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Masters Degrees (Discrete)

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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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Demand is growing worldwide for experts who have the technical skills to measure and manage financial risk. This masters degree prepares you to meet these requirements, equipping you to respond to emerging developments in the global capital and money markets. Read more

Demand is growing worldwide for experts who have the technical skills to measure and manage financial risk. This masters degree prepares you to meet these requirements, equipping you to respond to emerging developments in the global capital and money markets.

You’ll gain comprehensive knowledge of essential topics including the dynamics of financial markets, the causes and effects of financial risk and professional practices for measuring and managing portfolio risk. You can also choose optional modules allowing you to specialise in areas such as accounting, international banking and security investment analysis.

Learning from highly qualified teaching staff involved in world-class research in financial risk management, you’ll test your knowledge in genuine business scenarios during practical case study exercises and simulations. Thanks to our excellent links with industry and commerce, you’ll gain an understanding of the latest challenges in financial markets worldwide.

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 Risk Management; 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

The course builds your understanding of essential topics such as corporate finance and applies financial theory to practical problems. You’ll study financial modelling, risk and insurance, managing portfolio risk and derivatives. You’ll also undertake professional skills training throughout the year to help you develop the critical skills to apply your knowledge into the workplace.

Beyond these core areas of study, you’ll be able to specialise in areas that suit your career plans with your choice of optional modules. You’ll take at least one of Behavioural Finance or Discrete Time Finance, and can choose from topics such as forensic accounting and finance, international banking and finance or information and organisation design.

You’ll also develop your knowledge of research methods in finance and by the end of the year, you’ll be prepared to design and carry out your own research dissertation on a related topic of your choice, developing your ability to tackle the most pressing challenges currently facing the industry.

Course structure

Compulsory modules

You’ll study eight compulsory modules including your dissertation.

  • Portfolio Risk Management 15 credits
  • Corporate Finance 15 credits
  • Applied Finance 15 credits
  • Financial Modelling and Analysis 15 credits
  • Financial Derivatives 15 credits
  • Accounting and Finance Dissertation 45 credits
  • Critical Skills for the Finance Professional 15 credits

Optional modules

You’ll also take two optional modules. One of these must be either Behavioural Finance or Discrete Time Finance.

  • Security Investment Analysis 15 credits
  • Information and Organisation Design 15 credits
  • Forensic Accounting and Finance 15 credits
  • Behavioural Finance 15 credits
  • International Banking and Finance 15 credits
  • Financial Reporting and Analysis 15 credits
  • Discrete Time Finance 15 credits

For more information on typical modules, read Financial Risk Management 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. For example, students have developed their knowledge of financial markets through a one-week trading simulation delivered in partnership with Amplify Trading. Read more about our professional skills activities for 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

Graduates of this course are equipped with the skills and knowledge to pursue a range of career options within finance, including risk management, credit risk analysis, derivatives, insurance and finance research.

Previous graduates have gone on to work for a variety of financial institutions, investment banks, regulators and standard setters, international insurers, asset managers, underwriters and investment funds.

This degree is also an excellent qualification for individuals seeking to pursue further postgraduate research.

Careers support

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

We’re committed to offering a wide range of development and training opportunities. Recent activities have included field trips to corporate headquarters, CV building and interview sessions, professional risk management workshops and commercial awareness networking events.

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.



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The M.S. in Applied and Computational Mathematics program is designed to prepare students to join the workforce as a consulting mathematician or to pursue doctoral study in computational and industrial mathematics or other computationally-intensive field of study. Read more
The M.S. in Applied and Computational Mathematics program is designed to prepare students to join the workforce as a consulting mathematician or to pursue doctoral study in computational and industrial mathematics or other computationally-intensive field of study. 

Distinctive features include:

• Project-oriented approach in all courses - real-world industrial problems motivate coursework
• Team problem-solving practica emulate an industrial microcosm in which undergrads, grads, faculty, and industrial partners work together to study real-world problems
• Dual emphasis is placed on computational mathematics in the study of all real-world projects in each course of the curriculum

Students who complete the proposed program will:

• Acquire advanced knowledge of a wide variety of topics that span the realm of applied mathematics, including differential equations, discrete mathematics, probabilistic modelling, optimisation and statistical analysis. 
• Become adept at employing all steps of the mathematical modelling process in the analysis of real-world phenomena.
• Acquire expertise in using various forms of technology and in using, modifying, and creating numerical algorithms used in the analysis of real-world phenomena,
• Develop the valuable intuition of using the right tool for the right job.

Curriculum

Required modules:

MAT 500 Fundamentals of Applied Mathematics
MAT 548 Industrial Mathematics - Continuous Models
MAT 549 Industrial Mathematics - Discrete Models
MAT 552 Operations Research
MAT 553 Stochastic Modelling
MAT 554 Scientific Computing
MAT 555 Industrial Practicum - Continuous Models
STA 505 Mathematical Statistics I
MAT 556 Industrial Practicum - Discrete Models
STA 511 Intro Stat Computing & Data Management

Electives:

One three-credit elective must be chosen from one of the following

MAT 514 Theory Of Numbers
MAT 515 Algebra I
MAT 516 Algebra II
MAT 532 Geometry I
MAT 533 Geometry II
MAT 535 Topology
MAT 545 Real Analysis I
MAT 546 Real Analysis II
MAT 575 Complex Analysis I

An additional three credit elective must be chosen from any 500-level mathematics or statistics course not completed from the above list.

Collaborators and Local Industry

Representatives from the private sector consisting of mathematicians and scientists from large companies such as Vanguard, and PrimePay; employees of up-and-coming software companies such as iPipeline; and representatives of small privately-owned consulting firms and hedge fund companies, such as Wagner Associates and TFS Capital were consulted in the creation of this program.  We are continually expanding our network of collaborators within the private sector, with our newest collaborator being Stroud Preserve in West Chester.

