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Masters Degrees in Computational Mathematics, United Kingdom

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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Mathematics at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017). Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Mathematics at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

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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Financial Mathematics is a branch of Mathematics where advanced mathematical and statistical methods are developed for and applied to financial markets and financial management. Read more

Overview

Financial Mathematics is a branch of Mathematics where advanced mathematical and statistical methods are developed for and applied to financial markets and financial management. Its main aims are to quantify and hedge risks in the financial marketplace.

Effective computational methods are crucial for the successful use of mathematical modelling in finance. The MSc in Financial and Computational Mathematics is designed to reflect this combination of knowledge and skills so that its graduates are well equipped to enter the competitive job markets of quantitative finance and related fields.

The course is focused on computational techniques and mathematical modelling used in the financial industry and on the required background in finance. The course is provided by the School of Mathematical Sciences with valuable input from the School of Economics. To ensure that the degree keeps pace with changes in employer expectations and employment opportunities, the course has its own advisory board which consists of leading experts from the financial industry and academia.

Key facts:

- The School of Mathematical Sciences is one of the largest and strongest mathematics departments in the UK, with over 60 full-time academic staff.
- In the latest independent Research Assessment Exercise, the school ranked 8th in the UK in terms of research power across the three subject areas within the School of Mathematical Sciences (pure mathematics, applied mathematics, statistics and operational research).
- In the last independent Teaching Quality Assessment, the School scored 23 out of 24.
- The course has its own advisory board (see below) consisting of leading experts from the financial industry and academia.
- The course is offered in collaboration with the School of Economics.

Module details

Core modules include: financial mathematics, advanced financial mathematics, scientific computing and c++, advanced scientific computing, financial mathematics dissertation.

Optional Stream 1 (Maths/Stats and Computing): Optimisation, Time Series and Forecasting, Statistical Foundations.

Optional Stream 2A: Econometic Theory, Financial and Macro Econometrics, Time Series Econometrics, Mathematics for Engineering Management, Game Theory.

Optional Stream 2B: Microeconomic Analysis, Financial Economics, Options and Futures Markets, Mathematics for Engineering Management, Game Theory.

English language requirements for international students

IELTS: 6.5 (with no less than 6.0 in any element)

Further information



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Programme description. Computational Mathematics, in particular the physical applied areas and the theory and implementation of numerical methods and algorithms, have wide-ranging applications in both the public and private sectors. Read more

Programme description

Computational Mathematics, in particular the physical applied areas and the theory and implementation of numerical methods and algorithms, have wide-ranging applications in both the public and private sectors. More recently, in this era of ubiquitous and cheap computing power, there has been an explosion in the number of problems that require us to understand processes by modelling them, and to use data sets that are large. Thus the subject of Computational Mathematics has become increasingly prominent. Consequently there is high demand also for computational modellers and data scientists. This programme concentrates on the overlap and synergy between these fields.

Programme structure

The programme consists of 120 credits of courses in total during Semesters 1 and 2, followed by a 60 credit dissertation which is completed during the Summer. The courses taken will be dependent on the availability of courses each year which may be subject to change as curriculum develops to reflect a modern degree programme.

The first semester is composed of a combination of compulsory and optional courses. The compulsory courses will build strong applied mathematical and computational foundations. The curriculum is completed with optional courses in related subjects such as statistics and optimization.

The second semester is again composed of a combination of compulsory and optional courses, building on the skills gained in Semester 1. The compulsory courses include Research Skills, which will prepare you for the Summer Dissertation Project. The optional courses cover a wide range of areas including, for example, data science, high performance computing, and related disciplines such as Informatics and Physics.

The 60 credit individual dissertation will take the form of a supervised research-style project on a topic proposed by a staff member of the Applied and Computational Mathematics group. The aim of the project is to provide practical experience and skills for tackling scientific problems which require both computational approaches and mathematical insight. This will include identifying and applying appropriate mathematical and numerical techniques, interpreting the results, and presenting the conclusions.

Career opportunities

This programme will provide training in the tools and techniques of mathematical modelling and scientific computing, and will provide students with skills for problem solving using modern techniques of applied mathematics.



