This site uses cookies. By continuing to browse the site you are consenting to their use. Please visit our cookie policy to find out which cookies we use and why.
View cookie policy.
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
A successful career in data science requires you to possess truly interdisciplinary knowledge, so we ensure that you graduate with a wide-ranging yet specialised set of skills in this area. You are taught mainly within our Department of Mathematical Sciences and our School of Computer Science and Electronic Engineering, but also benefit from input from our Essex Business School, and our Essex Pathways Department. Data scientists are required in every sector, carrying out statistical analysis or mining data on social media, so our course can open the door to almost any industry, from health, to government, to publishing.
Our Department of Mathematical Sciences is genuinely innovative and student-focused. Our research groups are working on a broad range of collaborative areas tackling real-world issues. Here are a few examples:
Our data scientists carefully consider how not to lie, and how not to get lied to with data. Interpreting data correctly is especially important because much of our data science research is applied directly or indirectly to social policies, including health, care and education.
We do practical research with financial data (for example, assessing the risk of collapse of the UK’s banking system) as well as theoretical research in financial instruments such as insurance policies or asset portfolios.
We also research how physical processes develop in time and space. Applications of this range from modelling epilepsy to modelling electronic cables.
Our optimisation experts work out how to do the same job with less resource, or how to do more with the same resource.
Our pure maths group are currently working on two new funded projects entitled ‘Machine learning for recognising tangled 3D objects’ and ‘Searching for gems in the landscape of cyclically presented groups’.
We also do research into mathematical education and use exciting technologies such as electroencephalography or eye tracking to measure exactly what a learner is feeling. Our research aims to encourage the implementation of ‘the four Cs’ of modern education, which are critical thinking, communication, collaboration, and creativity.
Why we're great.
We are committed to developing the data scientists of the future.
86% of our mathematics graduates are in professional employment or further postgraduate study within six months of graduating from Essex (DLHE 2017).
We have active links with industry to broaden your employment potential and placement opportunities.
Placement year
MSc Data Science with Professional Placement offers a unique opportunity for you to gain relevant work experience within an external business or organisation, giving you a competitive edge in the job market and providing you with key contacts within the industry. The placement is undertaken between the taught part of the course and the individual project. Its aim is to allow you to acquire industry experience and, especially, develop an appreciation of how the skills acquired in the taught part of the course can be applied to real world problems.
You’ll be responsible for securing your own work placement, but if you change your mind and decide not to do your placement, or if you are not able to secure a placement, you can start your dissertation earlier and complete your Masters in the first year.
Our expert staff
Today’s computer scientists are creative people who are focused and committed, yet restless and experimental. We are home to many of the world’s top scientists, and our staff are driven by creativity and imagination as well as technical excellence. We conduct world-leading research in areas such as explorative data analysis, classification and clustering, evolutionary computation, data visualisation and financial forecasting. Specialist staff working on data science and analytics include:
Dr Luca Citi – machine learning, learning from biological signals and data (EEG, etc)
Dr Hongsheng Dai – computational Bayesian statistics
Professor Maria Fasli – machine learning, adaptation, semantic information extraction, ontologies, data exploration, recommendation technologies
Professor Udo Kruschwitz – natural language processing, analysis textual/unstructured data, information retrieval
Professor Berthold Lausen – biostatistics, classification and clustering, data science education, event time data, machine learning, predictive modelling
Dr Aris Perperoglou – data analysis and data visualisation, statistical modelling and smoothing, survival analysis, clinical trials
Professor Abdel Salhi – data mining, numerical analysis, optimisation
Professor Edward Tsang – applied AI, constraint satisfaction, computational finance and economics, agent-based simulations
Dr Spyros Vrontos – actuarial mathematics and actuarial modelling
Dr Xinan Yang – approximate dynamic programming, Markov decision process
Specialist facilities
All computers run either Windows 10 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
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 areas. Our recent graduates have gone onto work as data scientists and data analysts in both the private and public sectors.
We also offer supervision for PhD, MPhil and MSc by Dissertation.
We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.
Entry requirements
UK entry requirements
A 2:2 degree in one of the following subjects: Mathematics, Statistics, Operational research, Finance, Economics, Business Engineering, Computing, Biology, Physics or Chemistry.
Will consider applicants with a unrelated degree but which contained at least three modules in calculus, algebra, differential equations, probability & statistics, optimisation or other mathematical modules.
Applicants with a degree below a 2:2 or equivalent will be considered dependent on any relevant professional or voluntary experience, previous modules studied and/or personal statement.
International & EU entry requirements
We accept a wide range of qualifications from applicants studying in the EU and other countries. Get in touch with any questions you may have about the qualifications we accept. Remember to tell us about the qualifications you have already completed or are currently taking.
Sorry, the entry requirements for the country that you have selected are not available here. Please select
your country page
where you'll find this information.
English language 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.
Additional Notes
The University uses academic selection criteria to determine an applicant’s ability to successfully complete a course at the University of Essex. Where appropriate, we may ask for specific information relating to previous modules studied or work experience.
Structure
Example structure
Most of our courses combine compulsory and optional modules, giving you freedom to pursue your own interests. All of the modules listed below provide an example of what is on offer from the current academic year. Our Programme Specification provides further details of the course structure for the current academic year.
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.
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.
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.
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.
This module enables you to undertake a placement with an external Placement Provider. You will acquire effective work-based skills specific to your chosen field, and gain a detailed understanding of work processes. It’s an opportunity to put taught skills into practise and develop a network of industry professionals. Your placement is a sought-after contribution to your employability, giving you the tools employers look for in skilled graduates.
