Undergraduate Course

Integrated Master in Science: Mathematics and Data Science

Integrated Master in Science: Mathematics and Data Science

Overview

The details
Mathematics and Data Science
G199
October 2021
Full-time
4 years
Colchester Campus

Data is the lifeblood of our society and mathematics is the language that underpins the science of understanding that data. From medicine to government offices, and market research to the environment, the collection and analysis of data is crucial to understanding how to improve, create and guide products and services across the globe. 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.

Our MSci Mathematics and Data Science is an Integrated Masters course that will cover key skills in mathematics and data science giving you the opportunity to apply theory and methods to real-world problems. You’ll foster your understanding of mathematics, develop skills to use smart devices efficiently and use statistical techniques and methods to interpret and exploit big data.

The concept and skill of a programming language are the crucial basis for skills in data base programming and management, in developing data processing pipelines and in organising and analysing large and massive data sets. This course will introduce you to programming language Python and R for statistical analysis and data visualisation. In your third year and fourth year, analysing data and methods in group projects will be essential capstone modules for the learning outcomes of the course.

Topics include:

  • Mathematical skills including, applied mathematics, pure mathematics and optimisation
  • Computer science and programming
  • Statistics and operations research
  • Artificial intelligence, databases and information retrieval
  • Ethical issues around the use and processing of data
  • Specialist skills in the areas of big data, data analytics and data science

Our Integrated Masters gives you the opportunity to fast track your degree and complete your final year in nine months compared to a regular MSc which usually takes twelve months. Combining your undergraduate and postgraduate study in one degree will give you a strong theoretical background as well as specialist expertise through independent research. This combination makes graduates from our course attractive candidates for many employers.

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 can 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.

Why we're great.
  • You have access to our ultramodern facilities at our new STEM Centre that provide real-world experience.
  • You join a community of scholars leading the way in technological research and development.
  • An incredible 89% of our undergraduate maths students are in employment or further study (Graduate Outcomes 2020).
THE Awards 2018 - Winner University of the Year

Our expert staff

We are also 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. As well as being world-class academics, our staff are award-winning teachers. Many of our academics have won national or regional awards for lecturing, and many of them are qualified and accredited teachers – something which is very rare at a university.

We are a small but influential department, so our students and staff know each other personally. You never need an appointment to see your lecturers and professors, 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.

Specialist facilities

  • In addition to teaching, we have a Maths Support Centre, which offers help to students 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 a dedicated social and study space for Maths students in the department, which is situated in the new £18m STEM Centre
  • We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
  • 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

Your future

Demand for skilled graduates in the areas of mathematics, big data and data science is growing rapidly in both the public and private sector, and there is a predicted shortage of data scientists with the skills to understand and make commercial decisions based on the analysis of big data. An incredible 89% of our undergraduate maths students are in employment or further study (Graduate Outcomes 2020).

A successful career in data science requires you to possess truly interdisciplinary knowledge, so we’ll ensure 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. Mathematicians and 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.

The University of Essex is bridging the gap between academia and business, and we are at the forefront of helping the UK grasp the big data opportunities to get ahead in the global race. Our graduates in data science have been very successful in finding employment in the public sector, consulting, technology, retail, and utilities, while a number have gone on to postgraduate study or research.

We have a large pool of external contacts, ranging from companies providing robots for the media industry, through vehicle diagnostics, to the transforming of unstructured data to cloud-based multidimensional data cubes, who work with us and our students to provide advice, placements and eventually graduate opportunities.

We also work with our University's Student Development Team to help you find out about further work experience, internships, placements, and voluntary opportunities.

Entry requirements

UK entry requirements

A-levels: ABB including Mathematics or Further Mathematics

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.

Structure

Example structure

We offer a flexible course structure with a mixture of compulsory and optional modules chosen from lists. The first three undergraduate years listed below are an example structure from the current academic year. Your course structure could differ from this if modules change from year-to-year. The final Masters year shows you all of the modules currently available (compulsory and optional) so you can see the breadth of what is on offer.

Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore all modules listed are subject to change. To view the compulsory modules and full list of optional modules currently on offer, please view the programme specification via the link below.

Teaching and learning disclaimer

Following the impact of the pandemic, we made changes to our teaching and assessment to ensure our current students could continue with their studies uninterrupted and safely. These changes included courses being taught through blended delivery, normally including some face-to-face teaching, online provision, or a combination of both across the year.

The teaching and assessment methods listed show what is currently planned for 2021 entry; changes may be necessary if, by the beginning of this course, we need to adapt the way we’re delivering them due to the external environment, and to allow you to continue to receive the best education possible safely and seamlessly.

Calculus

This module will allow you to build your knowledge of differentiation and integration, how you can solve first and second order differential equations, Taylor Series and more.

