Framework (REF) 2014 show that our research environment (how the Mathematics Department supports research staff and students) and the impact of our research (its value to society) were both judged to be 100% world leading or internationally excellent.

All academic staff in Mathematics are active researchers and the department has a thriving research culture.

Read less
This MSc teaches advanced analytical and computational skills for success in a data rich world. Read more
This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantitative finance, artificial intelligence and machine vision.

Degree information

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the area of machine learning and statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research project (60 credits). Please note that not all combinations of optional modules will be available due to timetabling restrictions.

Core modules
-Supervised Learning
-Statistical Modelling and Data Analysis
-Graphical Models or Probabilistic and Unsupervised Learning
Plus one of:
-Applied Bayesian Methods
-Statistical Design of Investigations
-Statistical Computing
-Statistical Inference

Optional modules - students select 60 credits from the following list:
-Advanced Topics in Machine Learning
-Affective Computing and Human-Robot Interaction
-Applied Bayesian Methods
-Approximate Inference and Learning in Probabilistic Models
-Computational Modelling for Biomedical Imaging
-Information Retrieval and Data Mining
-Machine Vision
-Selected Topics in Statistics
-Optimisation
-Statistical Design of Investigations
-Statistical Inference
-Statistical Natural Language Programming
-Stochastic Methods in Finance
-Stochastic Methods in Finance 2
-Advanced Topics in Statistics
-Mathematical Programming and Research Methods
-Intelligent Systems in Business

Dissertation/report
All MSc students undertake an independent research project, which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning
The programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process.

Careers

There is a strong national and international demand for graduates with skills at the interface of traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities. Globally there are a large number of very successful users of this technology, many located in the UK. Areas in which expertise in statistics and machine learning is in particular demand include; finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, Chicago, as well as at UCL. The MSc is also ideal preparation for a PhD, in statistics, machine learning or a related area.

Top career destinations for this degree:
-Statistical and Algorithm Analyst, Telemetry
-Decision Scientist, Everline
-Computer Vision Researcher, Slyce
-Data Scientist, YouGov
-Research Engineer, DeepMind

Employability
Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. CSML graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our CSML graduates. Similarly graduates now work in companies in, amongst others, Germany, Iceland, France and the US in large-scale data analysis. The finance sector has also hired several graduates recently.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning having coordinated the PASCAL European Network of Excellence.

Coupled with the internationally renowned Gatsby Computational Neuroscience and Machine Learning Unit, and UCL Statistical Science, this MSc programme draws on world-class research and teaching talents. The centre has excellent links with world-leading companies in internet technology, finance and related information areas.

The programme is designed to train students in both the practical and theoretical sides of machine learning. A significant grounding in computational statistics is also provided.

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Mankind has always communicated, but the means of communication changes. Over the past century, communication technologies have had a fundamental impact on how we carry out our daily lives. Read more
Mankind has always communicated, but the means of communication changes. Over the past century, communication technologies have had a fundamental impact on how we carry out our daily lives. Besides using the internet and mobile phones for interpersonal communication; businesses, banking, transportation systems, TV and radio broadcasts and smart power grids rely on advanced communication technology.

In a constant and rapidly evolving field, you as a communication engineer will be needed to design and build the systems of the future.

Programme aim

Society today is firmly rooted in electronic communication systems, and it is hard to imagine life without them.

Global systems such as TV, radio, the Internet and wired and mobile telephones have a fundamental impact on the way we live and work.

In the near future, we will see rapid development of e.g.

- sensor network communication,
- algorithms to decrease energy consumption of communication networks,
- tele-presence systems that reduce the need for transportation of people,
- communication as it becomes an increasingly prominent aspect of vehicles and transportation,
- many more areas.

Exactly what the future will bring is unknown, but some things are almost certain: there will still be advanced communication systems - some of them will be different from what the world knows today and communication engineers will be needed to develop and maintain them.

Programme description

Global communication systems have not only changed the world but are also advancing at an exceptional rate. Future communication systems will form the foundation for a sustainable and intelligent society where people and equipment can be connected anywhere, any time – with anything. A high degree of connectivity will be a key enabler for new innovative technologies and applications that can benefit from information sharing.