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Computational mathematics is relevant to almost every science, engineering, business and finance discipline, as well as many industrial sectors. Read more
Computational mathematics is relevant to almost every science, engineering, business and finance discipline, as well as many industrial sectors. This means it’s an excellent choice if you’re considering broadening your career options whilst studying a discipline that is in high demand in an ever technological era. Studying this MSc is also excellent preparation for academic research in any area where computational techniques play a significant role, offering you a clear route to further study at PhD level.

The course delivers an outstanding combination of advanced applicationoriented mathematical concepts and computational methodologies. Broad in scope and genuinely multidisciplinary in nature, it’s based on solid and well-founded mathematical theory.

Subject modules are carefully designed to be accessible to anyone with a good first degree in mathematics or in science and engineering subjects which have a strong mathematical component.

Here at the University of Derby, our teaching team has wide-ranging expertise in contemporary areas of mathematics and their applications to modern society. You’ll be inspired by academics and practitioners who have experience of harnessing mathematics and computing to address real-world problems.

You’ll benefit from flexible study options to fit in with your lifestyle. If you’re already in employment, you can gain this MSc through part time study while developing your knowledge and technical competence in ways that are directly relevant to your job.

You’ll study modules such as:

Optimisation
Scientific Computing
Stochastic Processes
Networks and Algorithms
Nonlinear System Dynamics
Studying at Masters Level and Research Methods
Independent Scholarship

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Accurate and efficient scientific computations lie at the heart of most cross-discipline collaborations. It is key that such computations are performed in a stable, efficient manner and that the numerics converge to the true solutions, dynamics of the physics, chemistry or biology in the problem. Read more
Accurate and efficient scientific computations lie at the heart of most cross-discipline collaborations. It is key that such computations are performed in a stable, efficient manner and that the numerics converge to the true solutions, dynamics of the physics, chemistry or biology in the problem.

The programme closely follows the structure of our Applied Mathematical Sciences MSc and will equip you with the skill to perform efficient accurate computer simulations in a wide variety of applied mathematics, physics, chemical and industrial problems.

Students will take a total of 8 courses, 4 in each of the 1st and 2nd Semesters followed by a 3-month Project in the summer. A typical distribution for this programme is as follows:

Core courses

Modelling and Tools;
Stochastic Simulation;
Applied Linear Algebra;
Numerical Analysis;

Optional Courses

Dynamical Systems;
Optimization;
Partial Differential Equations;
Numerical Analysis of ODEs;
Applied Mathematics;
Statistical Methods;
Functional Analysis;
Software Engineering Foundations;
Mathematical Biology and Medicine;
Biologically Inspired Computation;
Advanced Software Engineering;
Geometry;
Bayesian Inference;

Typical project subjects

Simulation of Granular Flow and Growing Sandpiles;
Finite Element Discretisation of ODEs and PDEs;
Domain Decomposition;
Computational Spectral Theory;
Mathematical Modelling of Crime;
Mathematical Modelling of Micro-electron Mechanical Systems.
Can we Trust Eigenvalues on a Computer?

The final part of the MSc is an extended project in computational mathematics, giving the opportunity to investigate a topic in some depth guided by leading research academics from our 5-rated mathematics and statistics groups.

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The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods. Read more

The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods.

Currently graduates in this field are expected to have a working knowledge of advanced computational finance (including construction of algorithms and programming skills) as well as a sound knowledge of the theory of Probability and Stochastic Analysis. These are the core theories needed in the modern valuation of complex financial instruments.

This MSc programme delivers:

  • a flexible programme of study relevant to the needs of employers such as: top investment banks, hedge funds and asset management firms
  • a solid knowledge in financial derivative pricing, risk management and portfolio management
  • the transferable computational skills required by the modern quantitative finance world

Programme structure

You must obtain a total of 180 credits to be awarded the MSc. Over semesters 1 and 2, you will take compulsory courses worth a total of 85 credits and optional courses worth a further 35 credits. Successful performance in these courses (assessed through coursework or examinations or both) allows you to start work on a three-month dissertation project, worth 60 credits, for the award of the MSc degree.

There are two streams: the Financial stream and the Computational stream.

