Data Science

取得日期：2026-10-29

https://www.uwl.ac.uk/course/undergraduate/data-science?start=1898&option=33

https://www.uwl.ac.uk/international/your-country/international-entry-requirements/hong-kong

BSc (Hons) Data Science | University of West London

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Undergraduate

BSc (Hons) Data Science

Full-time

September 2027

Fees per year†

£17,250 – International

£10,050 – UK

Duration

Location

Overview

Course details

Our students

Entry and fees

Facilities

Teaching staff

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How to apply

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Start date

As a graduate of our BSc (Hons) Data Science degree, you will offer your employers outstanding skills in computer modelling, statistical analysis and software development.

The course offers a balance between theory and applications, allowing you to both develop a deep understanding of mathematical and statistical tools whilst, being able to translate these into concrete computer-based solutions.

Upon successful completion of this degree, you will be well-equipped to enter a career in data analysis, modelling, finance/insurance, or in the computing industry.

You may also choose to further specialise by undertaking postgraduate studies in subjects such as computer science, cyber security, data science or artificial intelligence.

Why study this course?

Our Computer Science courses are ranked 3rd in London - National Student Survey 2026*

2nd London university for overall student satisfaction - National Student Survey 2026*

Top 20 in the UK for teaching quality - The Times and Sunday Times Good University Guide 2027

Ranked top 50 in the UK and 2nd modern** London university - The Guardian University Guide 2027

Select your desired study option, then pick a start date to

see relevant course information:

Study options:

We support flexible study by offering some of our courses part-time or via distance learning. To give you real world experience before you graduate, we also offer some courses with a placement or internship. All available options are listed here. Your choices may affect some details of your course, such as the duration and cost per year. Please re-check the details on this page if you change your selection.

Duration: 3 years

With Foundation Year

Duration: 4 years

Start date:

If your desired start date is not available,

try selecting a different study option.

What you'll study

Our BSc (Hons) Data Science degree introduces you to mathematical, statistical and computing techniques that form the basis of many tools used in industry, engineering, business and education, using information and data as an integral part of decision-making and teamwork.

This Data Science course aims to produce graduates who:

meet employer requirements for well-trained, competent and adaptable professionals with expert knowledge in data-driven decision-making

are highly numerate and able to use this for applications for modelling purposes

are independent learners, able to acquire new skills and continue to build their knowledge, to adapt to a rapidly changing job market

can undertake postgraduate studies in several disciplines related to data science, computing and artificial intelligence

are strong communicators and have strong interpersonal and team working skills

have knowledge of ethical issues, particularly the need for sensitivity in data handling and decision-making.

Throughout your studies, you can expect a supportive learning environment that will:

develop your interpersonal skills

raise your self-awareness

encourage personal and career growth

stimulate the idea of lifelong learning.

You can expect regular contact with employers and career advisers as an integral part of taught modules and extracurricular activities. We offer an inclusive and diverse learning environment that makes use of a variety of teaching and learning methods, and encourages teamwork.

Please note, we regularly review our course content to offer relevant, impactful and career-shaping learning. Therefore, the following modules are indicative and subject to change.

Compulsory modules

Probability and Statistics

This module is an introduction to probability theory and statistical methods. The module leads to a deeper understanding of probability distributions, random variables and their role in sampling. Tools such as hypothesis testing are presented and a basic introduction to the statistical software SPSS is provided.

Linear Algebra

The aim of this module is to extend your knowledge of matrices, vectors and systems of linear equations and to introduce the abstract concepts of vector spaces, linear maps and inner products.

Programming

This module provides a thorough grounding in the fundamentals of Java programming language and object programming concepts. It will focus on the design and build of Java desktop applications using the Java Development Kit and popular Integrated Development Environments, following established industry standard methodologies. The module will have a strong emphasis on using OO modelling techniques to interpret and implement business related applications.

Calculus 1

This module introduces you to the most important techniques in Calculus. In particular, the module leads to a deeper understanding of the concepts of differentiation and integration. Tools and techniques for differentiation and integration will be presented in detail.

Data Science and Visualisation

Algorithms and Data Types

This module will help you to gain the knowledge and competence to deal with basic data structures and algorithms. You will learn how to specify collections using abstract data types (ADTs) and to implement them using a variety of techniques such as linked lists and trees. You'll also use a range of algorithms, including searching and sorting.

Multi-variate Calculus

Artificial Intelligence

In this module, you will gain insights into key techniques within the field of artificial intelligence (AI). Aspects of AI you will cover include agents, environments and learning as well as techniques such as regression, classification, clustering, reinforcement learning, learning recommendation and decision support systems.