Evolving technologies are e.g. 5G mobile communications, machine communications, fibre optical links and networks, and sensor network communication, with emerging new applications such as remote and assisted medical diagnosis and treatment, traffic and vehicle safety, environmental monitoring, maximizing efficiency and reliability in smart grid infrastructure, and tele-presence systems that reduce the need for energy consuming transportation of people.

In order to gain insight into communication systems of the future, and to develop such systems, solid analytical skills and an understanding of the fundamental principles of digital information transmissions are essential.

Besides the fundamentals in communication engineering we focus on e.g. random signal analysis, stochastic methods for digital modulation and coding, applications of digital signal processing, optical fibres and lasers and information theory and coding.

The combination of theoretical and applied knowledge in systems that apply on a global scale gives you a toolbox and a degree in Communication Engineering for a lifelong learning process in communication technologies.

Who should apply

You should apply if you find the future outlook for communication engineers interesting, and have the following skills at a bachelor’s level: signals and systems theory (including linear systems and transforms), mathematical analysis (including probability and linear algebra) and basic programming. Basic knowledge in data communications is recommended but not required.

Why apply

In order to gain insight into communication systems of the future, and to develop such systems, solid analytical skills and an understanding of the fundamental principles of digital information transmissions are essential, where mathematics and signal processing are important tools. The combination of theoretical and applied knowledge prepares students with a degree in Communication Engineering for a lifelong learning process in communications.

Educational methods

The pedagogical structure of the programme is targeted towards learning system design processes as practiced in the communication industry. In general, the educational methods are based on what are expected from engineering graduates in an industrial environment, with specific emphasis on building and refining problem-solving skills, team work and presentation skills. Certain emphasis is placed on solving complex tasks by defining subtasks and interfaces, performing these subtasks independently, and assembling the results. All courses in the program are permeated by the principles of sustainable development. You get the opportunity to interact with the industry via guest lectures and study visits. Finally, the Master’s Thesis gives you training in individual research, project planning, documentation and presentation. It can be carried out at the University, industry or another university/research institute.

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Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study Mathematics and Computing for Finance at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Read more
Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study Mathematics and Computing for Finance at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Postgraduate loans are also available to English and Welsh domiciled students. For more information on fees and funding please visit our website.

The MSc Mathematics and Computing for Finance course has been designed to meet the growing demand for specially trained mathematicians to work in the world’s financial markets and insurance.

Despite the current volatile nature of the banking industry, many banks still have a pressing need for employees with advanced mathematical skills who can further their understanding of turbulence in financial markets.

On the Mathematics and Computing for Finance course you will study different elements of both mathematics and computing in addition to developing your communication and presentational skills through a project you will undertake. As a student of the MSc in Mathematics and Computing for Finance programme you will be fully supported to ensure that your project is best suited to support your future career plans.

Aims of MSc in Mathematics and Computing for Finance

Have in depth knowledge in stochastic analysis and parts of advanced real analysis. (Fourier analysis and Partial Differential Equations) as well as parts of numerical analysis which are central for applications to finance.

Have developed advanced computing skills being essential for handling problems relevant for a job on the finance markets.

Have, as a mathematician, a good understanding of finance markets.

Have developed skills needed to work in a highly inter-disciplinary profession, including advanced programming techniques and communication skills across the borders.

Modules

Please visit our website for a full description of modules for the MSc Mathematics and Computing for Finance.

Careers

The ability to think rationally and to process data clearly and accurately are highly valued by employers. Mathematics graduates earn on average 50% more than most other graduates. The most popular areas are the actuarial profession, the financial sector, IT, computer programming and systems administration, and opportunities within business and industry where employers need mathematicians for research and development, statistically analysis, marketing and sales.

Some of our students have been employed by AXA, BA, Deutsche Bank, Shell Research, Health Authorities and Local Government. Teaching is another area where maths graduates will find plenty of career opportunities.

Research

The results of the Research Excellence Framework (REF) 2014 show that our research environment (how the Department supports research staff and students) and the impact of our research (its value to society) were both judged to be 100% world leading or internationally excellent.

All academic staff in Mathematics are active researchers and the department has a thriving research culture.