View Calculus on our Module Directory

Applied Mathematics

Want to understand Newtonian Dynamics? Interested in developing applications of mathematical ideas to study it? Enhance your skills and knowledge in the context of fundamental physical ideas that have been central to the development of mathematics. Analyse aspects of technology and gain experience in the use of computer packages.

View Applied Mathematics on our Module Directory

Statistics I

How do you apply the addition rule of probability? Or construct appropriate diagrams to illustrate data sets? Learn the basics of probability (combinatorial analysis and axioms of probability), conditional probability and independence, and probability distributions. Understand how to handle data using descriptive statistics and gain experience of R software.

View Statistics I on our Module Directory

Matrices and Complex Numbers

Can you perform simple operations on complex numbers? How do you solve systems of linear equations using row operations? Can you calculate the determinant and inverse of a matrix? Understand the basics of linear algebra, with an emphasis on vectors and matrices.

View Matrices and Complex Numbers on our Module Directory

Mathematical Skills

Want to develop your mathematical skills by solving problems that are varied in nature and difficulty? Keen to write mathematical arguments that explain why your calculations are answer a question? Examine problem-solving techniques for situations across mathematics, including calculus, algebra, combinatorics, geometry and mechanics.

View Mathematical Skills on our Module Directory

Discrete Mathematics

This module will provide you with a foundation of knowledge on the mathematics of sets and relations. You will develop an appreciation of mathematical proof techniques, including proof by induction.

View Discrete Mathematics on our Module Directory

Mathematical and Computational Modelling

This module introduces you to programming skills in the context of a range of mathematical modelling topics. Mathematical modelling skills will be an important focus alongside learning how to structure and implement codes in both Matlab and R. A key part of the module will be investigative open-ended computational modelling studies at both the group and individual level.

View Mathematical and Computational Modelling on our Module Directory

Mathematics Careers and Employability

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.

View Mathematics Careers and Employability on our Module Directory

Mathematics Careers and Employability

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.

View Mathematics Careers and Employability on our Module Directory

Statistics II

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.

View Statistics II on our Module Directory

Ordinary Differential Equations

The subject of ordinary differential equations is a very important branch of Applied Mathematics. Many phenomena from Physics, Biology, Engineering, Chemistry, Finance, among others, may be described using ordinary differential equations. To understand the underlying processes, we have to find and interpret the solutions to these equations. The last part of the module is devoted to the study of nonlinear differential equations and stability. This module provides an overview of standard methods for solving single ordinary differential equations and systems of ordinary differential equations, with an introduction to the underlying theory.

View Ordinary Differential Equations on our Module Directory

Real Analysis 1

How do you use quantifiers in analysis? What are the principles underlying proofs of basic theorems concerning limits, continuity and differentiability? Gain an understanding into real analysis, examining sequences and functions. Study relevant theorems (like Rolle’s and the Mean Value) and learn to reproduce elementary epsilon-delta arguments.

View Real Analysis 1 on our Module Directory

Vector Calculus

How do you define gradient, divergence and curl? Study the classical theory of vector calculus. Develop the two central theorems by outlining the proofs, then examining various applications and examples. Understand how to apply the ideas you have studied.

View Vector Calculus on our Module Directory

Real Analysis II

This module is a continuation of Real Analysis 1 and covers the Riemann Integral and its basic properties, integrability of continuous functions, and the Fundamental Theorem of Calculus as well as Improper Integrals. It then focuses on sequences and series of functions, and studies uniform convergence of functions and properties preserved under uniform convergence. Finally, the notion of Lebesgue integral is briefly introduced.

View Real Analysis II on our Module Directory

Linear Algebra (optional)

How do you prove simple properties of linear space from axioms? Can you check whether a set of vectors is a basis? How do you change a basis and recalculate the coordinates of vectors and the matrices of mapping? Study abstract linear algebra, learning to understand advanced abstract mathematical definitions.

View Linear Algebra (optional) on our Module Directory

Abstract Algebra (optional)

The module introduces you to the key abstract algebraic objects of groups, rings and fields and develops their fundamental theory. The theory will be illustrated and made concrete through numerous examples in settings that you will already have encountered.

View Abstract Algebra (optional) on our Module Directory

Optimisation (Linear Programming) (optional)

Are you able to solve a small linear programming problem using an appropriate version of the Simplex Algorithm? Learn to formulate an appropriate linear programming model and use the MATLAB computer package to solve linear programming problems. Understand the methods of linear programming, including both theoretical and computational aspects.