Theory of Computation

The aim of this module is to provide you with the knowledge and understanding of fundamental concepts of computational theory and computational complexity. You will learn how to examine whether a given problem can be solved computationally. The module aims to introduce computation science, modelling complex dynamic systems and diagramming models.

Numerical Methods

This module aims to introduce you to the numerical techniques required to solve different mathematical problems motivated by the engineering and the science sector.

Statistical Modelling

This module aims to develop understanding and proficiency in statistical modelling by introducing you to the normal theory linear model. It will provide you with the ability to formulate and apply these models in a range of practical settings, to carry out associated inference appreciating how this relates to the general likelihood inferential framework, and to perform appropriate model selection and model checking procedures.

Applications of Artificial Intelligence

This module will provide you with a deep understanding of artificial intelligence (AI) concepts, tools, techniques and their applications in solving real-world problems. You will develop practical skills in designing, implementing and evaluating AI-based solutions, gaining insights into both theoretical foundations and practical applications relevant to AI and data science. The module will cover extensive examples of applied AI and relate the covered topics to other modules within the programme providing a practical context from industry and day-to-day use of AI.

In addition to the three compulsory modules, you will study two modules (20 credits each) from the following options:

Ordinary and Partial Differential Equations OR Robotics and AI

Operational Research and Optimisation OR Natural Language Processing.

Machine Learning

Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience. This module familiarises you with some basic machine learning algorithms and techniques and their applications, as well as general questions related to analysing and handling large data sets. The emphasis will be on machine learning algorithms and applications, with some broad explanation of the underlying principles.

Databases and Analytics

There has been an explosion in data, much of which is not fully structured, but contains valuable information such as search trends, consumer behaviour and other patterns. This module aims to cover some of the developments in the broad range of "Big Data" problems. It will give you a good understanding of data structures, software development procedures and the range of analytical tool used to undertake a wide range of standard and custom analyses to provide data solutions to these issues.

Under the guidance of an academic supervisor, you will investigate in depth a mathematical or statistical topic with has close links to computing. You will be able to choose a project that may require the solution to a specific problem, the creation of an artefact in a real-world environment or an investigation of innovative ideas and techniques related to an area within their field of study.

You will compose a written report and provide an oral presentation on your work.

Optional modules

Ordinary and Partial Differential Equations

This module aims to study both the qualitative and quantitative aspects of ordinary and linear partial differential equations.

Robotics and AI

Operational Research and Optimisation

The module aims to introduce you to linear programming, the Simplex Method and the Transportation Algorithm. The module will enable you to solve linear programming problems as primal problems or using duality. The module includes an introductory theory for nonlinear programming problems by demonstrating the application of Lagrange Multiplier Theory as well as tackling optimisation problems.

Natural Language Processing

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Entry requirements

Requirements:

International

120 UCAS points required

from level 3 qualifications

These can include:

A Levels at grade B, B and B, or above

BTEC Extended Diploma with Distinction, Distinction, Merit

Access to HE Diploma

T Levels

Your Level 3 qualifications must include Mathematics or Statistics.

You also need GCSE English and Maths (grade 9-4 / A*-C) or Level 2 equivalents.

Looking for BSc (Hons) Data Science with Foundation Year?

View Foundation Year course

Whether you are changing career or don't have the exact subjects and grades required for this course, you might want to choose this course with a foundation year. This will give you an extra year's study to prepare you for the standard degree programme, where you can go on to graduate with a full Honours degree. Follow the link to see full details of the course with foundation year.

Mature applicants (aged 21+): If you do not hold the qualifications listed but have relevant work experience, you are welcome to apply. Your application will be considered on an individual basis.

Level 5 (year 2) entry

To directly enter the second year of this course, you will need to show appropriate knowledge and experience. For example, you are an ideal candidate if you have 120 undergraduate credits at Level 4 or a CertHE in a related subject area.

Level 6 (year 3) entry

To directly enter the third year of this course, you need to show appropriate knowledge and experience. For example, you are an ideal candidate if you have 240 undergraduate credits (at Levels 4 and 5), a DipHE, foundation degree or HND in a related subject area.

6.0 IELTS or above

You need to meet our English language requirement – a minimum of IELTS 5.5 for each of the four individual components (Reading, Writing, Speaking and Listening). Visit our English language requirements page for information on other English language tests we accept.

