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Data Science (Distance Learning) BSc(Hons)

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Start Dates

14 September 2026

Duration

3 years full-time 6 years part-time

UCAS Tariff

104 points

Recent Awards For Excellence

Computer Science & Information Systems - QS 2025
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About this course

Overview

Why choose Huddersfield for this course?

  • Develop an analytical, technical and professional skillset and learn industry standard tools like Python and R
  • If you don’t have the required academic qualifications, demonstrate your suitability by applying via our PBA route.
  • Study 100% online, from anywhere, at any time.

You’ll have the opportunity to graduate as a highly competent, ethically-aware data scientist, equipped with the comprehensive analytical, technical and professional skillset that data-driven organisations require.

This course is delivered 100% online and is designed for both contemporary learners and working professionals with a need for flexibility. As a University committed to widening participation, we welcome students from all backgrounds. This means that if you don’t have the required academic qualifications, you can demonstrate your suitability by applying via our Performance Based Admissions (PBA) route.

Why study Data Science (Distance Learning) BSc(Hons) at Huddersfield?

This course provides a strong foundation in data science combining key principles from mathematics, statistics, computing and artificial intelligence. You can gain skills in advanced areas such as big data analytics, Natural Language Processing (NLP) and deep learning using industry-standard tools like Python and R.

Furthermore, you’ll develop problem solving skills, programming expertise, statistical reasoning and data-driven decision making, within a supportive and collaborative learning environment, preparing you for an exciting career.

Why choose Distance Learning at Huddersfield?

This course is delivered 100% online, so you can study from anywhere in the world, at anytime. As a distance learning student, you’ll be taught by the same highly qualified academic team who teach on-campus. None of our teaching is outsourced to third parties, ensuring you receive the same standards of teaching excellence that Huddersfield is known for.

Performance Based Admissions (PBA) route

If your academic background doesn’t match our entry requirements, you can apply via our Performance Based Admissions* (PBA) route.

How does it work?

You’ll be required to take two or three short courses, totalling 60 credits. Successful completion of the courses, at the first attempt, will permit entry to the full Data Science BSc(Hons) at the next available intake. (See entry requirements for full details)

The credits from the short courses will be considered as accreditation of prior learning (APL) and will count towards your degree.

How do I apply?

Simply go to our application portal and select the course that’s right for you:

  • BSc(Hons) Data Science (Distance Learning)
  • Data Science (Distance Learning) PBA

You’ll have the option to select full-time or part-time study and your preferred start month. There is no application fee and you can save and restart your application at any time.

*Please note UK students are not eligible for Student Finance England funding for PBA tuition fees. Upon progression to Data Science BSc(Hons), you can apply for tuition fee funding (subject to eligibility).

Who can apply?

Entry Requirements

104 UCAS tariff points from a combination of Level 3 qualifications, with one of the qualifications in a STEM subject (Mathematics, Engineering, Computer Sciences, Physics, Chemistry or Biology).
BCC at A Level. We require one of those qualifications to be in STEM subject (Mathematics, Engineering, Computer Science, Physics, Chemistry or Biology).
MMM in BTEC Level 3 National Extended Diploma in either Applied Sciences, Computing or Engineering.
Merit at T Level in either Science or Engineering.
104 UCAS tariff points from International Baccalaureate qualifications. Must include one STEM subject (Mathematics, Engineering, Computer Science, Physics, Chemistry or Biology).

Successful applicants for this programme are accepted from a diverse range of professional and academic backgrounds - previous experience and qualifications in IT are not required. There is no need for prior coding experience - all skill levels are accommodated.

If your first language is not English, you will need to meet the minimum requirements of an English Language qualification. The minimum for IELTS is 6.0 overall with no element lower than 5.5, or equivalent or Duolingo English certificate, score 105 or above. An updated experience statement to also include ‘work’: Where you have studied in an English-speaking country, worked for an English-speaking company, or have a UK degree - this proof is accepted within two years of passing as meeting the English Language requirements. Read more about the University’s entry requirements for students outside of the UK on our International Entry Requirements page.

There are two routes for entry onto this course: Direct and Performance Based Admission (PBA) route.