Compulsory courses previously offered include (both streams):

  • Stochastic Analysis in Finance (20 credits, semester 1)
  • Discrete-Time Finance (10 credits, semester 1)
  • Finance, Risk and Uncertainty (10 credits, semester 1)
  • Object-Oriented Programming with Applications (10 credits, semester 1)
  • Risk-Neutral Asset Pricing (10 credits, semester 2)
  • Stochastic Control and Dynamic Asset allocation (10 credits, semester 2)
  • Monte Carlo Methods (5 credits, semester 2)
  • Numerical Methods for Stochastic Differential Equations (5 credits, semester 2)
  • Research-Linked Topics (10 credits, semesters 1 and 2)

Additional compulsory courses for Computational Stream previously offered include:

  • Numerical Partial Differential Equations (10 credits, semester 2)
  • Time Series (10 credits, semester 2)

Additional compulsory courses for Financial stream previously offered include:

  • Financial Risk Theory (10 credits, semester 2)
  • Optimization Methods in Finance (10 credits, semester 2)

Optional courses previously offered include:

  • Numerical Partial Differential Equations (10 credits, semester 2)
  • Time Series (10 credits, semester 2)
  • Financial Risk Theory (10 credits, semester 2)
  • Optimization Methods in Finance (10 credits, semester 2)
  • Integer and Combinatorial Optimization (10 credits, semester 2)
  • Bayesian Theory (10 credits, semester 1)
  • Credit Scoring (10 credits, semester 2)
  • Python Programming (10 credits, semester 1)
  • Scientific Computing (10 credits, semester 1)
  • Programming Skills - HPC MSc (10 credits, semester 1)
  • Parallel Numerical Algorithms - HPC MSc (10 credits, semester 1)
  • Applied Databases (10 credits)

Work placements/internships

We work closely with the Scottish Financial Risk Academy (SFRA) to offer a number of short courses led by industry (part of our Research-Linked Topics) and to provide the opportunity to our best students to write their dissertations during placements with financial services companies.

Learning outcomes

At the end of this programme you will have:

  • developed personal communications skills, initiative, and professionalism within a mathematical context
  • developed transferable skills that maximise your prospects for future employment, including writing, oral presentation, team-working, numerical and logical problem-solving, planning and time-management
  • improved your ability to convey ideas in an articulate fashion, to build upon previous mathematical training and further develop logic and deductive skills
  • mastered standard and advanced mathematical tools used to solve applied problems relevant to the mathematical finance industry
  • developed quantitative and computational skills for the proficient fulfilment of tasks in the financial sector

Career opportunities

Graduates can expect to go on to work in major financial institutions or to continue their studies by joining PhD programmes.



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This Masters degree provides you with knowledge of advanced finance concepts, whilst developing your quantitative, mathematical and research skills. Read more

This Masters degree provides you with knowledge of advanced finance concepts, whilst developing your quantitative, mathematical and research skills.

Taught by experienced academics based in both Leeds University Business School and the School of Mathematics, you’ll cover key topics including financial derivative pricing, discrete and continuous time models, risk management and portfolio optimisation, as well as statistical methods for finance.

You will be equipped with a rare combination of mathematical skills and the latest business finance knowledge, which is highly sought after in the financial sector by banks, investment and consultancy companies. It’s also excellent preparation if you’re interested in pursuing further academic research.

This course is ideal if you’ve previously studied finance, economics, mathematics, physics or computing, and are interested in applying your skills to financial markets.

Academic excellence

As a student, you will be able to access the knowledge of our advanced specialist research units, which also have strong links with leading institutions in the US, Europe and Asia. These include the Centre for Advanced Study in Finance (CASIF), the Institute of Banking and Investment (IBI) and the Credit Management Research Centre (CMRC).

This research makes an important contribution to your learning on the MSc Financial Mathematics; you will benefit from a curriculum that is informed by the latest knowledge and critical thinking.

You will also benefit from our strong relationships with the finance, credit and accounting professions. This provides a connection to the latest practitioner and policy developments, giving you a masters degree that is relevant to the contemporary environment.

