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The primary aim of this course is to educate you to MSc level in the theoretical and practical aspects of mathematical problem solving, mathematical model development, creating software solutions and communication of results. Read more
The primary aim of this course is to educate you to MSc level in the theoretical and practical aspects of mathematical problem solving, mathematical model development, creating software solutions and communication of results.

This course provides training in the use and development of reliable numerical methods and corresponding software. It aims to train graduates with a mathematical background to develop and apply their skills to the solution of real problems. It covers the underlying mathematical ideas and techniques and the use and design of mathematical software. Several application areas are examined in detail. It develops skills in mathematical problem-solving, scientific computing, and technical communication.

Training is also provided in general computing skills, mathematical typsetting, mathematical writing, desktop and web-based mathematical software development, and the use of computer languages and packages including Mathematica, parallel computing, C#, 3D graphics and animation, and visualisation.

The MSC is now available fully online and can be taken over 12 months full time or 24 months part time.

Visit the website: http://www.ucc.ie/en/ckr36/

Course Details

By the end of the course, you will be able to:

- use the description of a real world problem to develop a reasonable mathematical model in consultation with the scientific literature and possibly experts in the area
- carry out appropriate mathematical analysis
- select or develop an appropriate numerical method and write a computer programme which gives access to a sensible solution to the problem
- present and interpret these results for a potential client or a non-technical audience

Modules

Module descriptions - http://www.ucc.ie/calendar/postgraduate/Masters/science/page05.html#mathematical

AM6001 Introduction to Mathematica (5 credits)
AM6002 Numerical Analysis with Mathematica (5 credits)
AM6003 Cellular Automata (5 credits)
AM6004 Applied Nonlinear Analysis (Computational Aspects) (5 credits)
AM6005 Modelling of Systems with Strong Nonlinearities (5 credits)
AM6006 Mathematical Modelling of Biological Systems with Differential Equations (5 credits)
AM6007 Object Oriented Programming with Numerical Examples (10 credits)
AM6008 Developing Windowed Applications and Web-based Development for Scientific Applications (5 credits)
AM6009 3D Computer Graphics and Animation for Scientific Visualisation (5 credits)
AM6010 Topics in Applied Mathematical Modelling (5 credits)
AM6011 Advanced Mathematical Models and Parallel Computing with Mathematica (5 credits)
AM6012 Minor Dissertation (30 credits)

Format

The course places great emphasis on hands-on practical skills. There is a computer laboratory allocated solely for the use of MSc students. PCs are preloaded with all the required software and tools. Online students are expected to have a suitable PC or laptop available; all required software is provided for installation to faciliate course work. Online teaching hours, involving lecturers, tutorials and practical demonstrations, usually take place in the morninbg. The rest of the time, you are expected to do exercises, assignments and generally put in the time required to acquire key skills.

Assessment

Continuous assessment is the primary method of examining. In each module, typically 40% of the marks are available for take-home assignments and the remaining 60% of marks are examined by a practical computer-based examination. Final projects are read and examined by at least two members of staff.

For more information, please see the Book of Modules 2015/2016 - http://www.ucc.ie/calendar/postgraduate/Masters/science/page05.html#mathematical

Careers

Quantitative graduates with software skills are in high demand in industry according to the Governments Expert Group on Future Skills Needs. Demand for these skills is project to rise over the coming years not just in Ireland but in the EU and globally. Graduates have recently secured jobs in the following areas: banking, financial trading, consultancy, online gambling firms, software development, logistics, data analysis and with companies such as AIB, McAfee, Fexco, DeCare Systems, MpStor, the Tyndall Institute, Matchbook.com, First Derivatives and KPMG.

