Undergraduate Course

BSc Data Science and Analytics

(Including Foundation Year)

Now In Clearing
BSc Data Science and Analytics

Overview

The details
Data Science and Analytics (Including Foundation Year)
I1GF
October 2018
Full-time
4 years
Colchester Campus
Essex Pathways

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 numerical and textual data. Your course balances solid theory with practical application through exploring topics including:

  • Mathematical skills
  • 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

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.
  • You join a community of scholars leading the way in technological research and development.
  • We are home to many of the world's top scientists and engineers in their field.
  • 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 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

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:

  • 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
  • Our new Student Services Hub will support you and provide information for all your needs as a student
  • 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:

  • 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
  • Students 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.

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. And an incredible 88% of our Department of Mathematical Sciences and 88% of our School of Computer Science and Electronic Engineering students are in professional employment or postgraduate study within six months of graduating from Essex (DLHE 2016).

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:

  • Aviva
  • AXA
  • BT
  • Profusion
  • EDS
  • Mondaq
  • IBM
  • Royal Bank of Scotland
  • Accenture
  • Buck Consultants
  • Google
  • 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 the university’s Employability and Careers Centre 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

Entry requirements

Clearing entry requirements

If you have already received your results, use our Clearing application form to apply for 2018 entry through Clearing. You will be asked to provide details of your qualifications and grades.

English language requirements

English language requirements for applicants whose first language is not English: IELTS 5.5 overall. Specified component grades are also required for applicants who require a Tier 4 visa to study in the UK.

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

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.

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

Our Year 0 courses are only open to UK and EU applicants. If you’re an international student, but do not meet the English language or academic requirements for direct admission to your chosen degree, you could prepare and gain entry through a pathway course. Find out more about opportunities available to you at the University of Essex International College.

Structure

Example structure

We offer a flexible course structure with a mixture of compulsory modules and options chosen from lists. Below is just one example of a combination of modules you could take. For a full list of optional modules you can look at the course’s Programme Specification.

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.

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.

Computers and Electronics

Want to use a modern Unix/Linux based operating system? To build and text digital logic circuits and electronic circuits with a computer-based electronics simulator? Gain fundamental knowledge in computer science and electronic engineering. Understand computer architectures and components, and operating systems. Examine the principles of electronics and simple electronic circuits.

View Computers and Electronics on our Module Directory

Mathematics and Statistics (optional)

Want to use mathematical techniques to solve problems? And to calculate basic statistical measures? Develop mathematical skills like number work, algebra, geometry, probability and statistics that can be used on any course requiring mathematics above Higher GCSE standard. Learn to solve relevant problems and know how to present data clearly.

View Mathematics and Statistics (optional) on our Module Directory

Computer Programming

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

Team Project Challenge

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 Team Project Challenge on our Module Directory

Discrete Mathematics

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

View Discrete 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 packages.

View Statistics I on our Module Directory

Introduction to Programming

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

Object-Oriented Programming

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

Introduction to Databases

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

Web Development

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

Linear Mathematics (optional)

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

Group Project and Industrial Practice

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 and Industrial Practice on our Module Directory

Databases and Information Retrieval

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

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

Introduction to Quantitative Management

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

Software Engineering (optional)

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

Application Programming (optional)

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 (optional)

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

Web Application Programming (optional)

The aim of this module is to provide an understanding of the principles that underlie the design of web applications, and to provide practical experience of the technologies used in their construction.

View Web Application Programming (optional) on our Module Directory

Modelling Experimental Data (optional)

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

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

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

Bayesian Computational Statistics (optional)

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

Combinatorial Optimisation (optional)

In this module you will not only learn what underpins the algorithms used where variables are integer, but also apply these algorithms to solve integer and mixed integer problems with cutting-plane algorithms.

View Combinatorial Optimisation (optional) on our Module Directory

Large Scale Software Systems and Extreme Programming (optional)

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

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

Advanced Programming (optional)

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

Individual Capstone Project Challenge (optional)

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 Capstone Project Challenge (optional) on our Module Directory

Teaching

  • Our classes are run in small groups, so you receive a lot of individual attention
  • 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

  • Within our International Academy, your assessed coursework will generally consist of essays, reports, in-class tests, individual or group oral presentations

Fees and funding

Home/EU fee

£9,250

International fee

TBC

Fees will increase for each academic year of study.

Home and EU 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.

2018 Open Days (Colchester Campus)

  • Saturday, September 15, 2018
  • Saturday, October 27, 2018

How to apply during Clearing

Once you’ve checked that we have the right course for you, applying couldn’t be simpler. Fill in our quick and easy Clearing application form with as much detail as you can. We’ll then take a look and get back to you with a decision. There’s no need to call us to apply; just do it all online.

Find out more about Clearing

Interviews

We don’t interview all applicants during Clearing, however, we will only make offers for the following course after a successful interview:

  • BA Multimedia Journalism
  • BSc Nursing (Adult)
  • BSc Nursing (Mental Health)
  • BA Social Work

The interview allows our academics to find out more about you, and in turn you’ll be able to ask us any questions you might have. Further details will be emailed to you if you are shortlisted for interview.


Apply now
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