BSc Data Science
|Typical offer||AAA-AAB; IB: 36-34; BTEC: DDD|
Data Science at the University of Exeter
BSc Data Science is a technical degree focussing on the mathematical and analytical skills needed to begin a career as a data scientist or analyst.
You will study modules chosen from across Computer Science and Mathematics and will be introduced to both data science theory and applications. In your first year you will study subjects at the core of data science including programming, machine learning and statistics.
You will also be provided with an overview of the social and governance context for data science.
As your degree progresses you will have the freedom to choose from advanced optional modules. You will also undertake group and individual projects, often using real data or challenges to solve problems and find answers as a group or on your own.
The skills you will learn on this degree are highly transferable and apply to a diverse range of sectors offering huge potential for a varied and exciting career.
The modules we outline here provide examples of what you can expect to learn on this degree course based on recent academic teaching. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand.
The BSc Data Science is an innovative interdisciplinary taught course designed with industry and aimed at those wishing to work or research in data science. The course will cover the core areas of mathematics and computer science. It will also include new modules which will introduce you to applied data science as well as social context. Research projects in each academic year will allow you to develop research and project management skills in an area of interest, using real world datasets, guided by a leading academic supervisor.
Please note: This programme is currently in development. The modules listed below are indicative of the topic areas you can expect to cover on the course, but are subject to change.
In your first year, you will be introduced to the fundamental technical and professional skills needed to successfully engage with machine learning, artificial intelligence and data science. You will learn core knowledge and practical skills relating to data structures and algorithms that are commonly applied in this topic area, as well as some of the most common techniques and applications of AI and machine learning.
In year 2 you will gain theoretical and practical understanding of some of the core techniques in machine learning and data science. You will also learn how techniques are applied to workflows linked to tackling challenges in real-world social issues. Through lectures and practical exercises, you will develop vital professional and interpersonal skills needed to work effectively in the mathematical and digital sector, including project management and teamwork.
Year 3 will comprise group and individual work as you carry out your final year project. Optional modules will give you the opportunity to specialise in areas that are most suited to your interests, giving you a strong foothold for future career development.
Entry requirements 2020
A level: AAA-AAB;
GCE AL Maths grade B
Candidates may offer GCE AL Maths, Pure Maths or Further Maths.
IB Maths HL5
BTEC Extended Diploma (2010 and 2016)
Applicants studying a BTEC Extended Diploma will also require GCE AL Maths grade B.
For any questions relating to entry requirements please contact the team via our online form or 01392 724061
International students should check details of our English language requirements
If your academic qualifications or English language skills do not meet our entry requirements our INTO University of Exeter centre offers a range of courses to help you reach the required language and academic standards.
International Foundation programmes
Preparation for entry to Year 1 of an undergraduate degree:
Please read the important information about our Typical offer.
For full and up-to-date information on applying to Exeter and entry requirements, including requirements for other types of qualification, please see the Applying section.
Learning and teaching
We make use of a variety of teaching styles, including lectures, seminars, workshops and tutorials. Most modules involve two or three lectures per week, so you would typically have about 10 lectures each week. In addition, workshops and tutorials support and develop what you’ve learnt in lectures and enable you to discuss the lecture material and coursework in more detail.
You’ll have over 15 hours of direct contact time per week with your tutors and you will be expected to supplement your lectures with independent study. You should expect your total workload to average about 40 hours per week during term time.
We’re actively engaged in introducing new methods of learning and teaching, including increasing use of interactive computer-based approaches to learning through our virtual learning environment, where the details of all modules are stored in an easily navigable website. You can access detailed information about modules and learning outcomes and interact through activities such as the discussion forums.
We aim to provide a supportive environment where students and staff work together in an informal and friendly atmosphere. The department has a student-focused approach to teaching, whereby all members of staff deal with questions on an individual basis. We operate an open door policy, so it is easy to consult individual members of staff or to fix appointments with them via email. We are a friendly group of staff and you will get to know us well during your time here.
A research and practice led culture
We believe every student benefits from being taught by experts active in research and practice. You will discuss the very latest ideas, research discoveries and new technologies in seminars and in the field and you will become actively involved in a research project yourself. All our academic staff are active in internationally-recognised scientific research across a wide range of topics. You will also be taught by leading industry practitioners.
Modules are assessed by a combination of continuous assessment through small practical exercises, project work, essay writing, presentations and exam.
You must pass your first year assessment in order to progress to the second year, but the results do not count towards your degree classification. For three-year programmes, the assessments in the second and third years contribute to your final degree classification. For four-year programmes the assessments in the second, third and fourth years all contribute to your final degree classification.
There is an established strong market demand for suitably skilled data scientists and data science skills are increasingly being sought across the sectors, particularly by the finance and accounting industries, supermarkets, online retailers such as Amazon, and the NHS.
This Data Science course has been developed with partner employers, including IBM, the Met Office, South West Water, Black Swan and Oxygen House and has been designed to deliver skills that are most valued by employers. Modules will use the employers’ methods, platforms, software and data, to ensure that they are fully reflective of workplace practice. Throughout your studies you will conduct individual and group projects using real world data sets.
This course will prepare you to be an outstanding dynamic problem solver with an excellent technical skillset. In addition to learning the core principles of Mathematics and Computer Science, you will learn soft skills that employers have told us they are looking for, such as communication and presentation skills, and the ability to work effectively in a team.
The inclusion of individual- and group-based project work in every academic year will offer you an opportunity to apply your skills to solve real world problems and prepare you for future employment.