How to apply: http://www.ucc.ie/en/study/postgrad/how/

Funding and Scholarships

Information regarding funding and available scholarships can be found here: https://www.ucc.ie/en/cblgradschool/current/fundingandfinance/fundingscholarships/

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The objective of this programme of study is to prepare professionals able to deal with complex systems using sophisticated mathematical tools, yet with an engineering attitude. Read more

Mission and goals

The objective of this programme of study is to prepare professionals able to deal with complex systems using sophisticated mathematical tools, yet with an engineering attitude. It harmonises a solid scientific background with a command of advanced methodologies and technologies. The programme is characterised by a continuous synergy between Applied Mathematics and Engineering disciplines- The students may choose among three specialisations:
- Computational Science and Engineering
- Applied Statistics
- Quantitative Finance

See the website http://www.polinternational.polimi.it/educational-offer/laurea-magistrale-equivalent-to-master-of-science-programmes/mathematical-engineering/

Career opportunities

The professional opportunities offered by this course are rather ample and varied: engineering consultancy companies that deal with complex computational problems; manufacturing or civil engineering companies where analyses based on the use of advanced mathematical tools are needed; banks, insurance companies and financial institutions making use of quantitative finance for risk analysis or forecast; companies that require statistical interpretation and the processing of complex data, or the simulation of different scenarios; public and private research institutes and laboratories.

Eligible students

Students holding a Bachelor degree in Mathematical Engineering, or in a related area with a solid background in the core disciplines of the programme, i.e. Applied Mathematics, Computer Science, Applied Physics or other Engineering disciplines are eligible for application. In particular, eligible students' past studies must include courses in different areas of Engineering (among Informatics, Economics & Business Organization, Electrotechnics, Automation, Electronics, Applied Physics, Civil Engineering) for at least 25% of the overall courses, as well as courses in different areas of Mathematics (Mathematical Analysis, Linear Algebra, Geometry, Probability, Statistics, Numerical Analysis, Optimization) for at least 33% of the overall courses.
The following tracks are available:
1. Computational Science and Engineering
2. Applied Statistics
3. Quantitative Finance

Eligible students must clearly specify the track they are applying for in their motivation letter.

Presentation

See http://www.polinternational.polimi.it/uploads/media/Mathematical_Engineering.pdf
The Master of Science in Mathematical Engineering (MSME) aims to form an innovative and flexible professional profile, endowed with a wide spectrum of basic scientific notions and engineering principles, together with a deep knowledge of modern pure and applied mathematical techniques. MSME is characterized by a continuous synergy between Mathematics and Engineering methods, oriented to the modelling, analysis and solution of complex planning, control and management problems, and provides the students with the possibility to face problems from various scientific, financial and/or technological areas. The MSME graduates can find employment in Engineering companies specialized in handling complex computational problems, requiring a multidisciplinary knowledge; in companies manufacturing industrial goods for which design analysis based on the use of advanced mathematical procedures are required; in service societies, banks, insurance companies, finance or consultant agencies for the statistical interpretation and the simulation of complex situations related to the analysis of large number of data (e.g. management and optimization of services, data mining, information retrieval) or for handling financial products and risk management; in public and private institutions. The programme is taught in English.

Subjects

Three main tracks available:
1. Computational Science for Engineering
Real and functional analysis; algorithms and parallel programming; numerical and theoretical analysis for partial differential equations; fluid mechanics; computational fluid dynamics advanced programming techniques for scientific computing;

2. Statistics
Real and functional analysis; algorithms and parallel programming; stochastic dynamical models; applied statistics, model identification and data analysis; Bayesian statistics

3. Mathematical Finance
Real and functional analysis; algorithms and parallel programming; stochastic differential equations; mathematical finance; financial engineering; model identification and data analysis.

In the motivation letter the student must clearly specify the track he/she is applying for.

See the website http://www.polinternational.polimi.it/educational-offer/laurea-magistrale-equivalent-to-master-of-science-programmes/mathematical-engineering/

For contact information see here http://www.polinternational.polimi.it/educational-offer/laurea-magistrale-equivalent-to-master-of-science-programmes/mathematical-engineering/