Vastly different types of mathematical problems are studied by the members of this group.  Many have agreed to contribute to this M.S. program by way of delivering colloquium talks about their experiences in industry, and by creating and investigating real-world problems in our practicum courses.

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The course provides a detailed exposure to the context, issues and methods used to analyse the increasingly complex problems which are found in the defence environment and to support decision making. Read more

Course Description

The course provides a detailed exposure to the context, issues and methods used to analyse the increasingly complex problems which are found in the defence environment and to support decision making. It exposes the types of analysis and allows practical experience of tools and methods which are used, ranging from judgemental analysis through mathematical techniques to models and simulations. The course includes judgemental elicitation and analysis techniques, mathematical analysis methods (including optimisation), war gaming and combat modelling, logistics modelling and simulation methods. The use and utility of all the methods are explored through practical exercises and studies.

Course overview

The modular form of the course, consisting of a compulsory core and a selection of Standard and Advanced modules, enables you to select the course of study most appropriate to your particular requirements.

Standard modules normally comprise a week of teaching (or equivalent for distance learning) followed by a further week of directed study/coursework (or equivalent for part time and distance learning).

Advanced modules, which will enable you to explore some areas in greater depth, are two week (or equivalent for part time and distance learning) individual mini-projects on an agreed topic in that subject, which includes a written report and oral presentation.

- MSc students must complete a taught phase consisting of eight standard modules, which includes two core modules (Introduction to Operational Research Techniques and Decision Analysis), plus four advanced modules, followed by an individual thesis in a relevant topic. Thesis topics will be related to problems of specific interest to students and sponsors or local industry wherever possible.
- PgDip students are required to undertake the same taught phase as the MSc, but without the individual thesis.
- PgCert students must complete the core module (Introduction to Operational Research Techniques) together with five other modules; up to three of these may be advanced modules.

On successful completion of the course you will:

- Demonstrate a thorough understanding of the methods, techniques and tools for modelling defence problems and systems
- Be able to critically assess a range of approaches and methods to help support defence analysis and decision-making.

10 places are normally available for the full-time cohort.

The course is suitable for both military and civilian personnel, including those from defence industry and government departments

Individual Project

An individual research project on an agreed topic that allows you to demonstrate your technical expertise, independent learning abilities and critical appraisal skills.

Modules

Part-time students will typically not study as a cohort, but will follow an agreed individual programme of study, attending courses as convenient.
Advanced Modules, which typically comprise individual self-study, can be selected to follow on from any standard modules that have been chosen.
Standard Modules, which typically involve traditional classroom instruction and/or VLE-based delivery, can be chosen from the following:

Core -

Decision Analysis
Introduction to Operational Research Techniques

Optional -

Advanced Decision Analysis
Advanced Discrete and Continuous Simulation
Advanced Logistics Modelling
Advanced War Gaming and Combat Modelling
Applied Optimisation
Computational Statistics
Discrete and Continuous Simulation
Further Operational Research Techniques
Intelligent Systems
Intelligent Systems - Research Study
Logistics Modelling
Neural Networks
Optimisation
Statistical Analysis and Trials
War Gaming and Combat Modelling
Weapon System Performance Assessment

Assessment

Continuous assessment, written examinations, oral vivas and (MSc only) thesis.
Proportions of different assessment types will vary according to programme and elective options chosen. For an MSc these might typically comprise 15-24% continuous assessment (written and oral), 36-45% written examinations and 40% thesis/dissertation.

Career opportunities

Equips you for:

- Appointments within the armed forces or government, or in the defence related activities of commercial organisations.
- Further research leading to a PhD.

Further Information

For further information on this course, please visit our course webpage - http://www.cranfield.ac.uk/Courses/Masters/Military-Operational-Research

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The course addresses the design, development, procurement, use and management of models and simulations for applications in experimentation, training, testing, analysis and assessment of military forces, systems and equipment. Read more

Course Description

The course addresses the design, development, procurement, use and management of models and simulations for applications in experimentation, training, testing, analysis and assessment of military forces, systems and equipment.

Overview

On successful completion of the course you will be familiar with the technologies, methodologies, principles and terminology of Modelling and Simulation as used across defence, including the challenges and issues as well as the benefits. Through use of facilities such as the Simulation and Synthetic Environment Laboratory (SSEL), with its wide range of specialist applications, students will gain a broad understanding of modelling and simulation in areas such as training, acquisition, decision-support, analysis and experimentation.

•10 places are normally available for the full-time cohort
•The course is suitable for both military and civilian personnel, including those from defence industry and government departments

Start date: Full-time: annually in September. Part-time: by arrangement

Duration: Full-time MSc - one year, Part-time MSc - up to three years, Full-time PgCert - one year, Part-time PgCert - two years, Full-time PgDip - one year, Part-time PgDip - two years

English Language Requirements

Students whose first language is not English must attain an IELTS score of 6.5.

Course overview

The modular form of the course, consisting of a compulsory core and a selection of standard and advanced modules, enables each student to select the course of study most appropriate to their particular requirements.

Standard modules normally comprise a week of teaching (or equivalent for distance learning) followed by a further week of directed study/coursework (or equivalent for part time and distance learning).

Advanced modules, which enable students to explore some areas in greater depth, are two week (or equivalent for part time and distance learning) individual mini-projects on an agreed topic in that subject, which includes a written report and oral presentation.

- MSc students must complete a taught phase consisting of eight standard modules, which includes two core modules (Foundations of Modelling and Simulation and Networked and Distributed Simulation), plus four advanced modules, followed by an individual thesis in a relevant topic. Thesis topics will be related to problems of specific interest to students and sponsors of local industry wherever possible.