Course content

In your first semester you’ll develop a broad understanding of corporate finance and how financial theory relates to practice in business and financial markets. This will put your mathematical studies into context while you develop your skills in applied statistics and probability, optimisation methods and discrete time finance.

You’ll build on these skills in topics such as continuous time finance, risk management and computational methods. You’ll also gain specialist knowledge in topics that suit your career ambitions such as risk and insurance, actuarial science and behavioural finance.

The programme will improve your research skills and allow you to study different research methodologies, including those employed by our own leading academics. This will prepare you for your dissertation – an independent research project on a topic of your choice that you’ll submit by the end of the year.

Course structure

Compulsory modules

  • Corporate Finance 15 credits
  • Dissertation in Financial Mathematics 30 credits
  • Applied Statistics and Probability 15 credits
  • Discrete Time Finance 15 credits
  • Continuous Time Finance 15 credits
  • Risk Management 15 credits
  • Computations in Finance 15 credits
  • Optimisation Methods for Finance 15 credits

Optional modules

You'll also take two optional modules.

  • Security Investment Analysis 15 credits
  • Portfolio Risk Management 15 credits
  • Behavioural Finance 15 credits
  • Financial Derivatives 15 credits
  • International Investment 15 credits
  • Models in Actuarial Science 15 credits

For more information on typical modules, read Financial Mathematics MSc in the course catalogue

Learning and teaching

We use a variety of teaching and learning methods to help you make the most of your studies. These will include lectures, seminars, workshops, online learning and tutorials. Independent study is also vital for this course allowing you to prepare for taught classes and sharpen your own research and critical skills.

In addition to the assessed modules and research dissertation, you benefit from professional training activities and employability workshops. Thanks to our links with major companies across the business world, you can also gain a practical understanding of key issues.

Recent activities have included CV building and interview sessions, professional risk management workshops and commercial awareness events. For example, students have developed their knowledge of financial markets through a one-week trading simulation. Read more about professional development activities for postgraduate finance students.

Assessment

Assessment methods emphasise not just knowledge, but essential skills development too. They include formal exams, group projects, reports, computer simulation exercises, essays and written assignments, group and individual presentations.

This diversity enables you to develop a broad range of skills as preparation for professional life.

Career opportunities

You have various opportunities open to you as a Financial Mathematics graduate, including: quantitative analysis, risk management, investment banking, financial consultancy, insurance, accounting and academia.

Previous graduates have gone on to secure employment with Allianz (London), AstraZeneca, Barclays, Cathay Life Insurance, CITIC Group, Commerzbank, Deloitte, First Direct, Gaz de France, HSBC, KPMG, Moody’s, PricewaterhouseCoopers, Royal Bank of Scotland, RSA and UK Government Actuary’s Department.

Careers support

We help you to achieve your career ambitions by providing professional development support and training as part of the course. You benefit from the support of a professional development tutor, who will work with you to develop the important professional skills that employers value.

Read more about our careers and professional development support.

The University of Leeds Careers Centre also provides a range of help and advice to help you plan your career and make well-informed decisions along the way, even after you graduate. Find out more at the Careers website



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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.

About this degree

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 two core modules (30 credits), four to six optional modules (60 to 90 credits), up to two elective modules (up to 30 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 (15 credits)
  • Statistical Modelling and Data Analysis (15 credits)

Optional modules

Students must choose 15 credits from Group One Options. Of the remaining credits, students must choose a minimum of 30 and a maximum of 60 from Group Two, 15 credits from Group Three and a maximum of 30 credits from Electives.

Group One Options (15 credits)

  • Graphical Models (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)

Group Two Options (30 to 60 credits)

  • Advanced Deep Learning and Reinforcement Learning (15 credits)
  • Advanced Topics in Machine Learning (15 credits)
  • Applied Machine Learning (15 credits)
  • Approximate Inference and Learning in Probabilistic Models (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Introduction to Deep Learning (15 credits)
  • Machine Vision (15 credits)
  • Statistical Natural Language Processing (15 credits)

Group Three Options (15 credits)

  • Applied Bayesian Methods (15 credits)
  • Statistical Design of Investigations (15 credits)
  • Statistical Inference (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

A list of acceptable elective modules is available on the Departmental page.

