Program Overview
Data is the lifeblood of our society. 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.
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.
Our BSc Data Science and Analytics (including foundation year) is open to Home and EU students. It will be suitable for you if your academic qualifications do not yet meet our entrance requirements for the three-year version of this course and you want a programme that increases your subject knowledge as well as improves your English language and academic skills.
This four-year course includes a foundation year (Year Zero), followed by a further three years of study. During your Year Zero, you study four academic subjects relevant to your chosen course as well as a compulsory English language and academic skills module.
You are an Essex student from day one, a member of our global community based at the most internationally diverse campus university in the UK.
After successful completion of Year Zero in our Essex Pathways Department, you progress to complete your course with our Department of Mathematical Sciences.
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 structured and unstructured data.
Your course balances solid theory with practical application through exploring topics including:
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Mathematical skills
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Computer science and programming
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Statistics and operations research
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Artificial intelligence, databases and information retrieval
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Ethical issues around the use and processing of data
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Specialist skills in the areas of big data, data analytics and data science
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.
Why we're great.
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You join a community of scholars leading the way in technological research and development.
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We are home to many of the world's top scientists and engineers in their field.
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You have access to our ultramodern facilities at our new STEM building that provide real-world experience.
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 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:
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Dr Luca Citi – machine learning, learning from biological signals and data (EEG, etc)
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Dr Hongsheng Dai – computational Bayesian statistics
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Professor Maria Fasli – machine learning, adaptation, semantic information extraction, ontologies, data exploration, recommendation technologies
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Professor Berthold Lausen – biostatistics, classification and clustering, data science education, event time data, machine learning, predictive modelling
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Professor Abdel Salhi – data mining, numerical analysis, optimisation
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Dr Spyros Vrontos – actuarial mathematics and actuarial modelling
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Dr Xinan Yang – approximate dynamic programming, Markov decision process
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:
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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.
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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.
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We also research how physical processes develop in time and space. Applications of this range from modelling epilepsy to modelling electronic cables.
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Our optimisation experts work out how to do the same job with less resource, or how to do more with the same resource.
Specialist facilities
By studying within our
Essex Pathways Department
for your foundation year, you will have access to all of the facilities that the University of Essex has to offer, as well as those provided by our department to support you:
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We provide computer labs for internet research; classrooms with access to PowerPoint facilities for student presentations; AV facilities for teaching and access to web-based learning materials
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Our new
Student Services Hub
will support you and provide information for all your needs as a student
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Our social space is stocked with hot magazines and newspapers, and provides an informal setting to meet with your lecturers, tutors and friends
Our School of Computer Science and Electronic Engineering also offers excellent on-campus facilities:
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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
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All computers run either Windows 10 or are dual boot with Linux
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Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
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Students have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
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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 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.
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:
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Aviva
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AXA
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BT
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Profusion
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EDS
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Mondaq
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IBM
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Royal Bank of Scotland
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Accenture
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Buck Consultants
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Google
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Force India F1
Our Schools 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.
“I knew I wanted to do data science after discovering that it was the perfect subject for people who enjoy both computing and maths. I decided to study at Essex because it was one of the few universities who offered a degree in data science; it was also one of the highest rated universities in the UK. I’m currently enjoying programming the most, purely because I love problem solving, but I’ve enjoyed all of the modules I have studied so far. All of my professors and lecturers are helpful – they devote a lot of their time to us as students.
“I want to travel once I have finished university and therefore work long-distance – which in today’s modern world is definitely possible! Essex partners with a lot of businesses and companies, and gives students opportunities to gain highly useful work experience through a placement year. I think studying at Essex will put me in a great place when I graduate.”
Andreas Loucas, BSc Data Science and Analytics student
Program Outline
Course structure
We offer a flexible course structure with a mixture of core/compulsory modules, and optional modules chosen from lists.
Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field. The course content is therefore reviewed on an annual basis to ensure our courses remain up-to-date so modules listed are subject to change.
We understand that deciding where and what to study is a very important decision for you. We’ll make all reasonable efforts to provide you with the courses, services and facilities as described on our website. However, if we need to make material changes, for example due to significant disruption, or in response to COVID-19, we’ll let our applicants and students know as soon as possible.
Components
Components are the blocks of study that make up your course. A component may have a set module which you must study, or a number of modules from which you can choose.
Each component has a status and carries a certain number of credits towards your qualification.
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Status
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What this means
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Core
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You must take the set module for this component and you must pass. No failure can be permitted.
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Core with Options
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You can choose which module to study from the available options for this component but you must pass. No failure can be permitted.
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Compulsory
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You must take the set module for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.
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Compulsory with Options
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You can choose which module to study from the available options for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.
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Optional
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You can choose which module to study from the available options for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.
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The modules that are available for you to choose for each component will depend on several factors, including which modules you have chosen for other components, which modules you have completed in previous years of your course, and which term the module is taught in.
Modules
Modules are the individual units of study for your course. Each module has its own set of learning outcomes and assessment criteria and also carries a certain number of credits.