- PgDip students are required to undertake the same taught phase as the MSc, but without the individual thesis.

- PgCert students must complete the core module (Foundations of Modelling and Simulation) together with five other modules; up to three of these may be advanced modules.

Modules

Part-time students will typically not study as a cohort, but will follow an agreed individual programme of study, attending courses as convenient.
Advanced Modules, which typically comprise individual self-study, can be selected to follow on from any standard modules that have been chosen.
Standard Modules, which typically involve traditional classroom instruction and/or VLE-based delivery, can be chosen from the following:

Core:
- Foundations of Modelling and Simulation
- Networked and Distributed Simulation

Elective:
- Advanced Computer Graphics
- Advanced Discrete and Continuous Simulation
- Advanced Logistics Modelling
- Advanced Modelling and Simulation
- Advanced War Gaming and Combat Modelling
- Computational Statistics
- Computer Graphics
- Discrete and Continuous Simulation
- High Performance and Parallel Computing
- Intelligent Systems
- Intelligent Systems - Research Study
- Logistics Modelling
- Networked and Distributed Simulation Exercise
- Neural Networks
- Programming and Software Development in C
- Statistical Analysis and Trials
- War Gaming and Combat Modelling
- Weapon System Performance Assessment

Individual Project

An individual research project on an agreed topic that allows you to demonstrate your technical expertise, independent learning abilities and critical appraisal skills.

Assessment

Continuous assessment, written examinations, oral vivas and (MSc only) thesis.

Proportions of different assessment types will vary according to programme and modules taken. For an MSc these might typically comprise 15-24% continuous assessment (written and oral), 36-45% written examinations and 40% thesis/dissertation.

Career opportunities

Equips you for simulation-specific appointments within the armed forces or government, or in the defence related activities of commercial organisations.

For further information

On this course, please visit our course webpage http://www.cranfield.ac.uk/Courses/Masters/Defence-Simulation-and-Modelling

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This programme offers distinct specialisation areas in electronics. analogue VLSI design, bioelectronics and analogue and digital systems. Read more

This programme offers distinct specialisation areas in electronics: analogue VLSI design, bioelectronics and analogue and digital systems.

In analogue VLSI design, our facilities include a unique custom designed analogue integrated circuit specifically designed to support laboratory based teaching. Our advanced design and prototyping laboratories, advanced micro and nano fabrication facilities and state-of-the-art digital system laboratories use the latest industry standard software tools.

Alternatively, students may specialise in the emergent discipline of bioelectronics where our research and teaching interests include access to the fabrication facilities at the Scottish Microelectronics Centre. For students who wish to study a more general electronics course including digital systems, a prescribed course selection is available.

Programme structure

This programme is run over 12 months, with two semesters of taught courses, followed by a research project, leading to a masters thesis. There is a great deal of flexibility in our degree programme with three distinct streams as follows:

  • Analogue
  • Analogue and Digital
  • Bioelectronics

Analogue Stream

Compulsory courses:

  • Analogue IC Design
  • Analogue VLSI A
  • Discrete-time Signal Analysis (MSc)
  • Power Electronics (MSc)
  • Principles of Microelectronic Devices
  • Analogue Circuit Design
  • Analogue VLSI B
  • Research Project Preparation
  • Electronics: Project and Thesis

Optional courses: A choice of either :

  • Sigma Delta Data Converters

or

  • Microfabrication Techniques and
  • Technology and Innovation Management

Analogue and Digital Stream

Compulsory courses:

  • Analogue IC Design
  • Analogue VLSI A
  • Discrete-time Signal Analysis
  • Principles of Microelectronic Devices
  • Digital Systems Design
  • Digital Systems Laboratory
  • Research Project Preparation
  • Electronics: Project and Thesis

Optional courses: Either

  • Power Electronics or
  • Digital Systems Laboratory A

Plus one of:

  • Microfabrication Techniques
  • Modern Economic Issues in Industry
  • Technology and Innovation Management

And either:

  • Sigma Delta Data Converters

or

  • Embedded Mobile and Wireless Systems (EWireless)

Bioelectronics Stream

Compulsory courses:

  • Analogue Circuit Design
  • Analogue IC Design
  • Biosensors
  • Introduction to Bioelectronics (MSc)
  • Lab-on-Chip Technologies
  • Analogue VLSI A
  • Biosensors and Instrumentation
  • Microfabrication Techniques
  • Applications of Sensor and Imaging Systems
  • Research Project Preparation
  • Electronics: Project and Thesis

Optional courses: A choice of either:

  • Principles of Microelectronic Devices

or

  • Digital Systems Laboratory A

Career opportunities

You will gain significant practical experience in analogue and digital laboratories and become familiar with the latest industry standard design software and environments. Having been exposed to concepts such as design re-use and systems on chip technology, you will be able to cooperate with others in electronic system design. Recent graduates are now working as applications, design, field, test and validation engineering for employers such as BMW, Guangzhou Hangxin Avionics and Kongsberg Maritime.



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Our Music MLitt enables you to develop a flexible individual research programme in classical, popular, world, contemporary, early, folk and traditional music, applying approaches of interest to you (eg historiographic, theoretical, cultural, critical), under the supervision of specialists who are leaders in their field. Read more
Our Music MLitt enables you to develop a flexible individual research programme in classical, popular, world, contemporary, early, folk and traditional music, applying approaches of interest to you (eg historiographic, theoretical, cultural, critical), under the supervision of specialists who are leaders in their field.

This programme is primarily aimed at students who want to pursue independent musicological research, and who like the idea of first working on shorter research assignments (which can be on related or separate topics), before embarking on an extended final dissertation.