Search engines have become the first entry point into a world of knowledge and they form an essential part of many modern computer applications. While much of the underlying principles have been developed over decades, the landscape of search engine technology has changed dramatically in recent years to deal with data sources magnitudes larger than ever before (the rise of 'big data'). As a result of this, new paradigms for storing, indexing and accessing information have emerged. This module will provide the essential foundation of information retrieval and equip students with solid, applicable knowledge of state-of-the-art search technology.
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.
What skills do you need to succeed during your studies? And what about after university? How will you realise your career goals? Develop your transferable skills and experiences to create your personal profile. Reflect on and plan your ongoing personal development, with guidance from your personal advisor within the department.
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 learn what underpins the algorithms used where variables are integer and apply these algorithms to solve integer and mixed integer problems with cutting-plane algorithms.
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.
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.
The aim of this module is to provide an introduction to computer programming for students with little or no previous experience. The Python language is used in the Linux environment, and students are given a comprehensive introduction to both during the module. The emphasis is on developing the practical skills necessary to write effective programs, with examples taken principally from the realm of data processing and analysis. You will learn how to manipulate and analyse data, graph them and fit models to them. Teaching takes place in workshop-style sessions in a software laboratory, so you can try things out as soon as you learn about them.
We live in an era in which the amount of information available in textual form - whether of scientific or commercial interest - greatly exceeds the capability of any man to read or even skim. Text analytics is the area of artificial intelligence concerned with making such vast amounts of textual information manageable - by classifying documents as relevant or not, by extracting relevant information from document collections, and/or by summarizing the content of multiple documents. In this module we cover all three types of techniques.
As humans we are adept in understanding the meaning of texts and conversations. We can also perform tasks such as summarize a set of documents to focus on key information, answer questions based on a text, and when bilingual, translate a text from one language into fluent text in another language. Natural Language Engineering (NLE) aims to create computer programs that perform language tasks with similar proficiency. This course provides a strong foundation to understand the fundamental problems in NLE and also equips students with the practical skills to build small-scale NLE systems. Students are introduced to three core ideas of NLE: a) gaining an understanding the core elements of language--- the structure and grammar of words, sentences and full documents, and how NLE problems are related to defining and learning such structures, b) identify the computational complexity that naturally exists in language tasks and the unique problems that humans easily solve but are incredibly hard for computers to do, and c) gain expertise in developing intelligent computing techniques which can overcome these challenges.
The aim of this module is to familiarise students with the whole pipeline of processing, analysing, presenting and making decision using data. This module blends data analysis, decision making and visualisation with practical python programming. Students will need a reasonable programming background as they will be expected to develop a complete end-to-end data science application.
The aim of this module is to provide students with an understanding of the role of artificial neural networks (ANNs) in computer science and artificial intelligence. This will allow the student to build computers and intelligent machines which are able to have an artificial brain which will allow them to learn and adapt in a human like fashion.
In this module you'll be introduced to the basics of probability and random variables. Topics you will discuss include distribution theory, estimation and Maximum Likelihood estimators, hypothesis testing, basic linear regression and multiple linear regression implemented in R.
This module is a mix of theory and practice with big data cases in finance. Algorithmic and data science theories will be introduced and followed by a thorough introduction of data-driven algorithms for structures and unstructured data. Modern machine learning and data mining algorithms will be introduced with particular case studies on financial industry.
This module enables you to undertake a placement with an external Placement Provider. You will acquire effective work-based skills specific to your chosen field, and gain a detailed understanding of work processes. It’s an opportunity to put taught skills into practise and develop a network of industry professionals. Your placement is a sought-after contribution to your employability, giving you the tools employers look for in skilled graduates.
This is a dissertation module for MSc students. Student will be provided with a list of dissertation titles or your own, provided a member of staff agrees it is of suitable standard and is able to supervise it.
You will complete a professional placement between the taught part of the course and the individual project. This professional placement allows you to gain work experience during your postgraduate studies.
Teaching
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
Assessment
Courses are assessed on the results of your written examinations, together with continual assessments of your practical work and coursework
Dissertation
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
Fees and funding
Home/EU fee
£8,340
Year 2 fee is currently calculated as a 40% of the Year 1 fee.
International fee
£17,900
Year 2 fee is currently calculated as a 40% of the Year 1 fee.
Fees will increase for each academic year of study.
What's next
Open Days
We hold 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 tours@essex.ac.uk and we’ll arrange an individual campus tour for you.
We aim to respond to applications within two weeks. If we are able to offer you a place, you will be contacted via email.
For information on our deadline to apply for this course, please see our ‘how to apply’ information.
Visit Colchester Campus
Home to 15,000 students from more than 130 countries, our Colchester Campus is the largest of our three sites, making us one of the most internationally diverse campuses on the planet - we like to think of ourselves as the world in one place.
The Campus is set within 200 acres of beautiful parkland, located two miles from the historic town centre of Colchester – England's oldest recorded town. Our Colchester Campus is also easily reached from London and Stansted Airport in under one hour.
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.
Exhibitions
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.
At Essex we pride ourselves on being a welcoming and inclusive student community. We offer a wide range of support to individuals and groups of student members who may have specific requirements, interests or responsibilities.
The University makes every effort to ensure that this information on its course finder is accurate and up-to-date. Occasionally it can
be necessary to make changes, for example to courses, facilities or fees. Examples of such reasons might include a change of law or regulatory
requirements, industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding.
Changes to courses may for example consist of variations to the content and method of delivery of programmes, courses and other services, to
discontinue programmes, courses and other services and to merge or combine programmes or courses. The University will endeavour to keep such
changes to a minimum, and will also keep prospective students informed appropriately by updating our programme specifications.
The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and
Ordinances and in the University Regulations, Policy and Procedures.