Student profiles

"Further to my studies at Swansea University as a Master of Science graduate in Financial Mathematics, I am currently working at Deutsche Bank in London as part of the Structured Financial Services team providing client services for corporate lending and debt portfolios. The complex nature of the course has helped me become a logical decision maker and a highly skilled problem solver. These transferable skills are very useful in the world of Finance since the role is highly challenging working towards deadlines and structured transaction targets. My studies at Swansea University have also enriched me with leadership, motivational skills and have enhanced my communication skills. I work in a close team of 10 people within a large department which encourages a culture that strives towards learning and effective teamwork. I thoroughly enjoyed my time at Swansea University and cherish the many fond memories. I am so pleased to be expanding my horizon within a major financial centre."

Rhian Ivey, BSc Mathematics, MSc Mathematics and Computing for Finance

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MSc International Business focuses on the links between major business functions and their role in international business and commerce. Read more
MSc International Business focuses on the links between major business functions and their role in international business and commerce. You will develop knowledge of the special requirements for conducting business activities across national boundaries, relevant theories and their application in business. You will also have the opportunity to study at one of our European partner institutions and visit international companies through our integrated Study Abroad programme.

You will develop expertise in core areas of international business, strategic management and business economics. The wide range of elective modules will enable you to develop skills in a specific business function linked to international business (finance, marketing or operations management) or pursue related areas such as entrepreneurship and business sustainability.

Subject guide and modules

Core Modules:
-International Business
-Advanced Topics in International Business
-Strategic Management
-Economic Environment of Business
-Professional Development Programme
-Ethics in Academic Practice

Optional Modules - Choose four modules from the following list, subject to pre-requisites and restrictions on some modules and combinations:
-Intellectual Property Strategy & Management
-Business Finance
-International Finance
-Accounting for Non-Financial Managers
-Strategic Business Sustainability
-People & World Organisations
-Marketing Management
-International Marketing Management
-International Operations
-Strategic Global Outsourcing and Offshoring
-Operations Management
-CSR, Sustainable Development & Public Policy
-Entrepreneurial Strategies
-International Business strategy
-Entrepreneurial & Innovation in Emerging Economies

The programme then concludes with a dissertation project.

In addition to your Aston degree, you will have the opportunity to participate in our international study exchange programme. Studying with one of our partner institutions, the programme will enhance your international experience and communication skills, preparing you for a career in the international business environment.

Learning, teaching and assessment

The taught modules are assessed through a mixture of examinations and coursework. The taught element of the programme is complemented by a substantial piece of research leading to the completion of a dissertation.

Career opportunities

This degree will prepare you for a wide range of careers in businesses and organisations engaged in international activities, including consultancy, business analysis, marketing, business development, sales management, finance and purchasing. Recent graduate positions include:
-Assistant Advisor – Ernst & Young (Zimbabwe)
-Marketing Specialist – Bosch (China)
-Relationship Marketing Professional – IBM (India)
-Senior Analyst – Deutsche Bank (UK)
-Trainee Buyer – Tesco (UK)

Our international alumni ambassadors share their experiences of studying at Aston Business School with students all over the world who consider applying for a course at Aston.

Personal development

You will acquire skills in the following areas:
-Giving presentations
-Team working
-Report writing
-Negotiation
-IT skills

Core modules taught on this MSc will enable you to develop a wide range of business and transferable skills and knowledge demanded by graduate employers. These include:
-Interpreting financial statements covering income, balances and cash flow
-Budgetary control and analysing business costs using Cost Volume Profit and contribution analysis
-Econometric modelling of stochastic trends and performance forecasting
-International HRM including: cross-cultural communication; diversity management; and HR issues in expatriation/repatriation, acquisitions, mergers and joint ventures
-Developing marketing and pricing strategies, plans and marketing mix policies; international promotion strategies and distribution channels
-Internationalisation of production and trade, including: the international product life cycle; forms of international business activity; national competitive strategies; and economic integration
-International sectors, emerging and transition economies, cultural differences, international business strategies and strategic alliances
-Operational planning and management including: product, service and process design; choice of layout and technology; statistical stock control and MRPI
-Strategy processes and techniques including: value chain analysis, ‘make or buy’ decisions; implementing and managing strategic change in an international environment
-Innovation processes, innovation-based strategies and creating competitive advantage through innovation
-Multi-cultural teamwork and presentation skills developed through group-work
-Analytical skills developed through research dissertation

The wide range of elective modules available on this programme enables you to develop skills across a range of business functions including: marketing; HR; finance, investment and business law; operations; and international markets and investments.