It provides an excellent foundation for continuing on to doctoral study. It is also a valuable qualification in its own right and can add a further dimension to your undergraduate degree, in a 3+1 model.

The MLitt is a modular research programme, which means that it is made up of discrete areas of study:
-Music research training (20 credits)
-Research assignments (80 credits)
-Dissertation (80 credits)

The research assignments are one of the programme’s distinctive features. They allow you to propose and research two or three separate projects (weighted at 40+40, or 20+20+40 credits), which may be connected or on discrete topics, and which lay the ground for your final dissertation. These are completed at the end of April (in year two for part time students) leaving the rest of the programme devoted to your dissertation.

Delivery

The course is delivered on the Newcastle campus (with options – under certain circumstances – for study abroad). All students are required to complete the Music Research Training module during their first two semesters of study and beyond this, study is based on one to one tutorials with supervisors appropriate to your research assignments or dissertation.

The subjects of your Research Assignments and final dissertation require a formal proposal and approval; if these are practicable and within our areas of expertise, these can all be on a topic of your own choosing.

The MLitt is designed primarily with scholarly types of research in mind, but can also accommodate some practical components where appropriate, for example performance in the context of performance practice research.

Facilities

We have outstanding specialist music facilities, including our £4.5m purpose built Music Studios, designed with performance, multimedia and studio-based work in mind.

Additional facilities include:
-Two professional grade recording studios
-A large student common room, including a work area with PCs featuring specialist music software
-A range of recently refurbished rehearsal spaces
-A full range of recently refurbished teaching facilities, including a 100-seat lecture theatre, two 50 seat lecture theatres and three 25-seater seminar rooms
-12 practice rooms with integrated recording facilities
-A dedicated postgraduate workspace
-A project room equipped with 5.1 mixing system

The University Library also has extensive music collections (including a number of important manuscript and microfilm collections), subscribes to many specialist Music journals, has access to a significant body of online resources, and is widely recognised for the supportive service it offers students and staff.

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Actuaries evaluate and manage financial risk. They apply advanced mathematical and statistical techniques to solve financial problems, assess the likelihood of a particular event and the possible financial costs. Read more

Actuaries evaluate and manage financial risk. They apply advanced mathematical and statistical techniques to solve financial problems, assess the likelihood of a particular event and the possible financial costs. The role of the actuary is increasingly important in the current economic climate, where volatile markets are contributing to financial uncertainty.

Our MSc Actuarial Finance will introduce you to essential business topics and enable you to apply your quantitative skills to solve complex business problems, such as risk management, insurance and derivatives. You’ll gain comprehensive knowledge in business areas such as accountancy, corporate finance and economics, as well as key analytical techniques, processes and models that have significant applications in actuarial finance.

The Business School has strong links with the actuarial profession and the insurance and pensions industry, giving you the opportunity to interact with practising actuaries and industry experts.

Academic excellence

This course is delivered in partnership with the University’s School of Mathematics. You’ll learn from leading academics based in both schools, giving you access to world-class expertise in both areas.

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).

You will also benefit from our strong relationships with the actuarial profession. This provides a connection to the latest developments in the industry and gives you a masters degree that is relevant to the contemporary environment.

Course content

You’ll take compulsory modules in corporate finance and economics allowing you to put your work into context and understand the importance of the role played by actuaries. At the same time, you’ll build your mathematical skills with classes in applied statistics and probability, as well as discrete time finance.

Building on this, you’ll study continuous time finance and gain an understanding of how different stochastic processes and survival models are applied to actuarial science. This will compliment your studies in topics such as accounting, professionalism and ethics, international investment and portfolio risk management.

Throughout the year you’ll build your professional skills with a core module focussing on critical practical skills that will prepare you for the workplace. At the end of the course, you’ll bring together your business, mathematical and professional knowledge to complete an independent project on a topic of your choice, which will demonstrate your ability to apply the skills you’ve gained.

Course structure

Compulsory modules

  • Portfolio Risk Management 15 credits
  • Corporate Finance 15 credits
  • Professionalism & Ethics for Actuaries 15 credits
  • International Investment 15 credits
  • Economics for Business 15 credits
  • Financial Reporting and Analysis 15 credits
  • Applied Statistics and Probability 15 credits
  • Discrete Time Finance 15 credits
  • Models in Actuarial Science 15 credits
  • Continuous Time Finance 15 credits
  • Projects in Actuarial Finance 30 credits

For more information on typical modules, read Actuarial Finance MSc in the course catalogue

Learning and teaching

Our tutors come from highly respected professional and academic backgrounds. We use a range of teaching methods so you can benefit from their expertise including lectures, workshops, seminars, computer simulations and tutorials.

In addition to the assessed modules and research dissertation, you benefit from professional training activities and employability workshops.

Independent study is also an important part of the course, allowing you to prepare for taught classes and sharpen your own research and critical thinking skills.

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, presentations and reflective logs. This diversity enables you to develop a broad range of skills as preparation for professional life.

Career opportunities

As a graduate of this course you will be able to demonstrate in-depth knowledge of core actuarial principles, and the numerical and analytical skills to succeed in this rapidly changing complex environment.

Graduates have entered successful careers in insurance, pensions, accountancy, risk management and consultancy. Job prospects are wide ranging and include careers as accountants, consultants, scheme and insurance actuaries, and employment in investment banking and risk management.

As a result of the high level of quantitative skills developed, you also gain an excellent grounding for PhD study in accounting, finance and economics.

Careers support

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

We’re committed to offering a wide range of development and training opportunities. Recent activities have included CV building and interview sessions, professional risk management workshops and commercial awareness networking events.

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.