Find out how to apply here http://www.polinternational.polimi.it/how-to-apply/

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In this Master's specialisation, mathematicians working in areas pertinent to (theoretical) computer science, like algebra and logic, and theoretical computer scientists, working in areas as formal methods and theorem proving, have joined forces to establish a specialisation in the Mathematical Foundations of Computer Science. Read more
In this Master's specialisation, mathematicians working in areas pertinent to (theoretical) computer science, like algebra and logic, and theoretical computer scientists, working in areas as formal methods and theorem proving, have joined forces to establish a specialisation in the Mathematical Foundations of Computer Science. The programme is unique in the Netherlands and will be built on the excellence of both research institutes and the successful collaborations therein.
The emphasis of the Master's is on a combination of a genuine theoretical and up-to-date foundation in the pertinent mathematical subjects combined with an equally genuine and up-to-date training in key aspects of theoretical computer science. For this reason, the mathematics courses in this curriculum concentrate on Algebra, Complexity Theory, Logic, Number Theory, and Combinatorics. The computer science courses concentrate on Formal Methods, Type Theory, Category Theory, Coalgebra and Theorem Proving.
Within both institutes, ICIS and WINST, there is a concentration of researchers working on mathematical logic and theoretical computer science with a collaboration that is unique in the Netherlands. The research topics range from work on algebra, logic and computability, to models of distributed, parallel and quantum computation, as well as mathematical abstractions to reason about programmes and programming languages.

See the website http://www.ru.nl/masters/mathematics/foundations

Admission requirements for international students

1. A completed Bachelor's degree in Mathematics or Computer Science
In order to get admission to this Master’s you will need a completed Bachelor's in mathematics or computer science that have a strong mathematical background and theoretical interests. We will select students based on their motivation and their background. Mathematical maturity is essential and basic knowledge of logic and discrete mathematics is expected.

2. A proficiency in English
In order to take part in the programme, you need to have fluency in English, both written and spoken. Non-native speakers of English without a Dutch Bachelor's degree or VWO diploma need one of the following:
- TOEFL score of ≥575 (paper based) or ≥90 (internet based)
- IELTS score of ≥6.5
- Cambridge Certificate of Advanced English (CAE) or Certificate of Proficiency in English (CPE), with a mark of C or higher

Career prospects

There is a serious shortage of well-trained information specialists. Often students are offered a job before they have actually finished their study. About 20% of our graduates choose to go on to do a PhD but most find jobs as systems builders, ICT specialists or ICT managers in the private sector or within government.

Our approach to this field

In this Master's specialisation, mathematicians working in areas pertinent to (theoretical) computer science, like algebra and logic, and theoretical computer scientists, working in areas as formal methods and theorem proving, have joined forces to establish a specialisation in the Mathematical Foundations of Computer Science. The programme is unique in the Netherlands and will be built on the excellence of both research institutes and the successful collaborations therein.

The emphasis of the Master's is on a combination of a genuine theoretical and up-to-date foundation in the pertinent mathematical subjects combined with an equally genuine and up-to-date training in key aspects of theoretical computer science. For this reason, the mathematics courses in this curriculum concentrate on Algebra, General Topology, Logic, Number Theory, and Combinatorics. The computer science courses concentrate on Formal Methods, Type Theory, Category Theory, Coalgebra and Theorem Proving.

Our research in this field

Within both institutes, ICIS and WINST, there is a concentration of researchers working on mathematical logic and theoretical computer science with a collaboration that is unique in the Netherlands. The research topics range from work on algebra, logic and computability, to models of distributed, parallel and quantum computation, as well as mathematical abstractions to reason about programmes and programming languages.

See the website http://www.ru.nl/masters/mathematics/foundations

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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

Programme description

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 (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)
Research-Linked Topics (10 credits, semesters 1 and 2)

Optional courses - Computational stream:

Numerical Methods for Stochastic Differential Equations [compulsory] (5 credits, semester 2)
Numerical Partial Differential Equations [compulsory] (10 credits, semester 2)
Programming Skills - HPC MSc (10 credits, semester 1)
Parallel Numerical Algorithms - HPC MSc (10 credits, semester 1)

Optional courses - Financial stream:

Financial Risk Theory [compulsory] (10 credits, semester 2)
Optimization Methods in Finance [compulsory] (10 credits, semester 2)
Advanced Time Series Econometrics (10 credits, semester 2)
Credit Scoring (10 credits, semester 2)
Computing for Operational Research and Finance (10 credits, semester 1)
Financial Risk Management (10 credits, semester 2)
Stochastic Optimization (5 credits, semester 2)

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 programme develops mathematical modelling skills and provides mathematical techniques required by industry. The period October to June is devoted to lectures, tutorials and practical sessions comprising the core modules. Read more
This programme develops mathematical modelling skills and provides mathematical techniques required by industry.

