Data Science BSc

Course overview

We’re living in the age of big data. From the rise of smart devices to the day-to-day services we use, the field of data science has become crucial to making sense of all this data — and keeping major organisations ahead of this fast-moving industry.

At Leeds, we see data science as an exciting and multidisciplinary field broadly at the intersection of mathematics, computer science and communication.

Our Data Science BSc will equip you with advanced mathematical techniques such as machine learning and mathematical modelling to find patterns and to separate features in big data. You’ll also become an effective communicator and strategic thinker, able to understand and resolve complex problems – both individually and collaboratively in multi-professional and interdisciplinary teams. By the time you graduate, you’ll have the same key skills in programming, modelling and strategic communication that successful data scientists in industry have, preparing you for your next steps in your career.

The extensive skill set you’ll build throughout your degree is highly sought after across almost every industry worldwide, making data science an exceptionally lucrative career choice. If you enjoy doing creative detective work using the scientific tools and techniques to disentangle and tackle real-world problems, then data science at Leeds is the right course for you!

Why study at Leeds:

• Our globally-renowned research in data science, applied mathematics, statistics and financial mathematics feeds directly into the course, shaping your learning with the latest thinking and giving you an insight into relevant topics in industry.
• Learn how to tackle real-world-inspired data problems in a supportive and collaborative environment taught by expert academics who specialise in a variety of areas in the field.
• Tailor the course to suit your interests through interdisciplinary discovery modules that enable you to use your data insights throughout the course to address global challenges such as sustainability.
• Access excellent facilities and computing equipment in collaborative hackathon-style workshops, complemented by social areas, communal problem-solving spaces and quiet study rooms.
• Broaden your experience and enhance your career prospects with our industrial placement opportunities or study abroad programmes.
• Make the most of your time at Leeds by joining our student society MathSoc where you can meet more of your peers, enjoy social events and join the MathSoc football or netball team.

Course details

This course is, by design, multidisciplinary and follows a centred approach. In first year, the foundations are laid for mathematics, programming and communication, which are then further developed in second and third year.

The course delivery brings together scientific method, teamwork, communication, collaboration and creative problem solving. These skills will be blended in data science applications such as modelling, simulations, creating mock data sets and optimisation questions. You’ll use basic illustrative examples and motivational real-world case studies to understand how this foundational knowledge underpins advanced data science tools and techniques – and how they could be applied to help solve grand challenges too.

You’ll also have the chance to tailor your course with a selection of optional modules – from statistics, pure as well as applied mathematics to a range of exciting discovery modules – and get involved in your own 40-credit research project in third year.

Each academic year, you'll take a total of 120 credits.

Course Structure

The list shown below represents typical modules/components studied and may change from time to time. Read more in our terms and conditions.

Most courses consist of compulsory and optional modules. There may be some optional modules omitted below. This is because they are currently being refreshed to make sure students have the best possible experience. Before you enter each year, full details of all modules for that year will be provided.

For more information and a list of typical modules available on this course, please read Data Science BSc in the course catalogue.

Year 1

In your first year of study, you’ll have the opportunity to learn a range of foundational topics that’ll serve as the basis for the rest of your studies.

In mathematics, you’ll cover topics such as linear algebra, calculus, statistics and probability. On the computational side, you’ll learn the basics of functional programming and software engineering, including data structures, parallelisation and effective algorithms for dealing with big data.

You'll also be exposed to basic principles of information theory, data visualisation, geographical information systems (GIS), natural language processing (NLP) and the social sciences foundations of interpersonal communication along with hands-on practical advice and practice. You will learn about the data science ecosystem, data science projects and how data scientists operate in organisations.

These foundational concepts will provide the essential building blocks for data science, equipping you with the skills and knowledge necessary to understand more advanced topics as the course progresses.

Compulsory modules

Core Mathematics – 40 credits

This module introduces you to the fundamental topics in mathematics. You’ll learn the foundational concepts of function, number and proof, equipping you with the language and skills to tackle your mathematical studies. The module also consolidates basic calculus, extending it to more advanced techniques, such as functions of several variables. These techniques lead to methods for solving ordinary differential equations. Linear algebra provides a basis for wide areas of mathematics and data science, and this module provides the essential foundation.

Probability and Statistics – 20 credits

‘Probability is basically common sense reduced to calculation; it makes us appreciate with exactitude what reasonable minds feel by a sort of instinct.’ So said Laplace. In the modern scientific and technological world, it is even more important to understand probabilistic and statistical arguments. This module will introduce you to key ideas in both areas, with probability forming the theoretical basis for statistical tests and inference.