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.

Further information on modules and degree structure is available on the department website: Computational Statistics and Machine Learning MSc

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.

Recent career destinations for this degree

  • Data Scientist, Interpretive
  • Software Engineer, Google
  • Data Scientist, YouGov
  • Research Engineer, DeepMind
  • PhD in Computer Science, UCL

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.

Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.

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 the 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.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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Postgraduate degree programme in Financial Engineering Masters/MSc. Read more

Postgraduate degree programme in Financial Engineering Masters/MSc:

MSc Financial Engineering is a multi-disciplinary field that involves the application of the computational engineering, software engineering, and computer programming skills, as well as the underlying mathematical and statistical theories to the analysis and management of financial opportunities. Students will receive the most advanced computational and programming techniques which help them advance quickly in the field.

Course details

Financial engineering is a multi-disciplinary field that involves the application of computational engineering, software engineering, and computer programming skills, as well as the underlying mathematical and statistical theories to the analysis and management of financial opportunities. 

The programme is for strong (1st, 2.1 or equivalent) graduates from programmes in mathematics, or programmes with advanced mathematical components, and who wish to pursue a career in quantitative analysis in economic or financial sectors with state-of-art mathematical methods, computational skills and programming expertise.

Related links

Learning and teaching

In the Autumn and Spring semesters, you will take masters-level courses in both computational methods and programming and statistical methods in economics, as well as computer science courses such as the Computer Science Workshop, in addition to the core quantitative finance and further quantitative finance modules which are needed for a career in financial engineering and computational finance. 

In the summer you will undertake a project, working with research leaders in mathematics and computer sciences. This will provide directly relevant training for a career in academic, and quantitative analysis in financial industry. A key component will be training specifically in independent study and research, an essential skill for quantitative analyst.

Employability

Career opportunities

This programme gives an ideal preparation for a career in quantitative analysis in economic or financial sectors with state-of-art mathematical methods, computational skills and programming expertise. The School’s graduates work in a wide variety of fields in governmental and multi-national organisations.

University Careers Network

Preparation for your career should be one of the first things you think about as you start university. Whether you have a clear idea of where your future aspirations lie or want to consider the broad range of opportunities available once you have a Birmingham degree, our Careers Network can help you achieve your goal.

Our unique careers guidance service is tailored to your academic subject area, offering a specialised team (in each of the five academic colleges) who can give you expert advice. Our team source exclusive work experience opportunities to help you stand out amongst the competition, with mentoring, global internships and placements available to you. Once you have a career in your sights, one-to-one support with CVs and job applications will help give you the edge.

If you make the most of the wide range of services you will be able to develop your career from the moment you arrive.



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The MSc in Mathematics and Foundations of Computer Science, run jointly by the. Mathematical Institute. and the. Department of Computer Science. Read more

The MSc in Mathematics and Foundations of Computer Science, run jointly by the Mathematical Institute and the Department of Computer Science, focuses on the interface between pure mathematics and theoretical computer science. 

The mathematical side concentrates on areas where computers are used, or which are relevant to computer science, namely algebra, general topology, number theory, combinatorics and logic. Examples from the computing side include computational complexity, concurrency, and quantum computing. Students take a minimum of five options and write a dissertation.

The course is suitable for those who wish to pursue research in pure mathematics (especially algebra, number theory, combinatorics, general topology and their computational aspects), mathematical logic, or theoretical computer science. It is also suitable for students wishing to enter industry with an understanding of the mathematical and logical design and concurrency.

The course will consist of examined lecture courses and a written dissertation. The lecture courses will be divided into two sections:

  • Section A: Mathematical Foundations
  • Section B: Applicable Theories

Each section shall be divided into schedule I (basic) and schedule II (advanced). Students will be required to satisfy the examiners in at least two courses taken from section B and in at least two courses taken from schedule II. The majority of these courses should be given in the first two terms. 

During Trinity term and over the summer students should complete a dissertation on an agreed topic. The dissertation must bear regard to course material from section A or section B, and it must demonstrate relevance to some area of science, engineering, industry or commerce.