The period October to June is devoted to lectures, tutorials and practical sessions comprising the core modules.

This is followed by a period of about 14 weeks devoted to an individual project either in an industrial or engineering company or at the University.

Core study areas include mathematical modelling, regular and chaotic dynamics, programming and numerical methods, advanced reliability, availability and maintainability, elements of partial differential equations, static and dynamic optimisation and fluid mechanics.

See the website http://www.lboro.ac.uk/study/postgraduate/programmes/departments/mathematics/industrial-maths-modelling/

Programme modules

Compulsory Modules:
Semester 1
- Mathematical Modelling I
- Regular and Chaotic Dynamics
- Programming and Numerical Methods
- Advanced Reliability, Availability and Maintainability

Semester 2
- Mathematical Modelling II
- Elements of Partial Differential Equations
- Static and Dynamic Optimisation
- Fluid Mechanics

Assessment

A combination of written examinations, reports, individual and group projects, and verbal presentations.

Careers and further Study

Graduate employment over a wide range of industries encompassing aerospace, automotive electronics, and computer interests as well as software houses, insurance companies, and research establishments and institutions.

Scholarships and sponsorships

A limited number of scholarships are available for this programme as well as the loyalty bonus scheme which reduces fees for Loughborough graduates.

Why choose mathematics at Loughborough?

Mathematics at Loughborough has a long history of innovation in teaching, and we have a firm research base with strengths in both pure and applied mathematics as well as mathematics education.

The Department comprises more than 34 academic staff, whose work is complemented and underpinned by senior visiting academics, research associates and a large support team.

The programmes on offer reflect our acknowledged strengths in pure and applied research in mathematics, and in some cases represent established collaborative training ventures with industrial partners.

- Mathematics Education Centre (MEC)
The Mathematics Education Centre (MEC) at Loughborough University is an internationally renowned centre of research, teaching, learning and support. It is a key player in many high-profile national initiatives.
With a growing number of academic staff and research students, the MEC provides a vibrant, supportive community with a wealth of experience upon which to draw.
We encourage inquiries from students who are interested in engaging in research into aspects of learning and teaching mathematics at Masters, PhD and Post Doc levels. Career prospects With 100% of our graduates in employment and/or further study six months after graduating, career prospects are excellent. Graduates go on to work with companies such as BAE Systems, Citigroup, Experian, GE Aviation, Mercedes Benz, Nuclear Labs USA and PwC.

- Career prospects
With 100% of our graduates in employment and/or further study six months after graduating, career prospects are excellent. Graduates
go on to work with companies such as BAE Systems, Citigroup, Experian, GE Aviation, Mercedes Benz, Nuclear Labs USA and PwC.

Find out how to apply here http://www.lboro.ac.uk/study/postgraduate/programmes/departments/mathematics/industrial-maths-modelling/

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The programme provides graduates with strong mathematical skills, the necessary computational techniques and finance background relevant to subsequent employment in a sector of finance such as investment banks, hedge funds, insurance companies and the finance departments of large corporations where mathematics plays a key role. Read more
The programme provides graduates with strong mathematical skills, the necessary computational techniques and finance background relevant to subsequent employment in a sector of finance such as investment banks, hedge funds, insurance companies and the finance departments of large corporations where mathematics plays a key role.

The depth of the mathematics taught should enable graduates to pursue research careers in stochastic analysis, financial mathematics or other relevant areas.

The period October to June is devoted to lectures, tutorials and practical sessions comprising the core and optional modules. This is followed by a period of about 14 weeks devoted to an individual project.

Core study areas include measure theory and martingales, stochastic models in finance, stochastic calculus and theory of stochastic pricing and a research project.