In most cases you will study one module per component, but in some cases you may need to study more than one module. For example, a 30-credit component may comprise of either one 30-credit module, or two 15-credit modules, depending on the options available.
Modules may be taught at different times of the year and by a different department or school to the one your course is primarily based in. You can find this information from the
module code
. For example, the module code HR100-4-FY means:
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HR
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100
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4
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FY
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The department or school the module will be taught by.
In this example, the module would be taught by the Department of History.
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The module number.
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The
UK academic level
of the module.
A standard undergraduate course will comprise of level 4, 5 and 6 modules - increasing as you progress through the course.
A standard postgraduate taught course will comprise of level 7 modules.
A postgraduate research degree is a level 8 qualification.
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The term the module will be taught in.
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AU
: Autumn term
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SP
: Spring term
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SU
: Summer term
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FY
: Full year
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AP
: Autumn and Spring terms
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PS:
Spring and Summer terms
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AS:
Autumn and Summer terms
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Year 0
Year 1
Year 2
Final Year
Develop your problem solving skills in this module, as you are introduced to Statistical and Mathematical concepts with a particular focus on mechanics. You become familiar with R software, one of the most widely used statistical analysis software in the world, and learn how to use it to analyse and interpret data. You study simple concepts and techniques like data description and distribution; before moving on to more complex topics and theories including Newton’s laws of motion and the concepts of Mechanical energy. While also covering everything from probability rules and hypothesis testing to advanced algebra – you will be well equipped to present your solutions and findings to an audience with no specialist knowledge of Statistics and Mechanics.
View Mathematical Methods and Statistics on our Module Directory
Want to know the basic mathematical techniques of algebra? To understand calculus? To apply methods of differentiation and integration to a range of functions? Build the basic, then more advanced, mathematical skills needed for future study. Learn to solve relevant problems, choosing the most suitable method for solution.
View Essential Mathematics on our Module Directory
This blended-learning module is designed to support students in their academic subject disciplines and to strengthen their confidence in key skills areas such as: academic writing, research, academic integrity, collaborative and reflective practices.
The students are supported through the use of subject-specific materials tailored to their chosen degrees with alignment of assessments between academic subject modules and the skills module.
View Research and Academic Development Skills on our Module Directory
How do you test and evaluate the operation of simple computer programs? Or develop a program using tools in the Python programming language? Study the principles of procedural computing programming. Examine basic programming concepts, structures and methodologies. Understand good program design, learn to correct coding and practice debugging techniques.
View Computer Programming on our Module Directory
Our Team Project Challenge gives you the opportunity to develop a range of professional skills by working as part of a small student team on a specific project. The projects are research-based and incorporate the concepts of specifications, design, and implementation. You’ll learn about sustainability, project and time management, design, legal issues, health and safety, data analysis and presentation, team reporting, and self-evaluation.
You’ll also develop skills such as critical thinking and problem solving, agility, leadership, collaboration across networks, and effective oral and written communication, as well as curiosity and imagination, all of which will enhance your knowledge, confidence and social skills necessary to innovate and succeed in a competitive global environment.
View Team Project Challenge on our Module Directory
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
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
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 D. 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
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
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 Team Project Challenge 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 Introduction to Artificial Intelligence on our Module Directory
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
COMPONENT 05: COMPULSORY WITH OPTIONS
MA214-5-SP or MA216-5-SP
(15 CREDITS)
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) 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 on our Module Directory
You'll be introduced to a range of important concepts which are used in all areas of mathematics and statistics. This module is structured in such a way that during learning sessions you'll develop good practical understanding of these concepts via discussion and exercises, and have an opportunity to ask questions. Theory is introduced via recorded videos and the corresponding notes published on Moodle, and also via recommendations of textbooks. The contact hours are dedicated to interactive activities such as lab exercises and flipped lecture quizzes; also you will have some additional formative tests in Moodle.
View Matrices and Complex Numbers on our Module Directory
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
This is a two-term project for which a student should undertake about 150 hours work. Students will gain experience of some branch of mathematics, statistics, operational research or the interface of these disciplines with other fields. The student should also gain experience of solo work involving research concerning some previously unknown topic, the production of a project report and an oral examination.
View Capstone Project: Mathematics 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 Linear Regression Analysis 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 on our Module Directory
This module offers you an understanding of standard IR models, of their merits and limitations, and teaches you how to design and implement a standard information retrieval system. Discover the essential foundations of information retrieval and gain solid, applicable knowledge of state-of-the-art search technology. Explore advanced concepts of search applications such as personalisation, profiling and contextual search.
View Information Retrieval on our Module Directory
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 on our Module Directory
COMPONENT 06: COMPULSORY WITH OPTIONS
Option(s) from list
(30 CREDITS)
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
Teaching
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Our classes are run in small groups, so you receive a lot of individual attention
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Courses are taught by a combination of lectures, laboratory work, assignments, and individual and group project activities
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Group work
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A significant amount of practical lab work will need to be undertaken for written assignments and as part of your learning
Assessment
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In Year Zero, your assessed coursework will generally consist of essays, reports, in-class tests, individual or group oral presentations