It is intended that a major feature of this course is that candidates should show a broad knowledge and understanding over a wide range of material. Consequently, each lecture course taken will receive an assessment upon its completion by means of a test based on written work. Students will be required to pass five courses, that include two courses from section B and two at the schedule II level - these need not be distinct - and the dissertation.

The course runs from the beginning of October through to the end of September, including the dissertation.



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There is a high demand from industry worldwide, including from substantial sectors in the UK, for graduates with skills at the interface of traditional statistics and machine learning. Read more

There is a high demand from industry worldwide, including from substantial sectors in the UK, for graduates with skills at the interface of traditional statistics and machine learning. MRes graduates benefit from the department's excellent links in finding employment; this programme is also ideal preparation for a research career.

About this degree

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the areas of computational statistics and machine learning (CSML). Students will have the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research. They also undertake a nine-month research project which enables the department to more fully assess their research potential.

Students undertake modules to the value of 180 credits.

The programme consists of three core modules (30 credits), three optional modules (45 credits) and a dissertation (105 credits).

Core modules

  • Investigating Research
  • Researcher Professional Development

Optional modules

Student select three modules from the following:

  • Advanced Deep Learning and Reinforcement Learning
  • Advanced Topics in Machine Learning
  • Applied Bayesian Methods
  • Approximate Inference and Learning in Probabilistic Models
  • Graphical Models
  • Information Retrieval and Data Mining
  • Introduction to Deep Learning
  • Introduction to Machine Learning
  • Inverse Problems in Imaging
  • Machine Vision
  • Probabilistic and Unsupervised Learning
  • Selected Topics in Statistics
  • Statistical Computing
  • Statistical Inference
  • Statistical Models and Data Analysis
  • Supervised Learning

Dissertation/report

All students undertake an independent research project which culminates in a substantial dissertation.

Teaching and learning

The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by laboratory work with assistance from demonstrators. Students liaise with their academic or industrial supervisor to choose a study area of mutual interest for the research project. Performance is assessed by unseen written examinations, coursework and the research dissertation.

Further information on modules and degree structure is available on the department website: Computational Statistics and Machine Learning MRes

Careers

Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, and Chicago, as well as at UCL. Similarly, CSML graduates now work in companies in Germany, Iceland, France and the US in large-scale data analysis. The finance sector is also particularly interested in CSML graduates.

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, while in London there are many companies looking to understand their customers better who have hired CSML graduates. Computational statistics and machine learning skills are in particular demand in areas including finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. CSML graduates have obtained PhD positions both in machine learning and related large-scale data analysis, and across the sciences.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning, having co-ordinated the PASCAL European Network of Excellence which represents the largest network of machine learning researchers in Europe.

UCL Computer Science graduates are particularly valued by the world’s leading organisations in internet technology, finance, and related information areas, as a result of the department’s strong international reputation and ideal location close to the City of London.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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Overview. Scientific computing is a new and growing discipline in its own right. It is concerned with harnessing the power of modern computers to carry out calculations relevant to science and engineering. Read more

Overview

Scientific computing is a new and growing discipline in its own right. It is concerned with harnessing the power of modern computers to carry out calculations relevant to science and engineering.

By its very nature, scientific computing is a fundamentally multidisciplinary subject. The various application areas give rise to mathematical models of the phenomena being studied.

Examples range in scale from the behaviour of cells in biology, to flow and combustion processes in a jet engine, to the formation and development of galaxies. Mathematics is used to formulate and analyse numerical methods for solving the equations that come from these applications.

Implementing the methods on modern, high performance computers requires good algorithm design to produce efficient and robust computer programs. Competence in scientific computing thus requires familiarity with a range of academic disciplines. The practitioner must, of course, be familiar with the application area of interest, but it is also necessary to understand something of the mathematics and computer science involved.

Whether you are interested in fundamental science, or a technical career in business or industry, it is clear that having expertise in scientific computing would be a valuable, if not essential asset. The question is: how does one acquire such expertise?

This course is one of a suite of MScs in Scientific Computation that are genuinely multidisciplinary in nature. These courses are taught by internationally leading experts in various application areas and in the core areas of mathematics and computing science, fully reflecting the multidisciplinary nature of the subject. The courses have been carefully designed to be accessible to anyone with a good first degree in science or engineering. They are excellent preparation either for research in an area where computational techniques play a significant role, or for a career in business or industry.