Optional study areas include programming and numerical methods, regular and chaotic dynamics, financial economics, functional analysis, elements of PDEs, static and dynamic optimisation, asset management and derivatives, and corporate finance

See the website http://www.lboro.ac.uk/study/postgraduate/programmes/departments/mathematics/mathematical-finance/

Programme modules

Semester 1:
Compulsory Modules
- Introduction to Measure Theory and Martingales
- Stochastic Models in Finance

Optional Modules (choose two)
- Programming and Numerical Methods
- Regular and Chaotic Dynamics
- Financial Economics

Semester 2:
Compulsory Modules
- Stochastic Calculus and Theory of Stochastic Pricing
- Research Project

Optional Modules (choose three)
- Functional Analysis
- Elements of PDEs
- Static and Dynamic Optimisation
- Either Asset Management and Derivatives or Corporate Finance

Assessment

A combination of written examinations, reports, individual and group projects, and verbal presentations.

Careers and further study

This programme may lead to a wide range of employment within industry, the financial sectors, and research establishments. It may also provide an ideal background for postgraduate research in Stochastic Analysis, Probability Theory, Mathematical Finance and other relevant areas.

Scholarships and sponsorships

A number of part-fee studentships may be available to appropriately qualified international students.

Why choose mathematics at Loughborough?

Mathematics at Loughborough has a long history of innovation in teaching, and we have a firm research base with strengths in both pure and applied mathematics as well as mathematics education.

The Department comprises more than 34 academic staff, whose work is complemented and underpinned by senior visiting academics, research associates and a large support team.

The programmes on offer reflect our acknowledged strengths in pure and applied research in mathematics, and in some cases represent established collaborative training ventures with industrial partners.

- Mathematics Education Centre (MEC)
The Mathematics Education Centre (MEC) at Loughborough University is an internationally renowned centre of research, teaching, learning and support. It is a key player in many high-profile national initiatives.
With a growing number of academic staff and research students, the MEC provides a vibrant, supportive community with a wealth of experience upon which to draw.
We encourage inquiries from students who are interested in engaging in research into aspects of learning and teaching mathematics at Masters, PhD and Post Doc levels. Career prospects With 100% of our graduates in employment and/or further study six months after graduating, career prospects are excellent. Graduates go on to work with companies such as BAE Systems, Citigroup, Experian, GE Aviation, Mercedes Benz, Nuclear Labs USA and PwC.

- Career prospects
With 100% of our graduates in employment and/or further study six months after graduating, career prospects are excellent. Graduates
go on to work with companies such as BAE Systems, Citigroup, Experian, GE Aviation, Mercedes Benz, Nuclear Labs USA and PwC.

Find out how to apply here http://www.lboro.ac.uk/study/postgraduate/programmes/departments/mathematics/mathematical-finance/

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Businesses, organisations, and individuals all strive to work as effectively as possible. Operational research uses advanced statistical and analytical methods to help improve the complex decision-making processes to deliver a product or service. Read more
Businesses, organisations, and individuals all strive to work as effectively as possible. Operational research uses advanced statistical and analytical methods to help improve the complex decision-making processes to deliver a product or service. Working in this field, you might be identifying future needs for a business, evaluating the time-life value of a customer, or carrying out computer simulations for airlines.

Our MSc Statistics and Operational Research will appeal if your first degree included mathematics as its major subject, and we expect you to have prior knowledge of statistics – for example significance testing or basic statistical distributions – and operational research such as linear programming.

You specialise in areas including:
-Continuous and discrete optimisation
-Time series econometrics
-Heuristic computation
-Experimental design
-Machine learning
-Linear models

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=PG00808&subgroup=2

Our expert staff

Our Department of Mathematical 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

Our MSc Statistics and Operational Research will equip you with employability skills like problem solving, analytical reasoning, data analysis, and mathematical modelling, as well as training you in independent work, presentation and writing skills.

Your exposure to current active research areas, such as decomposition algorithms on our module, Combinatorial Optimisation, prepares you for further study at doctoral level. Graduates of this course now hold key positions in government, business and academia.