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Computer Science is one of the drivers of technological progress in all economic and social spheres. Those graduating with an M.Sc. Read more

About Computer Science

Computer Science is one of the drivers of technological progress in all economic and social spheres. Those graduating with an M.Sc. in Computer Science are specialists in at least one field of computer science who have wide-ranging science-based methodological expertise.
Graduates are able to define, autonomously and comprehensively, computer science problems and their applications, structure them and build abstract models. Moreover, they are able to define and implement solutions that are at the state of the art of technology and science.

Features

– A broad, international and relevant selection of courses
– As a student, you will work on cutting-edge research projects
– Individual guidance in small learning groups
– Excellent enterprise relations maintained by the chairs and institutes
– Numerous partnerships with universities throughout the world, including a double degree programme with the Institut national des sciences appliquées de Lyon (INSA)

Syllabus

The programme offers the following five focus modules:
1) Algorithms and Mathematical Modelling
2) Programming and Software Systems
3) Information and Communication Systems
4) Intelligent Technical Systems
5) IT Security and Reliability
1) Algorithms and Mathematical Modelling: This module teaches you about determinstic and stochastic algorithms, their implementation, evaluation and optimisation. You will acquire advanced knowledge of computer-based mathematical methods – particularly in the areas of algorithmic algebra and computational stochastics – as well as developing an in-depth expertise in mathematical modelling and complexity analysis of discrete and continuous problems.
2) Programming and Software Systems: This module imparts modern methods for constructing large-scale software systems, as well as creating and using software authoring, analysis and optimisation tools. In this module you will consolidate your knowledge of the various programming paradigms and languages, the structure of language processing systems, and learn to deal with parallelism in program procedures.
3) Information and Communication Systems: In this module you will study the interactions of the classic computer science areas of information systems and computer networks. This focus area represents an answer to the problem of increasing volume and complexity of worldwide information distribution and networks, and for the growing requirements on quality and performance of computer communication. Additionally, you will learn to transfer database results to multimedia data.
4) Intelligent Technical Systems: In this module you are acquainted with digital image and signal processing, embedded systems and applications of intelligent technical systems in industrial and assistance systems, which are necessary for production automation and process control, traffic control, medical and building technology. You will learn to develop complex applications using computer systems and deal with topics such as image reconstruction, camera calibration, sensor data fusion and optical measurement technology.
5) IT Security and Reliability: This module group is concerned with security and reliability of IT systems, e.g. in hardware circuitry and communication protocols, as well as complex, networked application systems. To ensure the secure operation of these systems you will learn design methodology, secure architectures and technical implementation of the underlying components.

Language requirements

Unless English is your native language or the language of your secondary or undergraduate education, you should provide an English language certificate at level B2 CEFR, e.g. TOEFL with a minimum score of 567 PBT, 87 iBT or ITP 543 (silver); IELTS starting from 5.5; or an equivalent language certificate.

To facilitate daily life in Germany, it would be beneficial for you to have German language skills at level A1 CEFR (beginner’s level). If you do not have any German skills when starting out on the programme, you will complete a compulsory beginner’s German course during your first year of study.

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As well as giving a solid scientific understanding, the course also addresses commercial, ethical, legal and regulatory requirements, aided by extensive industrial contacts. Read more
As well as giving a solid scientific understanding, the course also addresses commercial, ethical, legal and regulatory requirements, aided by extensive industrial contacts.

Programme Structure

The MSc programmes in Biomedical Engineering are full-time, one academic year (12 consecutive months). The programmes consist of 4 core taught modules and two optional streams. Biomedical, Genetics and Tissue Engineering stream has 3 modules, all compulsory (individual course pages). The second option, Biomedical, Biomechanics and Bioelectronics Engineering stream consists of 5 modules. Students choosing this option will be required to choose 60 credit worth of modules.

The taught modules are delivered to students over two terms of each academic year. The taught modules are examined at the end of each term, and the students begin working on their dissertations on a part-time basis in term 2, then full-time during the months of May to September.