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Make a difference in the lives and well-being of children and families while advancing your career with our PgD Specialist Community Public Health Nursing (SCPHN). Read more

Make a difference in the lives and well-being of children and families while advancing your career with our PgD Specialist Community Public Health Nursing (SCPHN). Communities have diverse needs and challenges – and we’ve designed a programme that will prepare you to meet them.

As a health visitor you will have the opportunity to serve the common good on a daily basis, promoting better health at an individual, group and community level. And at GCU, you’ll get a world-class education from an institution with a long history of training in the field.

We’ll help you develop the skills you need for effective and efficient care, with a child- and family-centred approach that empowers providers and patients alike. With a focus on preventive and anticipatory care, you’ll take on a unique role in health service, collaborating with and encouraging new parents and their families. 

You’ll use the lessons you learn in GCU’s friendly, diverse and thriving community to build rapport, offering support and encouragement wherever you go next.

What you will study

Research, Public Health Theory to Social Action, Enhanced Health Visiting Practice; Contemporary Leadership and Change, SCPHN Consolidation of Practice. Integrated throughout the modules is Nurse Prescribing V100.

Progression

This programme articulates as a discrete pathway within the MSc Health and Social Care. Attainment of the MSc Health and Social Care will be conferred following successful completion of a Masters Research Dissertation.

Graduate prospects

With a PgD Specialist Community Public Health Nurse (Health Visiting), you’ll be fully prepared to advance in a rewarding new career and make a positive impact on your community. Our graduates complete the programme with Part 3 of the Nursing and Midwifery Council register. This programme also offers a discrete pathway within the MSc Health and Social Care.



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Mathematics underlies some vital situations in practical life. Game theory, with roots in mathematics, statistics and economics, is routinely applied to understanding and predicting human behaviour. Read more
Mathematics underlies some vital situations in practical life.

Game theory, with roots in mathematics, statistics and economics, is routinely applied to understanding and predicting human behaviour. Problems of protection of digital information against piracy are closely related to aspects of set systems. And the RSA cryptosystem, used on computers all over the world, depends on classical results of number theory.

Our MSc Mathematics covers many aspects of discrete mathematics and their potential use in practice, and provides you with options in:
-Optimisation
-Machine learning
-Data mining
-Statistics

Our interdisciplinary research recognises that mathematics, including what can be very abstract mathematics, is an essential part of research in many other disciplines.

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

This course can also be studied to a PGDip level - for more information, please view this web-page: http://www.essex.ac.uk/courses/details.aspx?mastercourse=PG00538&subgroup=2

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.

Our staff have published several well-regarded text books and are world leaders in their individual specialisms, with their papers appearing in learned journals like Communications in Algebra, Studia Logica, International Journal of Algebra and Computation, SIAM Journal in Optimization, IEEE Evolutionary Computation, Computers and Operations Research, Ecology, Journal of Mathematical Biology, and Journal of Statistical Applications in Genetics and Molecular Biology.

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 host regular events and seminars throughout the year
-Our students run a lively Mathematics Society, an active and social group where you can explore your interest in your subject with other students

Your future

Key employability skills you gain from this course include analytic reasoning, problem solving, techniques of discrete mathematics and an understanding of application areas of these techniques, algorithm design and implementation, and data analysis.

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.

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Computer Science Departmental degree requirements for the master’s degree, which are in addition to those established by the College of Engineering and the Graduate School (http://graduate.ua.edu/), are as follows for Plan I and Plan II students. Read more
Computer Science Departmental degree requirements for the master’s degree, which are in addition to those established by the College of Engineering and the Graduate School (http://graduate.ua.edu/), are as follows for Plan I and Plan II students.

- Master of Science–Thesis Option (http://cs.ua.edu/graduate/ms-program/#thesis)
- Master of Science–Non-Thesis Option (http://cs.ua.edu/graduate/ms-program/#nonthesis)
- Timetable for the Submission of Graduate School Forms for an MS Degree (http://cs.ua.edu/graduate/ms-program/#timetable)

Visit the website http://cs.ua.edu/graduate/ms-program/

MASTER OF SCIENCE–THESIS OPTION (PLAN I):

30 CREDIT HOURS
Each candidate must earn a minimum of 24 semester hours of credit for coursework, plus a 6-hour thesis under the direction of a faculty member. Unlike the general College of Engineering requirements, graduate credit may not be obtained for courses at the 400-level.

Degree Requirements Effective Fall 2011

Credit Hours
The student must successfully complete 30 total credit hours, as follows:

- 24 hours of CS graduate-level course work

- 6 hours of CS 599 Master’s Thesis Research: Thesis Research.

- Completion of at least one 500-level or 600-level course in each of the four core areas (applications, software, systems and theory). These courses must be taken within the department and selected from the following:
Applications: CS 528, CS 535, CS 557, CS 560, CS 609, CS 615
Software: CS 503, CS 507, CS 515, CS 516, CS 534, CS 600, CS 603, CS 607, CS 614, CS 630
Systems: CS 526, CS 538, CS 567, CS 606, CS 613, CS 618
Theory: CS 500, CS 570, CS 575, CS 601, CS 602, CS 612

- No more than 12 hours from CS 511, CS 512, CS 591, CS 592, CS 691, CS 692 and non-CS courses may be counted towards the coursework requirements for the master’s degree. Courses taken outside of CS are subject to the approval of the student’s advisor.

- Additional Requirements -

- The student will select a thesis advisor and a thesis committee. The committee must contain at least four members, including the thesis advisor. At least two members are faculty of the Computer Science department, and at least one member must be from outside the Department of Computer Science.