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

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

Example structure

-Nonlinear Programming
-Combinatorial Optimisation
-Modelling Experimental Data (optional)
-Statistical Methods (optional)
-Stochastic Processes (optional)
-Applied Statistics (optional)
-Bayesian Computational Statistics
-Research Methods
-Dissertation
-Ordinary Differential Equations (optional)
-Graph Theory (optional)
-Partial Differential Equations (optional)
-Portfolio Management (optional)
-Machine Learning and Data Mining (optional)
-Evolutionary Computation and Genetic Programming (optional)
-Time Series Econometrics (optional)
-Panel Data Methods (optional)
-Applications of Data Analysis (optional)
-Mathematical Research Techniques Using Matlab (optional)

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Sophisticated financial markets continue to play an essential role in the economies of the modern world, and a career in the finance and banking industry can be exciting, intellectually challenging and highly lucrative. Read more
Sophisticated financial markets continue to play an essential role in the economies of the modern world, and a career in the finance and banking industry can be exciting, intellectually challenging and highly lucrative.

In spite of recent turmoil in this sector, there is still strong demand for well-qualified individuals. Areas such as quantitative analysis and risk management are now as important as ever. However, there is an increased importance that professionals working in areas such as trading, sales and financial engineering also have a solid understanding of the mathematical models (and their limitations) that are used to price and risk-manage the financial products that are traded.

This specialised programme is aimed at both new graduates and current professionals. It has been designed to provide you with the necessary mathematical tools and techniques to understand and model the complexity of financial markets, thereby enabling you to develop a successful career in the finance industry. With this programme you will:

Understand many of the advanced mathematical models used in finance
Learn about a range of important numerical tools and techniques used in the field
Gain practical skills in computer programming and software design
Undertake a substantial project, under expert supervision, in a chosen area of interest

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Mathematics is the language that underpins the rest of science. Our Department of Mathematical Sciences has an international reputation in many areas like such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology. Read more
Mathematics is the language that underpins the rest of science. Our Department of Mathematical Sciences has an international reputation in many areas like such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology.

Graduate Diplomas last for six to nine months (full-time) and include the modules and assessed work of a Masters, without a dissertation. Our Graduate Diploma in Mathematics gives you training in basic mathematics techniques if your first degree contained only a modest amount of mathematics, so that you can proceed to a Masters in mathematics.

At Essex, Mathematics has truly broad reach; we are working on projects ranging from the economic impact of the behaviour of dairy cows, to understanding crowd behaviour through modelling a zombie apocalypse, to circular Sudoku and other puzzles. Our interdisciplinary research recognises that mathematics, including what can be very abstract mathematics, is an essential part of research in many other disciplines.

You therefore gain an exceptional range of knowledge and skills that are currently in demand in mathematically oriented employment; in business, commerce, industry, government service, education and in the wider economy.

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

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

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

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

Example structure

-Applied Statistics (optional)
-Bayesian Computational Statistics (optional)
-Combinatorial Optimisation (optional)
-Complex Variables and Applications (optional)
-Contingencies I
-Contingencies II
-Cryptography and Codes
-Finance and Financial Reporting (optional)
-Financial Derivatives (optional)
-Graph Theory (optional)
-Introduction to Numerical Methods (optional)
-Linear Algebra (optional)
-Mathematical Biology (optional)
-Mathematical Methods (optional)
-Mathematics of Portfolios (optional)
-Modelling Experimental Data (optional)
-Nonlinear Programming (optional)
-Ordinary Differential Equations (optional)
-Partial Differential Equations (optional)
-Project: Mathematics (optional)
-Quantum Mechanics (optional)
-Real Analysis (optional)
-Statistical Methods (optional)
-Statistics II (optional)
-Stochastic Processes (optional)
-Survival Analysis (optional)
-The Laws of Physics (optional)
-Vector Calculus (optional)

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Practices in the financial markets over the last years have contributed to the current economic situation. Read more

Overview

Practices in the financial markets over the last years have contributed to the current economic situation. This specialised programme that has been designed to provide you with the necessary mathematical techniques and tools to understand and model the complexity of financial markets, and to succeed in a future career in the finance industry.