Core Modules
Biomechanics and Biomaterials (15 credit)
Design and Manufacture (15 credit)
Biomedical Engineering Principles (15 credit)
Innovation, Management and Research Methods (15 credit)
Plus: Dissertation (60 credit)

Optional Modules

60 credit to be selected from the following optional modules:
Design of Mechatronic Systems (15 credit)
Biomedical Imaging (15 credit)
Biofluid Mechanics (15 credit)
Artificial Organs and Biomedical Applications (15 credit)
Applied Sensors Instrumentation and Control (30 credit)

Module Descriptions

Applied Sensors Instrumentation and Control

Main topics:

Sensors and instrumentation – Sensor characteristics and the principles of sensing; electronic interfacing with sensors; sensor technologies – physical, chemical and biosensors; sensor examples – position, displacement, velocity, acceleration, force, strain, pressure, temperature; distributed sensor networks; instrumentation for imaging, spectroscopy and ionising radiation detection; 'lab-on-a-chip'.

Control – Control theory and matrix/vector operations; state-space systems, multi-input, multi-output (MIMO) systems, nonlinear systems and linearization. Recurrence relations, discrete time state-space representation, controllability and observability, pole-placement for both continuous and discrete time systems, Luenberger observer. Optimal control systems, Stochastic systems: random variable theory; recursive estimation; introduction to Kalman filtering (KF); brief look at KF for non-linear systems and new results in KF theory.

Artificial Organs and Biomedical Applications

Main topics include: audiology and cochlear implants; prostheses; artificial limbs and rehabilitation engineering; life support systems; robotic surgical assistance; telemedicine; nanotechnology.

Biofluid Mechanics

Main topics include: review of the cardiovascular system; the cardiac cycle and cardiac performance, models of the cardiac system, respiratory system and respiratory performance, lung models, physiological effects of exercise, trauma and disease; blood structure and composition, blood gases. oxygenation, effect of implants and prostheses, blood damage and repair, viscometry of blood, measurement of blood pressure and flow; urinary system: anatomy and physiology, fluid and waste transfer mechanisms, urinary performance and control, effects of trauma, ageing and disease; modelling of biofluid systems, review of mass, momentum and energy transfers related to biological flow systems, fluid mechanics in selected topics relating to the cardiovascular and respiratory systems; measurements in biomedical flows.

Biomechanics and Biomaterials

Main topics include: review of biomechanical principles; introduction to biomedical materials; stability of biomedical materials; biocompatibility; materials for adhesion and joining; applications of biomedical materials; implant design.

Biomedical Engineering Principles

Main topics include: bone structure and composition; the mechanical properties of bone, cartilage and tendon; the cardiovascular function and the cardiac cycle; body fluids and organs; organisation of the nervous system; sensory systems; biomechanical principles; biomedical materials; biofluid mechanics principles, the cardiovascular system, blood structure and composition, modelling of biofluid systems.

Biomedical Imaging

Principle and applications of medical image processing – Basic image processing operations, Advanced edge-detection techniques and image segmentation, Flexible shape extraction, Image restoration, 3D image reconstruction, image guided surgery

Introduction of modern medical imaging techniques – Computerized tomography imaging (principle, image reconstruction with nondiffracting sources, artifacts, clinical applications)

Magnetic resonance imaging (principle, image contrast and measurement of MR related phenomena, examples of contrast changes with changes of instrumental parameters and medical applications)

Ultrasound imaging (description of ultrasound radiation, transducers, basic imaging techniques: A-scan, B-scan and Doppler technique; clinical application)

Positron emission tomography (PET imaging) (principle, radioactive substance, major clinical applications)

Design and Manufacture

Main topics include: design and materials optimisation; management and manufacturing strategies; improving clinical medical and industrial interaction; meeting product liability, ethical, legal and commercial needs.

Design of Mechatronic Systems

Microcontroller technologies. Data acquisition. Interfacing to power devices. Sensors (Infrared, Ultrasonic, etc.). Optoelectronic devices and signal conditioning circuits. Pulse and timing-control circuits. Drive circuits. Electrical motor types: Stepper, Servo. Electronic Circuits. Power devices. Power conversion and power electronics. Line filters and protective devices. Industrial applications of digital devices.