- The student will develop a written research proposal. This should contain an introduction to the research area, a review of relevant literature in the area, a description of problems to be investigated, an identification of basic goals and objectives of the research, a methodology and timetable for approaching the research, and an extensive bibliography.

- The student will deliver an oral presentation of the research proposal, which is followed by a question-and-answer session that is open to all faculty members and which covers topics related directly or indirectly to the research area. The student’s committee will determine whether the proposal is acceptable based upon both the written and oral presentations.

- The student will develop a written thesis that demonstrates that the student has performed original research that makes a definite contribution to current knowledge. Its format and content must be acceptable to both the student’s committee and the Graduate School.

- The student will defend the written thesis. The defense includes an oral presentation of the thesis research, followed by a question-and-answer session. The student’s committee will determine whether the defense is acceptable.

- The student will complete an oral comprehensive exam. This exam is scheduled with the Department Head prior to the semester in which the student intends to graduate.

- Other requirements may be specified by the Graduate School (http://graduate.ua.edu/) and by the College of Engineering.

Degree Requirements Prior to Fall 2011

Credit hours

The student must successfully complete 30 total credit hours, as follows:

- 6 hours of CS 599 Master’s Thesis Research

- 24 hours of CS graduate-level course work with a grade of A or B, including the following courses completed at The University of Alabama:
At least 3 hours of theory courses (CS 500 Discrete math, CS 601 Algorithms, CS 602 Formal languages, CS 612 Data structures)

At least 3 hours of software courses (CS 600 Software engineering, CS 603 Programming languages, CS 607 Human-computer interaction, CS 614 Compilers, CS630 Empirical Software Engineering)

At least 3 hours of systems courses (CS 567 Computer architecture, CS 606 Operating systems, CS 613 Networks, CS 618 Wireless networks)

At least 3 hours of applications courses (CS 535 Graphics, CS 560 or 591 Robotics, CS 591 Security, CS 609 Databases)

- Additional Requirements -

- The student will select a thesis advisor and a thesis committee. The committee must contain at least four members, including the thesis advisor. At least two members are faculty of the Computer Science department, and at least one member must be from outside the Department of Computer Science.

- The student will develop a written research proposal. This should contain an introduction to the research area, a review of relevant literature in the area, a description of problems to be investigated, an identification of basic goals and objectives of the research, a methodology and timetable for approaching the research, and an extensive bibliography.

- The student will deliver an oral presentation of the research proposal, which is followed by a question-and-answer session that is open to all faculty members and which covers topics related directly or indirectly to the research area. The student’s committee will determine whether the proposal is acceptable based upon both the written and oral presentations.

- The student will develop a written thesis that demonstrates that the student has performed original research that makes a definite contribution to current knowledge. Its format and content must be acceptable to both the student’s committee and the Graduate School.

- The student will defend the written thesis. The defense includes an oral presentation of the thesis research, followed by a question-and-answer session. The student’s committee will determine whether the defense is acceptable.

- The student will complete an oral comprehensive exam. This exam is scheduled with the Department Head prior to the semester in which the student intends to graduate.

- Other requirements may be specified by the Graduate School (http://graduate.ua.edu/) and by the College of Engineering.

MASTER OF SCIENCE–NON-THESIS OPTION (PLAN II):

30 CREDIT HOURS
Each candidate must earn a minimum of 30 semester hours of credit for coursework, which may include a 3-hour non-thesis project under the direction of a faculty member. Unlike the general College of Engineering requirements, graduate credit may not be obtained for courses at the 400-level.

Degree Requirements Effective Fall 2011

The student must successfully complete 30 total credit hours, as follows:

- Completion of at least one 500-level or 600-level course in each of the four core areas (applications, software, systems and theory).
Applications: CS 528, CS 535, CS 557, CS 560, CS 609, CS 615
Software: CS 503, CS 507, CS 515, CS 516, CS 534, CS 600, CS 603, CS 607, CS 614, CS 630
Systems: CS 526, CS 538, CS 567, CS 606, CS 613, CS 618
Theory: CS 500, CS 570, CS 575, CS 601, CS 602, CS 612

- No more than 12 hours from CS 511, CS 512, CS 591, CS 592, CS 691, CS 692 and non-CS courses may be counted towards the coursework requirements for the master’s degree. Courses taken outside of CS are subject to the approval of the student’s advisor.

- The student may elect to replace 3 hours of course work with 3 hours of CS 598 Research Not Related to Thesis: Non-thesis Project. This course should be proposed in writing in advance, approved by the instructor, and a copy placed in the student’s file. The proposal should specify both the course content and the specific deliverables that will be evaluated to determine the course grade.

- Additional Requirements -

- The student will complete an oral comprehensive exam. This exam is scheduled with the Department Head prior to the semester in which the student intends to graduate.

- Other requirements may be specified by the Graduate School and by the College of Engineering.

Degree Requirements Prior to Fall 2011

Credit hours

The student must successfully complete 30 total credit hours of CS graduate-level course work with a grade of A or B, as follows:

- The following courses will be completed at The University of Alabama:
At least 3 hours of theory courses (CS 500 Discrete math, CS 601 Algorithms, CS 602 Formal languages, CS 612 Data structures)

At least 3 hours of software courses (CS 600 Software engineering, CS 603 Programming languages, CS 607 Human-computer interaction, CS 614 Compilers, CS630 Empirical Software Engineering)

At least 3 hours of systems courses (CS 567 Computer architecture, CS 606 Operating systems, CS 613 Networks, CS 618 Wireless networks)

At least 3 hours of applications courses (CS 535 Graphics, CS 560 or 591 Robotics, CS 591 Security, CS 609 Databases)

- The student may elect to replace 3 hours of course work with 3 hours of CS 598 Research Not Related to Thesis: Non-thesis Project. This course should be proposed in writing in advance, approved by the instructor, and a copy placed in the student’s file. The proposal should specify both the course content and the specific deliverables that will be evaluated to determine the course grade.

