About the course
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.
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.
- 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
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.
Postgraduate study is the chance to take your education to the next level. The combination of compulsory and optional modules means our courses help you develop extensive knowledge in your chosen discipline, whilst providing plenty of freedom to pursue your own interests. Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore to ensure your course is as relevant and up-to-date as possible your core module structure may be subject to change.
For many of our courses you’ll have a wide range of optional modules to choose from – those listed in this example structure are, in many instances, just a selection of those available. Our Programme Specification gives more detail about the structure available to our current postgraduate students, including details of all optional modules.
How do you apply an algorithm or numerical method to a problem? What are the advantages? And the limitations? Understand the theory and application of nonlinear programming. Learn the principles of good modelling and know how to design algorithms and numerical methods. Critically assess issues regarding computational algorithms.
In this module you will not only learn what underpins the algorithms used where variables are integer, but also apply these algorithms to solve integer and mixed integer problems with cutting-plane algorithms.
Can you calculate confidence intervals for parameters and prediction intervals for future observations? Represent a linear model in matrix form? Or adapt a model to fit growth curves? Learn to apply linear models to analyse data. Discuss underlying assumptions and standard approaches. Understand methods to design and analyse experiments.
This module will enable you to expand your knowledge on multiple statistical methods. You will learn the concepts of decision theory and how to apply them, have the chance to explore “Monte Carlo” simulation, and develop an understanding of Bayesian inference, and the basic concepts of a generalised linear model.
Ever considered becoming an Actuary? This module covers the required material for the Institute and Faculty of Actuaries CT4 and CT6 syllabus. It explores the stochastic process and principles of actuarial modelling alongside time series models and analysis.
How do you apply multivariate methods? Or demographical and epidemiological methods? And how do you apply sampling methods? Study three application areas of statistics – multivariate methods, demography and epidemiology, and sampling. Understand how to apply and assess these methods in a variety of situations.
What do you understand about Bayes’ theorem and Bayesian statistical modelling? Or about Markov chain Monte Carlo simulation? Focus on Bayesian and computational statistics. Understand the statistical modelling and methods available. Learn to develop a Monte Carlo simulation algorithm for simple probability distributions.
Looking to build your research capabilities? This module will equip you with the principal research tools for your postgraduate course in Mathematical Sciences, including practice in the mathematical word-processing language LaTeX.
How do you solve systems of linear first-order equations in two unknowns with constant coefficients? Or analyse the stability characteristics of non-linear systems in two unknowns? Study the standard methods to solve single ordinary differential equations and systems of equations. Understand the underlying theory.
Can you prove basic results in the theory of graphs? Or deal with basic theory about matchings, like Hall’s theorem? Examine key definitions, proofs and proof techniques in graph theory. Gain experience of problems connected with chromatic number. Understand external graph theory, Ramsey theory and the theory of random graphs.
This module will cover partial differential equations (PDEs), which can describe a majority of physical processes and phenomena. You will learn the properties of first and second order PDEs, the concepts behind them and the methods for solving such equations.
Understand the process of portfolio management. You cover the main concepts such as efficient diversification, managing risk exposures, and the valuation of financial assets that are at the core of managing investment portfolios, and pay special attention to the practicalities of the implementation of these concepts.
Humans can often perform a task extremely well (e.g., telling cats from dogs) but are unable to understand and describe the decision process followed. Without this explicit knowledge, we cannot write computer programs that can be used by machines to perform the same task. “Machine learning” is the study and application of methods to learn such algorithms automatically from sets of examples, just like babies can learn to tell cats from dogs simply by being shown examples of dogs and cats by their parents. Machine learning has proven particularly suited to cases such as optical character recognition, dictation software, language translators, fraud detection in financial transactions, and many others.
Evolutionary computation is an exciting area of artificial intelligence that focuses on systematic methods (known as evolutionary algorithms) inspired by Darwinian evolution for getting computers to automatically solve problems starting from a high-level statement of what needs to be done. Evolutionary algorithms are today routinely used to solve difficult problems in industry, medicine, biology, finance, and much more. Evolutionary algorithms can even consistently solve difficult problems which require solutions in the form of computer programs. This is a form of automatic programming that is known as genetic programming. In this module you will learn how to use evolutionary algorithms and genetic programming to solve real-world problems from an international authority in these areas.
How do you analyse stationary time series? Or non-stationary (integrated) processes? Understand the econometric methods available to analyse models of economic time series. Examine how methods of estimation and inference can be applied to these models. Learn how to use these methods in your own research.
What are the main issues with panel data? And the main econometric techniques to analyse panel data? What methods can you use to evaluate spell duration data? Answer such questions with examples from labour economics, while gaining the skills to analyse a variety of research and policy problems.
What are the issues regarding different types of panel datasets? Or problems with survey methodology? Understand longitudinal data analysis by using micro-econometric techniques and critically examine survey methodology issues, like response rate and sampling frames. Apply panel data methods to study labour markets, focusing on marriage, unemployment and wages.
Mathematics is a tool used in many fields of research, and this module introduces students to techniques and ways of thinking designed to enable them to carry out their own mathematical investigations, or to apply mathematical ideas to an investigation of their own (typically for most students on this module, this will be their Dissertation project). We use the industry standard mathematical software Matlab, although the techniques introduced can also be applied using other software, and we study a range of techniques for numerical computation and processing of data.
- Core components can be combined with optional modules, to enable you to gain either in-depth specialisation or a breadth of understanding
- Learn to use LATEX to produce a document as close as possible to what professional mathematicians produce in terms of organisation, layout and type-setting
- Our postgraduates are encouraged to attend conferences and seminars on a Thursday afternoon
- Courses are assessed on the results of your written examinations, together with continual assessments of your practical work and coursework
- You will be provided with a list of dissertation titles or topics proposed by staff and it may be possible to propose a project of your own
- Most dissertations are between 10,000 and 30,000 words in length. However, these are guidelines, not mandatory word counts
- Close supervision by academic staff
UK entry requirements
A degree with an overall high 2:2.
International and EU entry requirements
We accept a wide range of qualifications from applicants studying in the EU and other countries.
for further details about the qualifications we accept. Include information in your email about the
undergraduate qualification you have already completed or are currently taking.
IELTS entry requirements
IELTS 6.0 overall with a minimum component score of 5.5
If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.
You can apply for our postgraduate courses online. You’ll need to provide us with your academic qualifications, as well as supporting documents such as transcripts, English language qualifications and certificates. You can find a list of necessary documents online, but please note we won’t be able to process your application until we have everything we need.
There is no application deadline but we recommend that you apply before 1 July for our taught courses starting in October. We aim to respond to applications within two weeks. If we are able to offer you a place, you will be contacted via email.
We hold postgraduate events in February/March and November, and open days for all our applicants throughout the year. Our Colchester Campus events are a great way to find out more about studying at Essex, and give you the chance to:
- tour our campus and accommodation
- find out answers to your questions about our courses, student finance, graduate employability, student support and more
- meet our students and staff
If the dates of our organised events aren’t suitable for you, feel free to get in touch by emailing firstname.lastname@example.org and we’ll arrange an individual campus tour for you.
If you live too far away to come to Essex (or have a busy lifestyle), no problem. Our 360 degree virtual tour allows you to explore the Colchester Campus from the comfort of your home. Check out our accommodation options, facilities and social spaces.
Our staff travel the world to speak to people about the courses on offer at Essex. Take a look at our list of exhibition dates to see if we’ll be near you in the future.