Computational Mathematics and Modelling – 20 credits

You’ll be introduced to computational techniques, algorithms and numerical solutions, as well as the mathematics of discrete systems. You’ll learn basic programming using the language Python and apply computational techniques to the solution of mathematical problems.

Data Science and Communication – 20 credits

This module examines the nature of data science, the data science lifecycle and the strategic organisational role of a data scientist. You’ll be introduced to a high-level approach to analysis of data in different formats and this module also lays the foundation to work at the level of raw data, experimentation and reproducible programming pipelines. A key aspect is the interdisciplinary and integrated nature of the subject, in particular the social science dimension around communication, collaboration, design and ethics.

Modelling for Big Data – 20 credits

This module lays the foundations for computer programming for big data, with basics of procedural and functional programming, data structures and good programming practice such as reproducibility, collaborative editing and version control. You’ll encounter and practise techniques for handling big data such as parallelisation, vectorisation and high-performance computing, and for the design of efficient algorithms. These are applied to exploratory data analysis, computational modelling, problem solving, and data visualisation.

Year 2

Your second year will build on the first-year foundations, giving you more insight into applying data science in the ‘real world’. In mathematics, you’ll cover the basics of machine learning, such as clustering, principal component analysis, numerical methods, optimisation and neural networks. You'll also study networks and complex systems, analysing and visualising examples of real-world networks, as well as stochastic processes and statistical inference.

In computer science, you’ll learn more advanced topics such as object-oriented programming, distributed computing, software engineering, data mining and natural language processing. Furthermore, you’ll gain experience in the use of various tools and techniques for collecting, analysing and visualising data.

These topics will provide you with the essential building blocks for data science, equipping you with the understanding you’ll need to apply this discipline in industry.

Compulsory modules

Further Linear Algebra and Discrete Mathematics – 20 credits

Explore the more abstract ideas of vector spaces and linear transformations, together with introducing the area of discrete mathematics (such as graphs and networks), both of which are of fundamental importance to data science

Graphs, Networks and Systems – 20 credits

You’ll be introduced to technical real-world considerations such as the analysis of networks and complex systems. The module builds on the mathematical foundations of algebra and graph theory and applies these to the analysis of networks. It compares and contrasts reductionism & superposition with non-linearity & complexity such as the messiness of the real world. Building on Computational Mathematics and Modelling, the module combines ideas of networks with non-linear activation functions in order to model the dynamics of basic neural networks and uses libraries to perform standard neural network tasks such as predictive modelling and classification.

Machine Learning and Object-Oriented Programming – 20 credits

Develop your programming skills for applications to software engineering and machine learning. You'll encounter the object-oriented programming paradigm, as well as software engineering practices around design thinking, resource trade-offs and project management tools. Fundamental machine learning paradigms such as supervised, unsupervised and reinforcement learning are introduced and key topics in data science such as clustering, genetic algorithms, random forests and dimensionality reduction are also explored.

Statistical Methods – 20 credits

Statistical models are important in many applications. They contain two main elements: a set of parameters with information of scientific interest and an "error distribution" representing random variation. This module lays the foundations for the analysis of such models. We’ll use practical examples from a variety of statistical applications to illustrate the ideas.

Stochastic Processes – 10 credits

A stochastic process refers to any quantity which changes randomly in time. The capacity of a reservoir, an individual’s level of no claims discount and the size of a population are all examples from the real world. The linking model for all these examples is the Markov process. With appropriate modifications, the Markov process can be extended to model stochastic processes which change over continuous time, not just at regularly spaced time points. You’ll explore the key features of stochastic processes and develop your understanding in areas like state, space and time, the Poisson process and the Markov property.

Time Series – 10 credits

In time series, measurements are made at a succession of times, and it is the dependence between measurements taken at different times which is important. This module will concentrate on techniques for model identification, parameter estimation, diagnostic checking and forecasting within the autoregressive moving average family of models and their extensions.

Optional modules (selection below indicative of typical options)

You’ll choose a total of 20 credits of optional modules that’ll help you tailor your knowledge to topics that interest you the most. These could be from the selection of options below or you could choose to combine your options with discovery modules.

Discovery modules give you the chance to apply your data science thinking in real-world scenarios whilst expanding out into different areas, broadening your knowledge and giving you that competitive edge in the jobs market.

• Investigations in Mathematics – 10 credits
• Vector Calculus and Transforms – 20 credits

Year 3

Your third year will blend mathematics, computing and communication, using advanced concepts, tools and techniques to provide a deeper understanding of real-life data science.

