• Undergraduate

Data Science BSc (Hons)

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Overview

Overview

Why study at the University of West London? 
  • Ranked 30th university in the UK - The Guardian University Guide 2025
  • Number 1 London university for overall student satisfaction - National Student Survey 2024**
  • Best university for Student Experience and Teaching Quality in the UK - The Times and Sunday Times Good University Guide 2024

As a graduate of a BSc Data Science degree, you will offer your employers outstanding skills in 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.

male student in computer lab

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.
With Foundation Year
Duration: 4 years

Start date:

If your desired start date is not available, try selecting a different study option.

Why study Data Science with us?

Why study Data Science with us?

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What our students say…

The lecturers are fantastic and I don't think I would get as many work experience opportunities at any other university.

Harry Poulter
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Industry focused teaching
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Course detail & modules

Course detail & modules

The course 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 to 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.
 

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

    The 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.

Compulsory modules

  • 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'll 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

    You will gain 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.

  • 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 AI

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

  1. Ordinary and Partial Differential Equations OR Robotics and AI 
  2. Operational Research and Optimisation OR Natural Language Processing

Compulsory modules

  • Machine Learning

    Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.

    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. Several software libraries and data sets publicly available will be used to illustrate the application of these algorithms. The emphasis will be thus 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. 

  • Project

    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

Entry requirements

Entry requirements

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.

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.
6.0 IELTS or above

You need to meet our English language requirement - a minimum of IELTS 5.5 for each of the 4 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.

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.

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.
Fees & funding

Fees & funding

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Please note:

  • Fees for the 2026/27 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 for two years or more, you will need to pay the fee for each academic year at the start of that year.
  • If your course runs for less than two years, the cost above is for your full course and you will need to pay the full fee upfront.
  • 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 dependants and disabled students.

We offer generous bursaries and scholarships to make sure your aspirations are your only limit. In recent years, hundreds of students have received our Full-time Undergraduate Student Bursary. 

View full details, including conditions and eligibility.

Funding your studies
{{ formatCurrencyValue(currentVariantData.field_p_cv_int_main_fee.name) }} per year

Please note:

  • Fees for the 2026/27 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 for two years or more, you will need to pay the fee for each academic year at the start of that year.
  • If your course runs for less than two years, the cost above is for your full course and you will need to pay the full fee upfront.
  • If no fee is shown above then the fees for this course are not available yet. Please check again later for updates.

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
Teaching staff

Teaching staff

Thomas Madsen is smiling in front of a white background

Dr Thomas Madsen

Before joining the University of West London, Dr Madsen was a Lecturer at University of Buckingham. Before that he held academic positions at a number of international institutions, including at 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.

Before joining the University of West London, Dr Madsen was a Lecturer at University of Buckingham. Before that he held academic positions at a number of international institutions, including at 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.

Study & career progression

Study & career progression

Student working on a computer, looking at a large monitor screen.

The demand for data science professionals is projected to grow significantly, a degree in data science can open several career paths, including:

  • Junior Data Analyst
  • Junior Data Engineer
  • Data Scientist
  • Machine Learning Engineer
  • Business Analyst
  • Data Engineer
  • Database Administrator
  • Quantitative Analyst
  • Research Scientist

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

How to apply

How to apply

Search for courses

Important notes for applicants

Disclaimer

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

**The National Student Survey 2023 and 2024 - Average of answers to all questions by registered student population. Excludes specialist institutions.

Testimonials - our students or former students provided all of our testimonials - often a student from the course but sometimes another student. For example, the testimonial often comes from another UWL student when 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. In some cases a new Course Leader has joined the University since the video was filmed.

Availability of placements - if you choose a course with placement/internship route we would like to advise you that if 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, this is to ensure you are still successful in being awarded a degree.