- Additional Requirements -

- The student will complete an oral comprehensive exam. This exam is scheduled with the Department Head prior to the semester in which the student intends to graduate.

- Other requirements may be specified by the Graduate School and by the College of Engineering.

TIMETABLE FOR THE SUBMISSION OF GRADUATE SCHOOL FORMS FOR AN MS DEGREE
This document identifies a timetable for the submission of all Graduate School paperwork associated with the completion of an M.S. degree

- For students in Plan I students only (thesis option) after a successful thesis proposal defense, you should submit the Appointment/Change of a Masters Thesis Committee form

- The semester before, or no later than the first week in the semester in which you plan to graduate, you should “Apply for Graduation” online in myBama.

- In the semester in which you apply for graduation, the Graduate Program Director will contact you about the Comprehensive Exam.

Find out how to apply here - http://graduate.ua.edu/prospects/application/

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The ideas of applied mathematics pervade several applications in a variety of businesses and industries as well as government. Sophisticated mathematical tools are increasingly used to develop new models, modify existing ones, and analyze system performance. Read more

Program overview

The ideas of applied mathematics pervade several applications in a variety of businesses and industries as well as government. Sophisticated mathematical tools are increasingly used to develop new models, modify existing ones, and analyze system performance. This includes applications of mathematics to problems in management science, biology, portfolio planning, facilities planning, control of dynamic systems, and design of composite materials. The goal is to find computable solutions to real-world problems arising from these types of situations.

The master of science degree in applied and computational mathematics provides students with the capability to apply mathematical models and methods to study various problems that arise in industry and business, with an emphasis on developing computable solutions that can be implemented. The program offers options in discrete mathematics, dynamical systems, and scientific computing. Students complete a thesis, which includes the presentation of original ideas and solutions to a specific mathematical problem. The proposal for the thesis work and the results must be presented and defended before the advisory committee.

Curriculum

Several options available for course sequence:
-Discrete mathematics option
-Dynamical systems option
-Scientific computing option

See website for individual module details.

Other entry requirements

-Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
-Submit a personal statement of educational objectives.
-Have an undergraduate cumulative GPA of 3.0 or higher.
-Submit two letters of recommendation, and complete a graduate application.
-International applicants whose primary language is not English must submit scores from the Test of English as a Foreign Language (TOEFL). A minimum score of 550 (paper-based) or 79-80 (Internet-based) is required. International English Language Testing System (IELTS) scores are accepted in place of the TOEFL exam. Minimum scores vary; however, the absolute minimum score required for unconditional acceptance is 6.5. For additional information about the IELTS, please visit http://www.ielts.org. Those who cannot take the TOEFL will be required to take the Michigan Test of English Proficiency at RIT and obtain a score of 80 or higher.
-Although Graduate Record Examination (GRE) scores are not required, submitting them may enhance a candidate's acceptance into the program.
-A student may also be granted conditional admission and be required to complete bridge courses selected from among RIT’s existing undergraduate courses, as prescribed by the student’s adviser. Until these requirements are met, the candidate is considered a nonmatriculated student. The graduate program director evaluates the student’s qualifications to determine eligibility for conditional and provisional admission.

Additional information

Student’s advisory committee:
Upon admission to the program, the student chooses an adviser and forms an advisory committee. This committee oversees the academic aspects of the student’s program, including the selection of a concentration and appropriate courses to fulfill the program’s requirements.

Cooperative education:
Cooperative education enables students to alternate periods of study on campus with periods of full-time, paid professional employment. Students may pursue a co-op position after their first semester. Co-op is optional for this program.

Part-time study:
The program is ideal for practicing professionals who are interested in applying mathematical methods in their work and enhancing their career options. Most courses are scheduled in the late afternoon or early evening. The program may normally be completed in two years of part-time study.

Nonmatriculated students:
A student with a bachelor’s degree from an approved undergraduate institution, and with the background necessary for specific courses, may take graduate courses as a nonmatriculated student with the permission of the graduate program director and the course instructor. Courses taken for credit may be applied toward the master’s degree if the student is formally admitted to the program at a later date. However, the number of credit hours that may be transferred into the program from courses taken at RIT is limited for nonmatriculated students.

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About the programme. In addition to being a science in its own right, mathematics plays a fundamental role in the quantitative areas of practically all other academic disciplines, particularly in the natural sciences, engineering, business administration, economics, medicine and psychology. Read more

About the programme

In addition to being a science in its own right, mathematics plays a fundamental role in the quantitative areas of practically all other academic disciplines, particularly in the natural sciences, engineering, business administration, economics, medicine and psychology. Mathematical results permeate nearly all facets of life and are a necessary prerequisite for the vast majority of modern technologies – and as our IT systems become increasingly powerful, we are able to mathematically handle enormous amounts of data and solve ever more complex problems.

Special emphasis is placed on developing students' ability to formalise given problems in a way that facilitates algorithmic processing as well as enabling them to choose or develop, and subsequently apply, suitable algorithms to solve problems in an appropriate manner. The degree programme is theoretical in its orientation, with strongly application-oriented components. Studying this programme, you can gain advanced knowledge in the mathematical areas of Cryptography, Computer Algebra, Algorithmic Algebra and Geometry, Image and Signals Processing, Statistics and Stochastic Simulation, Dynamical Systems and Control Theory as well as expert knowledge in Computer Science fields such as Data Management, Machine Learning and Data Mining.

Furthermore, you will have the chance to learn how to apply your knowledge to tackle problems in areas as diverse as Marketing, Predictive Analytics, Computational Finance, Digital Humanities, IT Security and Robotics.