Designed to equip talented individuals with the skills necessary for a successful career in finance, this MSc brings together the strengths of two highly respected schools at Queen Mary – Mathematical Sciences, and Economics and Finance – to give you an in-depth understanding of the subject. Rigorous training is provided, relevant to roles in quantitative analysis, trading, financial engineering and structuring, risk management, and software development.

The programme consists of eight modules and a dissertation. You will develop an advanced understanding of the mathematical models used in finance, learn about a range of important numerical tools and techniques, gain practical skills in computer programming, and undertake a substantial research project under expert supervision in a chosen area of interest. Past dissertation topics have been in areas such as the distribution of loan portfolio value; passport options; the Heston stochastic volatility model, pricing American options using Monte-Carlo, and asset pricing with jump diffusion models.

This programme will:

Introduce you to the mathematics used by practitioners in the field.
Provide you a rigorous training and strong analytical and quantitative skills in finance.
Cover a wide range of analytical tools applied in quantitative asset pricing and financial derivatives.

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

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

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

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

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

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

Our expert staff

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

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

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

Specialist facilities

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

Your future

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

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

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

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

Example structure

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

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Statistics is one of the most important fields of study in the world. The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society. Read more
Statistics is one of the most important fields of study in the world. The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society. If you are a logical person and enjoy solving problems, statistics at Essex is for you.

Our Department of Mathematical Sciences embraces pure mathematics, applied mathematics and statistics, and operational research, and our course offers you the opportunity to study statistics alongside other mathematical subjects.

Providing a balance of solid statistical theory and practical application, this course builds your knowledge in all areas of statistics, data analysis and probability. You also have the opportunity to specialise, taking optional modules in topics including:
-Survey methodology
-Operations research
-Applied mathematics
-Computer science

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

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

Working in industries such as health, business, social care and finance, graduates are consistently in demand, working on projects such as efficacy of social policy, comparable data of cardiac rehabilitation and manipulation of raw data for academic research.

Our Masters graduates have progressed into careers in banking and finance, actuarial sciences, biological sciences, market research and statistics, management and consultancy etc.

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

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

Example structure

-Modelling Experimental Data
-Statistical Methods
-Stochastic Processes
-Applied Statistics
-Bayesian Computational Statistics
-Research Methods
-Dissertation
-Nonlinear Programming (optional)
-Financial Modelling (optional)
-Research Methods in Finance: Empirical Methods in Finance (optional)
-Machine Learning and Data Mining (optional)
-Cloud Technologies and Systems (optional)
-Time Series Econometrics (optional)
-Panel Data Methods (optional)
-Topics in Contemporary Social Theory (optional)
-Introduction to Survey Design and Management (optional)
-Applied Sampling (optional)

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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.

Degree information

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

Students undertake modules to the value of 180 credits.

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

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

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

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

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

Careers

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

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

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

Why study this degree at UCL?

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

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

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

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Profitable design and operation of modern industrial systems requires integration of human, material, equipment and financial resources. Read more
Profitable design and operation of modern industrial systems requires integration of human, material, equipment and financial resources. In recent years this integration has become tighter due to the inclusion of information technology, and resulted in more complex systems. Industrial Engineering research focuses on quantitative analysis, synthesis and management of such complex systems. The affiliated faculty members come from the Industrial Engineering department of the College of Engineering, the Operations and Information Systems group of the College of Administrative Sciences and Economics, and other related fields. Our research areas are diverse, including Logistics, Supply Chain Management, Service Operations, Production Systems, Stochastic Processes, Financial Engineering, Mathematical Programming, Data Mining and Bioinformatics. The programs are built on the basic methodologies of operations research and their applications in manufacturing, distribution and service industries. Graduates of the M.Sc. program have been placed in respectable Ph.D. programs in North America, Europe and Turkey as well as various professional positions in industry.