You’ll learn how to extract data from data bases for data mining, big data visualisation and artificial intelligence, as well as the ethics and legal frameworks around data curation and governance.

A big component of the year will be a data science dissertation project alongside core data science modules and a range of optional modules in applied mathematics, pure mathematics, statistics and discovery. The dissertation project is your chance to apply everything you’ve learned to a real-world data science problem. Not only will you draw upon the technical knowledge learned throughout your degree, you’ll also use the advanced skills in areas like problem solving, strategic planning, analysis and visualisation to effectively tackle the challenges that arise in your project topic.

The exposure to an industry-relevant challenge is also a great first step in getting a taste for working in data science, building up industry experience before you graduate and demonstrating to employers your extensive skills in this field too.

Compulsory modules

Deep Learning and Explainable AI – 20 credits

This module builds on previous modules that cover Machine Learning and Neural Networks, and develops more advanced topics in areas like Artificial Intelligence and data mining, applied to key sectors such as finance, health, energy, sustainability etc. Key topics are Deep Learning and Explainable AI, for instance in the context of natural language processing, unstructured data labelling, object recognition or ethics. It reflects on the data science lifecycle in an organisational strategic context with a view to communicating insights and recommendations to institutional decision makers, achieving organisational goals, or effecting organisational change. The module thus aims to prepare you for applications of AI in a professional context in industry.

Data Curation and Governance – 20 credits

In this module, you will consider the practical aspects of working with real-world big data in a professional setting. It examines the need for data curation, quality and storage and explores appropriate tools such as databases and cloud technology. You’ll practise the important real-world task of data wrangling and data curation through cleaning, processing and transformation, and think about data from an organisational data modelling perspective. Professional settings also require an understanding of legal and ethical obligations surrounding e.g. privacy and data regulations/directives/compliance. This module therefore prepares you for the professional work environment in data science.

Project in Data Science – 40 credits

You’ll receive training in research skills, and you will develop and implement a personal training plan, conducting an independent research project in a topic in data science. You’ll meet in groups to discuss the project topic, with each team member researching a specific aspect of the topic, bringing back their findings to the group. You’ll produce an individual project report and give a group presentation of your work with each team member contributing.

Optional modules (selection below indicative of typical options)

You’ll choose a total of 40 credits of optional modules throughout semester 1 and 2 – 20 credits per semester. As with year 2, you may choose to combine your options with discovery modules and replace one 20-credit optional module with up to 20 credits of discovery modules in one semester.

Semester 1

You’ll choose 20 credits from the following options or choose discovery modules instead:

• Statistical Modelling – 20 credits
• Methods of Applied Mathematics – 20 credits
• Groups and Symmetry – 20 credits

Semester 2

You’ll choose 20 credits from the following options or choose discovery modules instead, if you haven’t chosen discovery modules in your first semester:

• Multivariate Analysis and Classification – 20 credits
• Graph Theory and Combinatorics – 20 credits
• Mathematical Biology – 20 credits
• Numbers and Codes – 20 credits
• Entropy and Quantum Mechanics – 20 credits

One-year optional work placement or study abroad

During your course, you’ll be given the opportunity to advance your skill set and experience further. You can apply to either undertake a one-year work placement or study abroad for a year, choosing from a selection of universities we’re in partnership with worldwide.

Learning and teaching

Data science is all about being able to apply skills from communication, mathematics and computer science in a real-world context. This course follows input and guidance from employers, data science practitioners, researchers, teachers, students and graduates, combined with cutting-edge pedagogical research such as in the Leeds SCALA approach.

• Student-centred: Supportive, inclusive, flexible, accessible and community-building curricula. 
• Active learning: Cognitively involved students, who are engaged with diverse content and media, and have opportunities to collaborate & participate. 

Your learning will be blended, with a mix of campus-based and online activities alongside study resources. There will be lectures, seminars and problem-solving classes – specifically on the mathematical side – in order to give you the necessary technical proficiency in the foundations for rigorous data science analyses.

There will also be interactive workshops (‘hackathon’-style) to collaborate with your cohort and teaching staff in a very active, hands-on way (problem-based learning). Workshops aim to integrate concepts and tools from lectures in an authentic and motivating context, encouraging you to apply those learned skills in an interdisciplinary context to applications – just like in the real-world.

This also creates an inclusive environment where diversity is strength, and where we can all contribute and learn together as a tight-knit community.

On this course, you’ll be taught by our expert academics, from lecturers through to professors. You may also be taught by industry professionals with years of experience, as well as trained postgraduate researchers, connecting you to some of the brightest minds on campus.