Programme syllabus

The core modules consist of two mathematics seminars and the presentation of your master's thesis.The compulsory elective modules are divided into eight module groups:

1)   Algebra, Geometry and Cryptography

This module group imparts advanced results in the areas of algebra and geometry, which constitute the fundament for algorithmic calculations, particularly in cryptography but also in many other mathematical areas.

2)   Mathematical Logic and Discrete Mathematics

The theoretical possibilities and limitations of algorithm-based solutions are treated in this module group.

3)   Analysis, Numerics and Approximation Theory

Methods from the fields of mathematical analysis, applied harmonic analysis and approximation theory for modelling and approximating continuous and discrete data and systems as well as efficient numerical implementation and evaluation of these methods are the scope of this module group.

4) Dynamical Systems and Optimisation

Dynamical systems theory deals with the description of change over time. This module group is concerned with methods used for the modelling, analysis, optimisation and design of dynamical systems, as well as the numerical implementation of such techniques.

5) Stochastics, Statistics

This module group deals with methods for modelling and analysing complex random phenomena as well as the construction, analysis and optimisation of stochastic algorithms and techniques used in statistical data analysis.

6) Data Analysis and Data Management and Programming

This module group examines the core methods used in computer science for the analysis of data of heterogeneous modalities (e.g. multimedia data, social networks and sensor data) and for the realisation of data analysis systems.

7) Applications

In this module group, you will practise applying the mathematical methods learned in module groups 1 to 6 to real-world applications such as Marketing, Predictive Analytics and Computational Finance.

8) Key Competencies and Language Training

In this module group, you will choose seminars that develop your non-subject-specific skills, such as public speaking and academic writing and other soft skills; you may also undertake internships. This serves to complement your technical expertise gained during your degree studies and helps to prepare you for your professional life after university.



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This MSc programme is run by Heriot-Watt University jointly with The University of Edinburgh. Upon graduation, you will receive a degree certificate bearing crests of both universities. Read more

This MSc programme is run by Heriot-Watt University jointly with The University of Edinburgh. Upon graduation, you will receive a degree certificate bearing crests of both universities.

It provides you with expertise in financial mathematics, including stochastic calculus, and a range of practical techniques for analysing financial markets. You will also learn quantitative skills for developing and managing risk that are in high demand.

Programme structure

This programme involves two taught semesters of compulsory and optional courses, followed by a dissertation project.

The teaching is shared between Heriot-Watt University (HW) and The University of Edinburgh (UoE). The timetable is arranged so that you spend three days a week at the Heriot-Watt Riccarton Campus and two days a week at University of Edinburgh’s King’s Buildings Campus.

The dissertation project is either carried out with an industrial partner (e.g. Aberdeen Asset Management, Moody’s Analytics and Lloyds Banking Group) or supervised by academics at Heriot-Watt University. The programme typically involves the following courses.

Compulsory courses:

  • Credit Risk Modelling (HW)
  • Derivatives Markets (HW)
  • Derivative Pricing and Financial Modelling (HW)
  • Discrete-Time Finance (UoE)
  • Financial Markets (HW)
  • Special Topics 1 (UoE)
  • Special Topics 2 (HW)
  • Stochastic Analysis in Finance (UoE)

Option courses:

  • Optimization Methods in Finance (UoE)
  • Financial Econometrics (HW)
  • Portfolio Theory (HW)
  • Numerical Techniques of Partial Differential Equations (HW)
  • Simulation (UoE)
  • Statistical Methods (HW)
  • Statistical Inference (HW)
  • Time Series Analysis (HW)
  • Stochastic Control and Dynamic Asset Allocation (UoE)

Work placements/internships

Adding depth to your learning, our work placement programme puts you at the heart of organisations such as Aberdeen Asset Management, Moody’s Analytics and Lloyds Banking Group.

Career opportunities

Graduates typically work in major financial institutions or continue their studies by joining PhD programmes.



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This masters programme covers the advanced mathematics that has revolutionised finance since the works of Black, Scholes and Merton in the early seventies. Read more
This masters programme covers the advanced mathematics that has revolutionised finance since the works of Black, Scholes and Merton in the early seventies. This programme is aimed at those students who are passionate about mathematics and driven to make a career amongst the many and varied financial institutions throughout the world.

The programme, which is part of the Maxwell Institute for Mathematical Sciences, the joint research institute of mathematical sciences at the University of Edinburgh and Heriot-Watt University, provides an intensive training in the mathematical ideas and tools vital to the finance industry. By developing essential new mathematical concepts, especially in stochastic calculus, and placing the mathematics in the contexts of financial markets, derivative pricing and credit risk, the programme equips students for a range of exciting and potentially lucrative career opportunities.

The programme is delivered jointly between Heriot-Watt University and the University of Edinburgh. This means you will be enrolled as a student at both univerities and benefit from access to all the services and facilities each university has to offer.

Teaching is delivered by renowned academics from both Heriot-Watt and the University of Edinburgh - some classes will therefore take place at Heriot-Watt's campus and others at the University of Edinburgh campus. Successful students will graduate with a degree awarded jointly by Heriot-Watt and the University of Edinburgh and both names will appear on the graduation certificate.

Programme content

Core courses

Derivatives Markets
Derivative Pricing and Financial Modelling
Financial Markets
Discrete-Time Finance
Stochastic Analysis in finance
Credit Risk Modelling
Special Topics, including industry lead projects

Options

Statistical Methods
Financial Econometrics
Time Series Analysis
Modern Portfolio Theory
Optimisation Methods in Finance
Numerical Methods for PDEs
Simulation in Finance
Deterministic Optimisation Methods in Finance

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