Innovation and Management and Research Methods

Main topics include: company structure and organisation will be considered (with particular reference to the United Kingdom), together with the interfacing between hospital, clinical and healthcare sectors; review of existing practice: examination of existing equipment and devices; consideration of current procedures for integrating engineering expertise into the biomedical environment. Discussion of management techniques; design of biomedical equipment: statistical Procedures and Data Handling; matching of equipment to biomedical systems; quality assurance requirements in clinical technology; patient safety requirements and protection; sterilisation procedures and infection control; failure criteria and fail-safe design; maintainability and whole life provision; public and environmental considerations: environmental and hygenic topics in the provision of hospital services; legal and ethical requirements; product development: innovation in the company environment, innovation in the clinical environment; cash flow and capital provision; testing and validation; product development criteria and strategies.

Dissertation

The choice of Dissertation topic will be made by the student in consultation with academic staff and (where applicable) with the sponsoring company. The topic agreed is also subject to approval by the Module Co-ordinator. The primary requirement for the topic is that it must have sufficient scope to allow the student to demonstrate his or her ability to conduct a well-founded programme of investigation and research. It is not only the outcome that is important since the topic chosen must be such that the whole process of investigation can be clearly demonstrated throughout the project. In industrially sponsored projects the potential differences between industrial and academic expectations must be clearly understood.

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The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Read more
The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Technology is growing and evolving at an incredible speed, and both the rate of growth of data we generate and the devices we use to process it can only increase.

Data science is a growing and important field of study with a fast-growing number of jobs and opportunities within the private and public sector. The application of theory and methods to real-world problems and applications is at the core of data science, which aims especially to use and to exploit big data.

If you are interested in solving real-world problems, you like to develop skills to use smart devices efficiently, you want to use and to foster your understanding of mathematics, and you are interested and keen to use statistical techniques and methods to interpret data, MSc Data Science at Essex is for you. You study a balance of solid theory and practical application including:
-Computer science
-Programming
-Statistics
-Data analysis
-Probability

Our Department of Mathematical Sciences has an international reputation in many areas including semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology.

You also benefit from being taught in our School of Computer Science and Electronic Engineering, who are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of their research rated ‘world-leading’ or ‘internationally excellent’ (REF 2014).

The collaborative work between our departments has resulted in well-known research in areas including artificial intelligence, data analysis, data analytics, data mining, data science, machine learning and operations research.

Our expert staff

Our Department of Mathematical Sciences is a small but influential department, so our students and staff know each other personally. You never need an appointment to see your tutors and supervisors, just knock on our office doors – we are one of the few places to have an open-door policy, and no issue is too big or small.

The academic staff in our School of Computer Science and Electronic Engineering are conducting world-leading research in areas such as evolutionary computation, brain-computer interfacing, intelligent inhabited environments and financial forecasting.

Specialist staff working on data analytics include Dr Paul Scott, who researches data mining, models of memory and attention, and artificial intelligence, and Professor Maria Fasli, who researches data exploration, analysis and modelling of complex, structured and unstructured data, big data, cognitive agents, and web search assistants.

Specialist facilities

-Unique to Essex is our renowned Maths Support Centre, which offers help to students, staff and local businesses on a range of mathematical problems. Throughout term-time, we can chat through mathematical problems either on a one-to-one or small group basis
-We have our own computer labs for the exclusive use of students in the Department of Mathematical Sciences – in addition to your core maths modules, you gain computing knowledge of software including Matlab and Maple
-We have six laboratories that are exclusively for computer science and electronic engineering students
-All computers run either Windows 7 or are dual boot with Linux
-Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
-You have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
-We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors
-We host regular events and seminars throughout the year
-Collaborate with the Essex Institute of Data Analytics and Data Science (IADS) and the ESRC Business and Local Government (BLoG) Data Research Centre of the University of Essex
-The UK Data Archive and the Institute for Social and Economic Research (ISER) at Essex contribute to our internationally outstanding data science environment

Your future

With a predicted shortage of data scientists, now is the time to future-proof your career. Data scientists are required in every sector, carrying out statistical analysis or mining data on social media, so our course opens the door to almost any industry, from health, to government, to publishing.

Our graduates are highly sought after by a range of employers and find employment in financial services, scientific computation, decision making support and government, risk assessment, statistics, education and other sectors.

We also offer supervision for PhD, MPhil and MSc by Dissertation. We have an international reputation in many areas such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology, and our staff are strongly committed to research and to the promotion of graduate activities.

