About the course
Harvard Business Review recently described the job of Data Scientist as “the sexiest job of the 21st century”. Data science is about doing some detective work and carrying out the investigations needed to inform important decisions and to predict new trends. 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.
At Essex, we help you to understand how utilising the speed and processing-power of computers can assist in using data to make better decisions. You discover the new methods and the smart, unusual questions needed to make sense of both numerical and textual data. Your course balances solid theory with practical application through exploring topics including:
- Computer science and programming
- Databases and mathematical skills
- Ethical issues around the use and processing of data
- Specialist skills in the areas of big data, data analytics and data science
You don’t need to know any computer programming before you begin the course, and as you study a module on the mathematics required for data science, you don’t need an A-level in mathematics either.
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 School of Computer Science and Electronic Engineering, but also benefit from input from our Department of Mathematical Sciences, Essex Business School, and our International Academy.
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.
Your education extends beyond the university campus. We support you extending your education through providing the option of an additional year at no extra cost. The four-year version of our degree allows you to spend the third year studying abroad or employed on a placement, while otherwise remaining identical to the three-year course.
Studying abroad allows you to experience other cultures and languages, to broaden your degree socially and academically, and to demonstrate to employers that you are mature, adaptable, and organised. Popular destinations include:
- The United States
- New Zealand
- Latin America
- The Middle East
- Hong Kong
Alternatively, you can spend your third year on a placement with an external organisation, as part of one of our placement year degrees. The learning outcomes associated with this programme focus on using the specialist technical skills acquired in the first two years of the course and developing communications skills with customers. You will be responsible for finding your placement, but with support and guidance provided by both your department and our Employability and Careers Centre.
Students are provided with support to secure a placement. Recent placements undertaken by our students have been with ARM, Microsoft, Intel, Nestlé, British Aerospace, and the Rutherford Appleton Laboratory, as well a range of SME software and hardware companies.
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 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 Luca Citi – machine learning, learning from biological signals and data (EEG, etc)
- Dr Adrian Clark – automatic construction of vision systems using machine learning and evaluation of algorithms, data visualisation and augmented reality
- Professor Maria Fasli – analysis of structured/unstructured data, machine learning, adaptation, semantic information extraction, ontologies, data exploration, recommendation technologies
- Professor John Gan – machine learning for data modelling and analysis, dimensionality reduction and feature selection in high-dimensional data space
- Dr Udo Kruschwitz – natural language processing, analysis textual/unstructured data, information retrieval
- Professor Massimo Poesio – cognitive science of language, text mining, computational linguistics
- Professor Edward Tsang – applied AI, constraint satisfaction, computational finance and economics, agent-based simulations
- 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
- 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
Demand for skilled graduates in the areas of 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.
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.
Our recent graduates have gone on to work for a wide range of high-profile companies including:
- Royal Bank of Scotland
- Force India F1
Our School has 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 the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.
Studying at Essex is about discovering yourself, so your course combines compulsory and optional modules to make sure you gain key knowledge in the discipline, while having as much freedom as possible to explore 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 just a selection of those available. The opportunity to take optional modules will depend on the number of core modules within any year of the course. In many instances, the flexibility to take optional modules increases as you progress through the course.
Our Programme Specification gives more detail about the structure available to our current first-year students, including details of all optional modules.
Please note that depending on your entry grades you will take either Mathematics for Data Science or Linear Mathematics in your first year, but not both.
This module introduces students to three key aspects of professional development. These are product development, team work, and project management. In teams of six you work throughout the year to develop a performance for a Nao robot, with a Python module at the core of the product. Apart from the core skills you also learn about contextual issues such as intellectual Property (IP), sustainability, ethical issues, and health & safety. The module is a great opportunity to build a product in a team of fellow students and have that wonderful feeling of having created something original.
View 'Professional Development' on our Module Directory
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 packages.
View 'Statistics I' on our Module Directory
The aim of this module is to provide an introduction to the fundamental concepts of computer programming. After completing this module, students will be expected to be able to demonstrate an understanding of the basic principles and concepts that underlie the procedural programming model, explain and make use of high-level programming language features that support control, data and procedural abstraction. Also, they will be able to analyse and explain the behaviour of simple programs that incorporate standard control structures, parameterised functions, arrays, structures and I/O.
View 'Introduction to Programming' on our Module Directory
Want to become a Java programmer? Topics covered in this module include control structures, classes, objects, inheritance, polymorphism, interfaces, file I/O, event handling, graphical components, and more. You will develop your programming skills in supervised lab sessions where help will be at hand should you require it.
View 'Object-Oriented Programming' on our Module Directory
Databases are everywhere. They are employed in banking, production control and the stock market, as well as in scientific and engineering applications. For example, the Human Genome Project had the goal of mapping the sequence of chemical base pairs which make up human DNA. The result is a genome database. This module introduces the underlying principles of databases, database design and database systems. It covers the fundamental concepts of databases, and prepares the student for their use in commerce, science and engineering.
View 'Introduction to Databases' on our Module Directory
The aim of this module is to provide students with an introduction to the principles and technology that underlie internet applications and the techniques used in the design and construction of web sites. Students showcase their skills by designing and building both client and server components of a data driven web site.
View 'Web Development' on our Module Directory
Can you perform simple operations on matrices? 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 'Linear Mathematics (optional)' on our Module Directory
This course covers the principles of project management, team working, communication, legal issues, finance, and company organisation. Working in small teams, students will go through the full project life-cycle of design, development and implementation, for a bespoke software requirement. In this course, students gain vital experience to enable them to enter the computer science/Electrical engineering workforce, with a degree backed by the British Computer Society, and by the Institute of Engineering and Technology.