Key facts:

- This course is offered in collaboration with the School of Computer Science.

- It is one of a suite of courses focusing on scientific computation.

- The School of Mathematical Sciences is one of the largest and strongest mathematics departments in the UK, with over 60 full-time academic staff.

- In the latest independent Research Assessment Exercise, the school ranked 8th in the UK in terms of research power across the three subject areas within the School of Mathematical Sciences (pure mathematics, applied mathematics, statistics and operational research).

Modules

Advanced Techniques for Differential Equations

Computational Linear Algebra

Operations Research and Modelling

Programming for Scientific Computation

Scientific Computation Dissertation

Simulation for Computer Scientists

Stochastic Financial Modelling

Variational Methods

Vocational Mathematics

Data Mining Techniques and Applications

Mathematical Foundations of Programming

English language requirements for international students

IELTS: 6.0 (with no less than 5.5 in any element)

Further information



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The course provides you with a strong mathematical background with the skills necessary to apply your expertise to the solution of real finance problems. … Read more

The course provides you with a strong mathematical background with the skills necessary to apply your expertise to the solution of real finance problems. You will develop skills so that you are able to formulate a well posed problem from a description in financial language, carry out relevant mathematical analysis, develop and implement an appropriate numerical scheme and present and interpret these results.

The course lays the foundation for further research in academia or for a career as a quantitative analyst in a financial or other institution.

You will take three introductory courses in the first week. The introductory courses cover partial differential equations, probability and statistics and MATLAB.

The first term focuses on compulsory core material, offering 80 hours of lectures and 40 hours of classes/practical. The core courses are as follows:

  • Stochastic Calculus
  • Financial Derivatives
  • Numerical Methods I - Monte-Carlo
  • Numerical Methods I - Finite Differences
  • Statistics and Financial Data Analysis
  • Financial Programming with C++ 1

In the second term, three streams are offered; each stream consists of 32 hours of lectures and 16 hours of classes/practical. The Tools stream is mandatory and you will also take either the Modelling stream or the Data-driven stream.

Modelling stream

  • Exotic derivatives
  • Stochastic volatility, jump diffusions
  • Commodities
  • Fixed income

Data-driven stream

  • Asset pricing and inefficiency of markets
  • Market microstructure and trading
  • Algorithmic trading
  • Advanced financial data analysis
  • Machine learning
  • Python

Tools stream

  • Numerical methods 2 - Monte Carlo methods
  • Numerical methods 2 - Finite differences
  • Calibration
  • Optimisation
  • Introduction to stochastic control

As well as the streams, the course includes a compulsory one-week (24 hours of lectures) intensive module on quantitative risk management which is to be held in/around the week before the third term.

The third term is dedicated to a dissertation project which is to be written on a topic chosen in consultation with your supervisor.

The second component of the financial computing course, Financial Computing with C++ 2 (24 hours of lectures and practicals in total), is held shortly after the third term.

The examination will consist of the following elements:

  • two written examinations and one take-home project, each of two hours' duration - the written examinations will cover the core courses in mathematical methods and numerical analysis
  • a written examination on the Modelling stream or a written examination and a computer-based practical examination on the Data-driven stream
  • a written examination assessing the Tools stream
  • a take-home project assessing the course in quantitative risk management
  • two practical examinations assessing two courses in financial computing with C++.

Graduate destinations

MSc graduates have been recruited by prominent investment banks and hedge funds. Many past students have also progressed to PhD-level studies at leading universities in Europe and elsewhere.



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Photonics enables the majority of world-wide communications, almost all data on the Internet exists in optical form at some point during the transmission. Read more
Photonics enables the majority of world-wide communications, almost all data on the Internet exists in optical form at some point during the transmission. Photonics also has many other applications in medicine, sensing biology and chemistry. On this course you will combine taught material which is specifically relevant to your research topic with advanced knowledge obtained from working in a world leading research environment. The course enhances employability in many industries including communications, civil engineering and bio-technology.