We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-Dissertation (optional)
-MSc Project and Dissertation (optional)
-Applied Statistics
-Machine Learning and Data Mining
-Modelling Experimental Data
-Text Analytics
-Artificial Neural Networks (optional)
-Bayesian Computational Statistics (optional)
-Big-Data for Computational Finance (optional)
-Combinatorial Optimisation (optional)
-High Performance Computing (optional)
-Natural Language Engineering (optional)
-Nonlinear Programming (optional)
-Professional Practice and Research Methodology (optional)
-Programming in Python (optional)
-Information Retrieval (optional)
-Data Science and Decision Making (optional)
-Research Methods (optional)
-Statistical Methods (optional)
-Stochastic Processes (optional)

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Take advantage of one of our 100 Master’s Scholarships to study Mathematics at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Read more
Take advantage of one of our 100 Master’s Scholarships to study Mathematics at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Postgraduate loans are also available to English and Welsh domiciled students. For more information on fees and funding please visit our website.

The MSc Mathematics course has been designed for students who wish to build on their BSc, extending their range of mathematics expertise across a broader spread of topics, and demonstrating their literature research skills through an extended dissertation.

Such a qualification will mark graduates out as having a broader and deeper understanding of mathematics, and the skills required to pursue a significant project with a high level of independence, presenting their results in a written report. This will give MSc Mathematics graduates an edge in the ever more competitive jobs market.

On the Mathematics course you will study different elements of mathematics in a broad sense - including mathematical elements of computing if desired - in addition to developing your research, project management, and written communication skills through a project you will undertake. As a student of MSc in Mathematics, you will be fully supported to ensure that your project further develops an excellent foundation for your future career plans.

Modules

Modules on the MSc Mathematics include:

• Algebraic coding theory
• Biomathematics
• Black-Scholes theory
• Data science
• Differential geometry
• Fourier analysis
• Ito calculus
• Lie theory
• Numerical analysis
• Partial differential equations
• Stochastic processes
• Statistical mechanics
• Topology

Please visit our website for a full description of modules for the MSc Mathematics.

On top of the Mathematics modules you study, you will also complete a dissertation as part of your studies.

Facilities

The Aubrey Truman Reading Room, located in the centre of the Department of Mathematics, houses the departmental library and computers for student use. It is a popular venue for students to work independently on the regular example sheets set by their lecturers, and to discuss Mathematics together.

Our main university library, Information Services and Systems (ISS), contains a notably extensive collection of Mathematics books.

Mathematics students will benefit from the £31m Computational Foundry for computer and mathematical sciences which will provide the most up-to-date and high quality teaching facilities featuring world-leading experimental set-ups, devices and prototypes to accelerate innovation and ensure students will be ready for exciting and successful careers. (From September 2018)

Careers

The ability to think rationally and to process data clearly and accurately are highly valued by employers. Mathematics graduates earn on average 50% more than most other graduates. The most popular areas are the actuarial profession, the financial sector, IT, computer programming and systems administration, and opportunities within business and industry where employers need mathematicians for research and development, statistically analysis, marketing and sales.

Some of our Mathematics students have been employed by AXA, BA, Deutsche Bank, Shell Research, Health Authorities and Local Government. Teaching is another area where Mathematics graduates will find plenty of career opportunities.

Research

The results of the Research Excellence Framework (REF) 2014 show that our research environment (how the Department supports research staff and students) and the impact of our research (its value to society) were both judged to be 100% world leading or internationally excellent.

All academic staff in Mathematics are active researchers and the department has a thriving research culture.

http://www.swansea.ac.uk/postgraduate/taught/science/mscmathematics/

Student Profile

"Further to my studies at Swansea University as a Master of Science graduate in Financial Mathematics, I am currently working at Deutsche Bank in London as part of the Structured Financial Services team providing client services for corporate lending and debt portfolios. The complex nature of the Mathematics course has helped me become a logical decision maker and a highly skilled problem solver. These transferable skills are very useful in the world of Finance since the role is highly challenging working towards deadlines and structured transaction targets. My studies at Swansea University have also enriched me with leadership, motivational skills and have enhanced my communication skills. I work in a close team of 10 people within a large department which encourages a culture that strives towards learning and effective teamwork. I thoroughly enjoyed my time at Swansea University and cherish the many fond memories. I am so pleased to be expanding my horizon within a major financial centre."

Rhian Ivey, BSc Mathematics, MSc Mathematics and Computing for Finance

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