View Optimisation (Linear Programming) (optional) on our Module Directory

Numerical Methods (optional)

What are the processes and pitfalls of mathematical approximation? How do you carry out simple numerical processes “by hand”? Understand the practical techniques for carrying out numerical computations on a range of mathematical problems. Build your knowledge of mathematical computing. Learn how to implement and execute algorithms in Matlab.

View Numerical Methods (optional) on our Module Directory

Survival Analysis (optional)

What are the principles of actuarial modelling? And what are survival models? Examine how calculations in clinical trials, pensions, and life and health insurance require reliable estimates of transition intensities/survival rates. Learn how to estimate these intensities. Build your understanding of estimation procedures for lifetime distributions.

View Survival Analysis (optional) on our Module Directory

Analytical Mechanics (optional)

This module concerns the general description and analysis of the motion of systems of particles acted on by forces. Assuming a basic familiarity with Newton's laws of motion and their application in simple situations, you will develop the advanced techniques necessary to study more complicated, multi-particle systems. You will also consider the beautiful extensions of Newton's equations due to Lagrange and Hamilton, which allow for simplified treatments of many interesting problems and provide the foundation for the modern understanding of dynamics.

View Analytical Mechanics (optional) on our Module Directory

Quantum Mechanics (optional)

University of Essex enjoy breaking away from tradition. In this module you will break from “classical physics” and gain a conceptual understanding in quantum physics. You will develop skills in solving quantum mechanical problems associated with atomic and molecular systems.

View Quantum Mechanics (optional) on our Module Directory

Mathematics Careers and Employability

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.

View Mathematics Careers and Employability on our Module Directory

Complex Variables and Applications

Can you identify curves and regions in the complex plane defined by simple formulae? How do you evaluate residues at pole singularities? Study complex analysis, learning to apply the Residue Theorem to the calculation of real integrals.

View Complex Variables and Applications on our Module Directory

Capstone Project: Mathematics

This module will allow you to step out of the classroom and gain real experience in your selected branch of Mathematics that you could not gain from a lecture. You will be able to develop your ability to work independently on research and produce a project report on your topic of interest.

View Capstone Project: Mathematics on our Module Directory

Quantum Mechanics (optional)

University of Essex enjoy breaking away from tradition. In this module you will break from “classical physics” and gain a conceptual understanding in quantum physics. You will develop skills in solving quantum mechanical problems associated with atomic and molecular systems.

View Quantum Mechanics (optional) on our Module Directory

Group Theory (optional)

You will study abstract algebra by developing the theory of groups. The theory will be illustrated through examples in settings that you will already have encountered in your course.

View Group Theory (optional) on our Module Directory

Nonlinear Programming (optional)

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.

View Nonlinear Programming (optional) on our Module Directory

Combinatorial Optimisation (optional)

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.

View Combinatorial Optimisation (optional) on our Module Directory

Advanced Ordinary Differential Equations (optional)

The subject of Ordinary Differential Equations (ODEs) is a very important and fascinating branch in mathematics. An abundance of phenomena in physics, biology, engineering, chemistry, finance and neuroscience to name a few, may be described and studied using such equations. The module will introduce you to advanced topics and theories in ODEs and dynamical systems.

View Advanced Ordinary Differential Equations (optional) on our Module Directory

Graph Theory (optional)

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.

View Graph Theory (optional) on our Module Directory

Statistical Methods (optional)

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.

View Statistical Methods (optional) on our Module Directory

Partial Differential Equations (optional)

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.

View Partial Differential Equations (optional) on our Module Directory

Modelling Experimental Data

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.

View Modelling Experimental Data on our Module Directory

Research Methods

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.

View Research Methods on our Module Directory

Advanced Capstone Project: Actuarial Science, Data Science or Mathematics

Our Advanced Capstone Projects are opportunities for students to study independently a topic in mathematics, statistics and related areas (such as mathematical physics, data science, modelling and so on) and develop skills such as writing reports and giving presentations. You will be monitored by a supervisor, who will periodically set tasks and discuss the progress of the work. The key purpose of Advanced Capstone Projects is that you should be given the opportunity to show your strengths and be allowed a certain amount of freedom and leeway in how you complete the project. It will also provide opportunities for you to develop transferable communication, time- and task-management skills, through researching the topic and organising and producing the written and oral reports.

View Advanced Capstone Project: Actuarial Science, Data Science or Mathematics on our Module Directory

Machine Learning

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.

View Machine Learning on our Module Directory

Survival Analysis (optional)

What are the principles of actuarial modelling? And what are survival models? Examine how calculations in clinical trials, pensions, and life and health insurance require reliable estimates of transition intensities/survival rates. Learn how to estimate these intensities. Build your understanding of estimation procedures for lifetime distributions.