View 'Group Project & Industrial Practice' on our Module Directory
The aim of this module is to build on the foundations of data and information systems laid down in the first year, learn how to design and manage fully structured data repositories and explore the rather different principles and techniques involved in representing, organising and displaying unstructured information.
View 'Databases and Information Retrieval' on our Module Directory
Artificial intelligence will be a great driver of change in the coming decades. This module provides an introduction to three fundamental areas of artificial intelligence: search, knowledge representation, and machine learning. These underpin all more advanced areas of artificial intelligence and are of central importance to related fields such as computer games and robotics. Within each area, a range of methodologies and techniques are presented, with emphasis being placed on understanding their strengths and weaknesses and hence on assessing which is most suited to a particular task.
View 'Artificial Intelligence' on our Module Directory
This application-driven course teaches you how to formulate and solve real-world problems concerned with decision-making in modern management. You learn how to build simulation models, how to run simulations using simple Excel spreadsheets, and, to evaluate and interpret output results.
View 'Introduction to Quantitative Management' on our Module Directory
This module aims to equip students with the main principles guiding the activities involved in software development throughout its lifecycle, including software requirements, object-oriented analysis and design, software validation and testing, and software maintenance and software evolution.
View 'Software Engineering (optional)' on our Module Directory
This module extends the students' knowledge and skills in object-oriented application programming by a treatment of further Java language principles and of important Application Programming Interfaces (APIs). The Java Collections API is explored in some more detail with emphasis on how to utilise these classes to best effect. A particular focus will be on the interaction with databases (e.g. via JDBC) and on writing secure applications.
View 'Application Programming (optional)' on our Module Directory
Data structures and algorithms lie at the heart of Computer Science as they are the basis for the efficient solution of programming tasks. In this module, students will study core algorithms and data structures, as well as being given an introduction to algorithm analysis and basic computability.
View 'Data Structures and Algorithms (optional)' on our Module Directory
Can you formulate an appropriate linear programming model? Are you able to solve a small linear programming problem using an appropriate version of the Simplex Algorithm? Can you use the MATLAB computer package to solve linear programming problems? Understand the methods of linear programming, including both theoretical and computational aspects.
View 'Linear Programming (Half Course) (optional)' on our Module Directory
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
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 (optional)' on our Module Directory
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
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.
View 'Bayesian Computational Statistics (optional)' on our Module Directory
What is the architecture of Socket programming? What mechanisms can synchronise distributed programs? And what are the techniques for the correct operation of distributed programs? Understand the theory and practice of distributed computing technology. Examine conventional techniques (eg Sun Java RMI) and web-based middleware techniques, like web services.
The world demands software systems with ever increasing richness of behaviours and degrees of complexity. However, traditional software engineering techniques, which were inherited with relatively minor adaptations from other, older branches of engineering, have been struggling to scale up to the challenges posed by modern software systems. As a result, a large proportion (as much as a quarter!) of software projects based on traditional methods end up being cancelled at some point in their lifecycle, with many more being late, over budget and with less features than initially stipulated. In this module you will learn why traditional software engineering techniques fail, and you will become very familiar (through lectures, labs, videos and a large group project) with a novel set of techniques, known as Extreme Programming and Agile Software Development, which fundamentally solve these problems. In the last decade, these techniques have been so successful that today as many as 80% of all projects adopt agilite methods.
View 'Large Scale Software Systems and Extreme Programming (optional)' on our Module Directory
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
Want to learn about more advanced programming constructs and techniques? Topics covered in this module include concurrency, distributed programming, design patterns, and others. We will also take a closer look at some of the programming concepts taught previously. The module features a substantial, non-trivial assignment that should help you to hone - and demonstrate - your programming skills.
View 'Advanced Programming (optional)' on our Module Directory
The highlight of our undergraduate degree courses is the individual capstone project. This project module provides students with the opportunity to bring together all the skills they have gained during their degree and demonstrate that they can develop a product from the starting point of a single 1/2 page description, provided either by an academic member of staff or an external company. In all the student spends 450 hours throughout the academic year, reporting to their academic tutor, and in the case of company projects, to a company mentor. All projects are demonstrated to external companies on our Project Open Day.
View 'Individual Project (optional)' on our Module Directory
On a placement year you gain relevant work experience within an external business or organisation, giving you a competitive edge in the graduate job market and providing you with key contacts within the industry. The rest of your course remains identical to the three-year degree.
On your year abroad, you have the opportunity to experience other cultures and languages, to broaden your degree socially and academically, and to demonstrate to employers that you are mature, adaptable, and organised. The rest of your course remains identical to the three-year degree.
- 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
- 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
If you already have your results and want to apply for 2016 entry through Clearing, complete our Clearing application form
and we’ll get back in touch with you or give us a ring
to discuss your grades.
IELTS entry requirements
English language requirements for applicants whose first language is not English: IELTS 6.0 overall. (Different requirements apply for second year entry.)
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.
If you are an international student requiring a Tier 4 visa to study in the UK please see our immigration webpages for the latest Home Office guidance on English language qualifications.
Other English language qualifications may be acceptable so please contact us for further details. If we accept the English component of an international qualification then it will be included in the information given about the academic levels required. Please note that date restrictions may apply to some English language qualifications.