Assessment

Assessments will give you the opportunity to engage and learn in a variety of ways, which is more representative of what real work is like. In fact, many assessments may also be a chance to build up your own portfolio to help with revision or to showcase your proficiency in the relevant data science tools and techniques to potential employers when you apply for jobs after you graduate.

Whilst exams remain a useful tool for assessing key technical ability – especially for mathematical modules – the course will focus on the ability to apply techniques and tools from mathematics, computing and communication in the context of data science applications. As such, you could also be assessed through a variety of diverse ways throughout the course including project work, report writing, presentations, case studies, group collaborations, videos, blog posts and a portfolio.

Entry requirements

A-level: AAA/A*AB including a minimum of grade A in Mathematics

AAA/A*AB including a minimum of grade A in Mathematics, AAB/A*BB including a minimum of grade A in Mathematics plus Further Mathematics, or AAB/A*BB including a minimum of grade A in Mathematics, plus A in AS Further Mathematics.

Where an A-Level Science subject is taken, we require a pass in the practical science element, alongside the achievement of the A-Level at the stated grade.

Excludes A-Level General Studies or Critical Thinking.

GCSE: English Language at grade C (4) or above, or an appropriate English language qualification. We will accept Level 2 Functional Skills English in lieu of GCSE English.

Other course specific tests:

Extended Project Qualification (EPQ), International Project Qualification (IPQ) and Welsh Baccalaureate Advanced Skills Challenge Certificate (ASCC): We recognise the value of these qualifications and the effort and enthusiasm that applicants put into them, and where an applicant offers the EPQ, IPQ or ASCC we may make an offer of AAB/A*BB including a minimum of grade A in Mathematics, plus A in EPQ/IPQ/Welsh Bacc ASCC.

Alternative qualification

Read more about UK and Republic of Ireland accepted qualifications or contact the School’s Undergraduate Admissions Team.

Alternative entry

We’re committed to identifying the best possible applicants, regardless of personal circumstances or background.

Access to Leeds is a contextual admissions scheme which accepts applications from individuals who might be from low income households, in the first generation of their immediate family to apply to higher education, or have had their studies disrupted.

Find out more about Access to Leeds and contextual admissions.

Typical Access to Leeds offer: ABB including A in Mathematics and pass Access to Leeds OR A in Mathematics, B in Further Mathematics and C in a 3rd subject and pass Access to Leeds

Foundation years

If you do not have the formal qualifications for immediate entry to one of our degrees, you may be able to progress through a foundation year.

We offer a Studies in Science with Foundation Year BSc for students without science and mathematics qualifications.

You could also study our Interdisciplinary Science with Foundation Year BSc which is for applicants whose background is less represented at university.

On successful completion of your foundation year, you will be able to progress onto your chosen course.

International Foundation Year

International students who do not meet the academic requirements for undergraduate study may be able to study the University of Leeds International Foundation Year. This gives you the opportunity to study on campus, be taught by University of Leeds academics and progress onto a wide range of Leeds undergraduate courses. Find out more about International Foundation Year programmes.

English language requirements

IELTS 6.0 overall, with no less than 5.5 in any one component, or IELTS 6.5 overall, with no less than 6.0 in any one component, depending on other qualifications present. For other English qualifications, read English language equivalent qualifications.

Improve your English
If you're an international student and you don't meet the English language requirements for this programme, you may be able to study our undergraduate pre-sessional English course, to help improve your English language level.

Fees

UK: To be confirmed

International: £29,000 (per year)

Tuition fees for UK undergraduate students starting in 2024/25
Tuition fees for UK full-time undergraduate students are set by the UK Government and will be £9,250 for students starting in 2024/25.

The fee may increase in future years of your course in line with inflation only, as a consequence of future changes in Government legislation and as permitted by law.

Tuition fees for UK undergraduate students starting in 2025/26
Tuition fees for UK full-time undergraduate students starting in 2025/26 have not yet been confirmed by the UK government. When the fee is available we will update individual course pages.

Tuition fees for international undergraduate students starting in 2024/25 and 2025/26
Tuition fees for international students for 2024/25 are available on individual course pages. Fees for students starting in 2025/26 will be available from September 2024.

Tuition fees for a study abroad or work placement year
If you take a study abroad or work placement year, you’ll pay a reduced tuition fee during this period. For more information, see Study abroad and work placement tuition fees and loans.

Read more about paying fees and charges.