You also need academic qualifications at the same level as UK applicants. In some countries where teaching is in English, we may accept local qualifications. Check for local equivalents.

We offer pre-sessional English language courses if you do not meet these requirements.

Find out more about our English Language courses.

Fees & funding

Funding:

per year†

Please note:

Fees for the 2027/28 academic year and onwards may be subject to Government regulation and change.

Tuition fees are charged for each year of your course. If your course runs over 2 academic years or more, you will need to pay the fee for each academic year at the start of that year, except if otherwise stated.

If no fee is shown above then the fees for this course are not available yet. Please check again later for updates.

Funding your studies

You may be eligible for a student loan to cover the cost of tuition fees or a maintenance loan. Additional funding is available to some types of students, such as those with dependents and disabled students.

We offer generous bursaries and scholarships to make sure your aspirations are your only limit.

View full details, including conditions and eligibility.

International students - funding your studies

We offer scholarships for international students including International Ambassador Scholarships.

Further information about funding and financial support for international students is available from the UK Council for International Student Affairs.

Financial support for international students

Computer Science facilities

Explore Computer Science facilities available to our students, from the Artificial Intelligence Lab to the Robotics Lab to our new Digital Futures Lab for gaming and VR.

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SEGA Lab

The lab provides access to GPU-accelerated workstations that support AI development, simulation, data science, and cyber security experimentation. You will have the opportunity to work with systems that reflect current industry practice.

Computing labs

Our computing labs offer numerous computers, most of which are equipped with wide-screen monitors. The latest computer room offers 27-inch screens.

UWL Libraries will help you to succeed as you progress through your studies. Find out more information about our libraries.

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Your future career

The demand for data science professionals is projected to grow significantly, and a Data Science degree can open several career paths, from a business analyst to a research scientist.

You may also choose to further specialise your skills by undertaking a masters course. View our computer science-related postgraduate courses.

One-to-one, life-long careers support

Taught by lecturers with industry experience

Career-focused modules

Support with CV and interview skills

Alumni networking events

On campus, specialist careers fairs

Work on real-world industry projects

Contacts with major technology companies

Job roles

junior data analyst

junior data engineer

data scientist

machine learning engineer

business analyst

data engineer

database administrator

quantitative analyst

research scientist.

Dr Thomas Madsen Course Leader

Dr Madsen is a mathematician and Lead for Teaching and Quality at the School of Computing and Engineering. Before joining UWL in 2020, he held academic positions at a number of international institutions, including Aarhus University (Denmark), Centro di Ricerca Matematica Ennio De Giorgi (Pisa) and King’s College London.

Most of Dr Madsen’s research and publications are in the area of differential geometry. He particularly enjoys analysing and finding explicit solutions to partial differential equations that arise in a geometric context (e.g. Einstein’s equations), using symmetry techniques.

Dr Madsen is a member of the London Mathematical Society Computer Science and the Early Career Research Committees.

View more

View full profile

Dr Eman Alajrami

Dr Sama Aleshaiker

Dr Nasim Dadashi Serej

Dr Fateme Dinmohammadi

Show more

How to apply:

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Head to the UCAS website where you can apply using:

our institution code - W05

the UCAS course code (below)

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You can apply to us in two ways:

on the UCAS website you will need our institution code (W05) and the UCAS course code (see top of page)

directly on our website – follow the ‘Apply now’ link

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Important notes for applicants

Disclaimer

†Fees - the published fee is an annual fee, payable each year, for the duration of the course. Fees are subject to change. Home tuition fees may be subject to annual increases but will not increase by more than the fee caps as prescribed by the Office for Students or such other replacing body. Full-time taught international fees are subject to an annual increase.

Teaching - your course will primarily be delivered in-person, however it may include delivery of some activities online, either in real-time or recorded.

Testimonials - quotes are provided by current or former UWL students. These are mostly from the course but quotes from a UWL student on another course may be used if, for example, the course is new.

Optional modules - where optional modules are offered they will run subject to staff availability and viable student numbers opting to take the module.

Videos - all videos on our course pages were accurate at the time of filming, however staff and course content may have since changed.

Availability of placements - if you choose a course with placement/internship route and a placement/internship opportunity does not arise when you are expected to undertake the placement then the University will automatically transfer you to the non-internship route, to ensure you are still successful in being awarded a degree.

Rankings

*The National Student Survey 2026 result is based on the average of all answers by the taught student populations.

**Modern universities - defined as higher education institutions that were granted university status in, and after, 1992.

***PRES - rank out of 53 institutions.

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