Current faculty projects and research interests:

• Production Systems
• Service Operations
• Logistics
• Optimization Theory & Algorithms
• Stochastic Processes
• Finanacial Engineering
• Data Mining
• Bioinformatics
• Revenue Management
• Supply Chain Management

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A UK 2:1 honours degree or its international equivalent in an area related to management science, operational research, statistics, econometrics, mathematics, physics, computer science, engineering, or business and management with a distinct quantitative content. Read more

Entry Requirements (extended)

A UK 2:1 honours degree or its international equivalent in an area related to management science, operational research, statistics, econometrics, mathematics, physics, computer science, engineering, or business and management with a distinct quantitative content.

Your background should ideally include courses and/or experience gained in topics such as linear algebra, calculus, probability, statistics, and computer programming.

If you have a UK 2:1 honours degree or its international equivalent in an unrelated subject we may consider your application if you have relevant work experience.

Work experience is desirable but not mandatory.

Students are required to have their own laptops. In particular, applicants need to ensure that their laptops can run MATLAB.

Programme Descriptions

Given the ever-increasing volume of data that businesses have access to today, there is a demand for employees who have computational and analytical skills and who can help inform business decisions to increase efficiency and ultimately build a competitive edge. Our Masters in Business Analytics has been developed with just that in mind and aims to train students to understand the variety of business environments as well as importance and relevance of decision problems facing organisations and ways of solving these using a variety of techniques.

Students will learn how to apply descriptive, predictive, and prescriptive analytics of big data concepts and techniques to generate valuable insight that can assist with decision making. Last, but not least, the Masters will provide students with hands-on experience in applying these concepts and using these techniques and most of the popular analytics software through their projects including industry projects. In sum, the programme aims to equip students with the skills required to interpret, conceptualise and convert Big Data into useful information and thus enabling students to make better informed decisions as future business managers.

Programme Structure

All students attend and complete four core courses in Semester 1 and four option courses in Semester 2. The programme concludes with a 60-credit Dissertation. Details of core and option courses are shown below.

Core courses - Semester 1

Principles of Data Analytics
Predictive Analytics and Modelling of Data
Prescriptive Analytics with Mathematical Programming
Business Analytics with Heuristics
Option courses - Semester 2

Performance Analytics with DEA
Media and Web Analytics
Industrial Analytics
Business Analytics with Simulation
Inventory Analytics
Supply Chain Analytics with Games
The Dissertation: Each student conducts an individual research project on a subject within business analytics, chosen in consultation with an academic supervisor. MSc candidates write up their work as a Dissertation (maximum 15,000 words with a maximum of 10% margin either way), which is submitted by the end of August. Some Dissertation topics are offered that involve working with business, NGOs or other organisations.

Learning Outcomes

General Educational aims are to:

Enable all participants to recognise, understand and apply the language, theory and models of the field of business analytics.
Foster an ability to critically analyse, synthesise and solve complex unstructured business problems.
Encourage an aptitude for business improvement, innovation and entrepreneurial action.
Encourage the sharing of experiences to enhance the benefits of collaborative learning.
Instil a sense of ethical decision-making and a commitment to the long-run welfare of both organisations and the communities that they serve.
Specific Educational aims are to:

Understand and critically apply the concepts and methods of business analytics.
Identify, model and solve decision problems in different settings
Interpret results/solutions and identify appropriate courses of action for a given managerial situation whether a problem or an opportunity.
Create viable solutions to decision making problems.

Career Opportunities

It is anticipated that the demand for data analytics employees will continue and indeed grow as analytics is viewed as a key competitive resource by many companies. If market forecasts are anything to go by then the Big Data technology and services market will grow at a 26.4% annual growth rate to $41.5 billion through 2018, or about 6 times the growth rate of the overall information technology market (Source: IDC). This will inevitably lead to further growth of the market for employees in the area of data analytics.

The new MSc in Business Analytics will offer students from a range of degree backgrounds the opportunity to equip themselves with an artillery of concepts, methods and applications of business analytics along with hands-on and practical experience in applying them to industry projects in different business settings. It is not just about being able to analyse and digest the data available but to then translate this into effective decision-making. In sum, we expect the new programme to open a range of career pathways in analytics for our graduates including business consultants, business analysts, business intelligence & analytics consultants, metrics & analytics specialists, analytics associates, data analysts, solution architects, business process analysts, management consulting associates, and operational research consultants.

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