View Survival Analysis (optional) on our Module Directory

Data Visualisation (optional)

In a world increasingly driven by data, the need for analysis and visualisation is more important than ever. In this module you will look at data through the eyes of a numerical detective. You will work on the lost art of exploratory data analysis, reviewing appropriate methods for data summaries with the aim to summarise, understand, extract hidden patterns and identify relationships. You will then work on graphical data analysis, using simple graphs to understand the data, but also advanced complex methods to scrutinise data and interactive plots to communicate data information to a wider audience. For data analysis and visualisations you will use R-studio, and a combination of R-shiny applications and google visualisations for interactive plotting.

View Data Visualisation (optional) on our Module Directory

Stochastic Processes (optional)

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.

View Stochastic Processes (optional) on our Module Directory

Applied Statistics (optional)

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.

View Applied Statistics (optional) on our Module Directory

Introduction to Programming in Python (optional)

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.

View Introduction to Programming in Python (optional) on our Module Directory

Information Retrieval (optional)

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.

View Information Retrieval (optional) on our Module Directory

Text Analytics (optional)

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.

View Text Analytics (optional) on our Module Directory

Natural Language Engineering (optional)

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.

View Natural Language Engineering (optional) on our Module Directory

Data Science and Decision Making (optional)

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.

View Data Science and Decision Making (optional) on our Module Directory

Neural Networks and Deep Learning (optional)

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.

View Neural Networks and Deep Learning (optional) on our Module Directory

Big-Data for Computational Finance (optional)

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.

View Big-Data for Computational Finance (optional) on our Module Directory

Teaching

  • Courses are taught by a combination of lectures, laboratory work, assignments, and individual and group project activities
  • Group work
  • A significant amount of practical lab work will need to be undertaken for written assignments and as part of your learning

Assessment

  • You are assessed through a combination of written examinations and coursework
  • All our modules include a significant coursework element
  • You receive regular feedback on your progress through in-term tests
  • Courses are assessed on the results of your written examinations, together with continual assessments of your practical work and coursework

Fees and funding

Home/UK fee

£9,250

International fee

£17,700

EU students commencing their course in the 2021-22 academic year will be liable for the International fee.

Fees will increase for each academic year of study.

Home/UK fee information

International fee information

What's next

Open Days

Our events are a great way to find out more about studying at Essex. We run a number of Open Days throughout the year which enable you to discover what our campus has to offer. You have 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

Check out our Visit Us pages to find out more information about booking onto one of our events. And if the dates aren’t suitable for you, feel free to book a campus tour here.

Applying

Applications for our full-time undergraduate courses should be made through the Universities and Colleges Admissions Service (UCAS). Applications are online at: www.ucas.com. Full details on this process can be obtained from the UCAS website in the how to apply section.

Our UK students, and some of our EU and international students, who are still at school or college, can apply through their school. Your school will be able to check and then submit your completed application to UCAS. Our other international applicants (EU or worldwide) or independent applicants in the UK can also apply online through UCAS Apply.

The UCAS code for our University of Essex is ESSEX E70. The individual campus codes for our Loughton and Southend Campuses are 'L' and 'S' respectively.

You can find further information on how to apply, including information on transferring from another university, applying if you are not currently at a school or college, and applying for readmission on our How to apply and entry requirements page.

Applicant Days and interviews

Resident in the UK? If your application is successful, we will invite you to attend one of our applicant days. These run from January to April and give you the chance to explore the campus, meet our students and really get a feel for life as an Essex student.

Some of our courses also hold interviews and if you're invited to one, this will take place during your applicant day. Don't panic, they're nothing to worry about and it's a great way for us to find out more about you and for you to find out more about the course. Some of our interviews are one-to-one with an academic, others are group activities, but we'll send you all the information you need beforehand.

If you're outside the UK and are planning a trip, feel free to email applicantdays@essex.ac.uk so we can help you plan a visit to the University.

Colchester Campus

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.

 

Virtual tours

If you live too far away to come to Essex (or have a busy lifestyle), no problem. Our 360 degree virtual tours allows you to explore our University from the comfort of your home. Check out our Colchester virtual tour and Southend virtual tour to see 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.


Find out more

The University makes every effort to ensure that this information on its programme specification is accurate and up-to-date. Exceptionally it can be necessary to make changes, for example to courses, facilities or fees. Examples of such reasons might include, but are not limited to: strikes, other industrial action, staff illness, severe weather, fire, civil commotion, riot, invasion, terrorist attack or threat of terrorist attack (whether declared or not), natural disaster, restrictions imposed by government or public authorities, epidemic or pandemic disease, failure of public utilities or transport systems or the 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 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.

Ask us a question
Ask us a question

Want to quiz us about your course? Got a question that just needs answering? Get in touch and we’ll do our best to email you back shortly.