There may be additional costs related to your course or programme of study, or related to being a student at the University of Leeds. Read more on our living costs and budgeting page.

Scholarships and financial support

If you have the talent and drive, we want you to be able to study with us, whatever your financial circumstances. There is help for students in the form of loans and non-repayable grants from the University and from the government. Find out more in our Undergraduate funding overview.

Applying

Apply to this course and check the deadline for applications through the UCAS website.

We may consider applications submitted after the deadline. Availability of courses in UCAS Extra will be detailed on UCAS at the appropriate stage in the cycle.

Admissions guidance

Read our admissions guidance about applying and writing your personal statement.

What happens after you’ve applied

You can keep up to date with the progress of your application through UCAS.

UCAS will notify you when we make a decision on your application. If you receive an offer, you can inform us of your decision to accept or decline your place through UCAS.

How long will it take to receive a decision

We typically receive a high number of applications to our courses. For applications submitted by the January UCAS deadline, UCAS asks universities to make decisions by mid-May at the latest.

Offer holder events

If you receive an offer from us, you’ll be invited to an offer holder event. This event is more in-depth than an open day. It gives you the chance to learn more about your course and get your questions answered by academic staff and students. Plus, you can explore our campus, facilities and accommodation.

International applicants

International students apply through UCAS in the same way as UK students.

We recommend that international students apply as early as possible to ensure that they have time to apply for their visa.

Read about visas, immigration and other information here.

If you’re unsure about the application process, contact the admissions team for help.

Admissions policy

University of Leeds Admissions Policy 2025

Contact us

School of Mathematics Undergraduate Admissions

Email: maths.admiss@leeds.ac.uk
Telephone:

Career opportunities

Data science has become indispensable to so many industries worldwide – meaning you’ll be in high demand with a wealth of job prospects open to you when you graduate.

Leeds, in particular, is a major hub for data scientists – with a strong presence of high-profile data science start-ups and large tech companies, as well as many key players in the fintech sector, such as banks, consultancies and accountancy firms too.

As a University of Leeds student, you’ll benefit from our strong industry links, too, which’ll give you the platform to network with potential employers and gain valuable work experience before you graduate.

Plus, University of Leeds students are among the top 5 most targeted by top employers according to The Graduate Market 2024, High Fliers Research.

With the combination of specialist and transferable skills you’ll learn on this BSc, you could go into areas like data analysis, machine learning engineering or data science in strategy or in communication across a range of fields and industries like:

• technology
• finance
• healthcare
• charities
• government

Careers support

At Leeds, we help you to prepare for your future from day one. Our Leeds for Life initiative is designed to help you develop and demonstrate the skills and experience you need for when you graduate. We will help you to access opportunities across the University and record your key achievements so you are able to articulate them clearly and confidently.

You will be supported throughout your studies by our dedicated Employability Team, who will provide you with specialist support and advice to help you find relevant work experience, internships and industrial placements, as well as graduate positions. You’ll benefit from timetabled employability sessions, support during internships and placements, and presentations and workshops delivered by employers.

Explore more about your employability opportunities at the University of Leeds.

You will also have full access to the University’s Careers Centre, which is one of the largest in the country.

Study abroad and work placements

Study abroad

Studying abroad is a unique opportunity to explore the world, whilst gaining invaluable skills and experience that could enhance your future employability and career prospects too.

From Europe to Asia, the USA to Australasia, we have many University partners worldwide you can apply to, spanning across some of the most popular destinations for students.

This programme offers you the option to spend time abroad as an extra academic year and will extend your studies by 12 months.

Once you’ve successfully completed your year abroad, you’ll be awarded the ‘international’ variant in your degree title which demonstrates your added experience to future employers.

Find out more at the Study Abroad website.

Work placements

A placement year is a great way to help you decide on a career path when you graduate. You’ll develop your skills and gain a real insight into working life in a particular company or sector. It will also help you to stand out in a competitive graduate jobs market and improve your chances of securing the career you want.

Benefits of a work placement year:

• 100+ organisations to choose from, both in the UK and overseas
• Build industry contacts within your chosen field
• Our close industry links mean you’ll be in direct contact with potential employers
• Advance your experience and skills by putting the course teachings into practice
• Gain invaluable insight into working as a professional in this industry
• Improve your employability

If you decide to undertake a placement year, this will extend your period of study by 12 months and, on successful completion, you will be awarded the ‘industrial’ variant in your degree title to demonstrate your added experience to future employers.

With the help and support of our dedicated Employability Team, you can find the right placement to suit you and your future career goals.

Find out more about Industrial placements.