This programme trains aspiring academic and industrial research scientists. The programme consists of a training aspect of taught components (equivalent to 3 modules approximately during the first 3 months) and a significant interdisciplinary research project.

Research topics are chosen by students from a list of topics mirroring the computational expertise of the Mathematics group and the newly founded Systems Analytics Research Institute at Aston, with Computationally oriented projects in for example, biology, optimization, pattern analysis and physics as well as finance. The projects are supervised by academics from the Mathematics group and the Systems Analytics Research Institute at Aston.

The MSc integrates a taught component of three modules over approximately three months (30 credits) with a substantial individual research project lasting nine months (150 credits)

Core modules:
-Computational Mathematics (10 Credits)
-Research skills and Professional Development (10 Credits)
-Specialist/Technical Research Skills (10 Credits)

Exemption from these modules may be arranged through APL or APEL, provided this is done prior to enrolment.

Personal Development

Students will have the opportunity to carry out a research project in collaboration with industry and through this learn about the application context of technology and the interaction between research and business.

Facilities & Equipment

Students will get access to appropriate computational and experimental facilities.

Aston University offers Wi-Fi connection, modern lecture/tutorial rooms, computer labs, lounge area, good learning resources and publications are available in the library and many electronically.

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Non-equilibrium processes underpin many challenging problems across the natural sciences. The mission of the Non-Equilibrium Systems. Read more

Non-equilibrium processes underpin many challenging problems across the natural sciences. The mission of the Non-Equilibrium Systems: Theoretical Modelling, Simulation and Data-Driven Analysis MSc is to provide students an insight into cross-disciplinary approaches to non-equilibrium systems, focussing on the three key strands of theoretical modelling, simulation and data-driven analysis. It draws on a broad range of expertise in Mathematics, Physics, Chemistry, Informatics, Computational and Systems Biomedicine, Earth and Environmental Sciences at King’s College London. This course is an ideal study pathway for graduates who wish to work in research and development in an academic or industrial environment. 

Key benefits

  • Located in the heart of London, giving unparalleled access to research facilities.
  • You will be studying innovative modules covering Non-Equilibrium Systems.
  • Research-led study programme taught by staff who are recognised leaders in their field.
  • We offer a friendly and supportive learning environment with small class sizes.

Description

The Non-Equilibrium Systems: Theoretical Modelling, Simulation and Data-Driven Analysis MSc programme aims to provide you with deeper insights into non-equilibrium processes using theoretical modelling, simulation and data-driven analysis and prepare you for roles within active research.

You will complete the course in one year, studying September to September and taking a combination of required and optional modules totalling 180 credits. The broad range of optional modules will allow you to develop a study pathway that reflect your interests.

We also offer the opportunity to explore an additional zero-credit module called Foundations for CSM and CANES, designed as a refresher module covering vital mathematics and physics skills. 

For more information visit http://www.kcl.ac.uk/innovation/groups/noneqsys/Handbook/MSc%20Handbook/CANES-MSc-Programme/CANES-MSc.aspx

Course purpose

For graduates with excellent undergraduate or equivalent qualifications in any relevant discipline (including; mathematics, physics, chemistry, engineering, materials science, biophysics, geophysical sciences and computer science) who want to work in research and development in an academic or industrial environment. The programme aim is to develop deeper insights into non-equilibrium processes using theoretical modelling, simulation and data-driven analysis and prepare students ideally for active research.

Course format and assessment

We use lectures, seminars and group tutorials to deliver most of the modules on the programme. You will also be expected to undertake a significant amount of independent study.

Each module in your degree is worth a number of credits. You are expected to spend approximately 10 hours of effort for each credit (so for a typical module of 15 credits this means 150 hours of effort). These hours cover every aspect of the module: lectures, tutorials, labs (if any), independent study based on lecture notes, tutorial preparation and extension, coursework preparation and submission, examination revision and preparation, and examination.

Assessment

Assessment methods will depend on the modules selected. The primary methods of assessment for this course are written examinations and coursework. You may also be assessed by reports, problem sets and oral presentations.

Career prospects

Leads to PhD study or careers in teaching, industrial research or the financial sector.

Sign up for more information. Email now

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