Performance Based Admission (PBA) Route(s):

This distance learning course offers a PBA admissions entry route, based on the successful completion of two or three short courses, as detailed below.

The minimum criteria for admission to the short courses are GCSE English Language or Literature at grade 4 or above and Maths at grade 5 or above, or grade C and B respectively if awarded under the previous GCSE grading scheme; or have 3 years work experience in a professional role with significant data/numerical requirements.

Full time students:

September start - you will be required to take two short courses: Calculus (20 credits) and Programming for Data Science (40 credits). Successful completion of both short courses at the first attempt will permit entry onto the full BSc programme in the following January.

January start - you will be required to take three short courses: Computing Science and Mathematics (20 credits), Linear Algebra (20 credits) and Probability Theory and Statistical Analysis (20 credits). Successful completion of all three short courses at the first attempt will permit entry onto the full BSc programme in the following September.

Part-time students:

September start - you will be required to take two short courses: Programming for Data Science (40 credits) followed by Linear Algebra (20 credits). Successful completion of both short courses at the first attempt will permit entry onto the full BSc programme in the following September.

January start - you will be required to take two short courses: Linear Algebra (20 credits) followed by Programming for Data Science (40 credits). Successful completion of both short courses at the first attempt will permit entry onto the full BSc programme in the following January.

Once the credits for the short courses have been confirmed, you will be invited to apply for the full course at the next available entry point. The PBA modules will then be considered as accreditation of prior learning (APL) as part of the full BSc(Hons) application, and therefore the credits you have earned in your short courses will count towards your degree.

What will you learn?

Course Details

In this module, you will dive into the essential concepts of differential and integral calculus - the basis of many advanced analytical techniques. Learn how to calculate derivatives and integrals, and explore applications of integration to real-world problems. You’ll also discover how to solve first and second order differential equations with constant coefficients - crucial for modelling dynamic systems and understanding patterns in data.

This module provides you with a comprehensive introduction to the mathematical foundations of probability and statistical methodology, essential for understanding and analysing data in a range of real-world contexts. You will learn how to apply appropriate statistical techniques to solve problems, interpret results and draw meaningful conclusions. Core topics include probability spaces, conditional probabilities, Bayes theorem, discrete and continuous random variables, statistical distributions, independence, density and mass functions, variance, standard deviation and expectation. You’ll also gain experience in statistical sampling and sampling distributions and explore key sampling distributions, including chi squared and t-tests. By combining theoretical understanding with practical application, this module equips you with the critical skills needed for statistical modelling and data interpretation—an essential toolkit for modern data science.

This module introduces fundamental concepts in linear algebra – a key area of mathematics for understanding data structures, algorithms and computational methods. This module will provide you with a thorough grounding in matrix theory, including properties of matrices (determinant, rank, inverse etc.) and their use in solving systems of linear equations. Topics include existence, ill-conditioning, linear dependence, orthogonality, QR factorisation, Cholesky factorisation, LU factorisation and other solution methods. You will be introduced to the concepts of eigenvalues and eigenvectors, along with methods for determining eigen-solutions using both deterministic and numerical methods.

In this module, we introduce you to basic computing science and mathematical concepts related to software development. You will explore topics such as set theory, graphs and trees, finite state machines, grammars and languages, propositional logic and searching and sorting algorithms. You’ll put the theory into practice, gaining hands-on experience using a programming language and software that lets you directly implement finite state machines.

This module aims to introduce you to fundamental programming concepts, using a programming language that is widely adopted for data analytic workflows. In the first part of this module, you will gain knowledge in core programming elements and constructs, followed by an introduction to programming packages and modules that are designed for basic data processing and analytic tasks.

This module is designed to provide you with the opportunity to explore large data sets and big data analytical systems in depth. You’ll gain experience working with big data technologies and develop the skills needed to analyse and interpret large, complex datasets commonly found in real-world professional settings.

This module provides you with the opportunity to investigate multivariate systems in depth, essential for understanding complex, real-world data. You’ll develop the skills and experience to formulate and apply statistical models and execute techniques associated with multivariate analysis on data sets, preparing you to tackle data challenges commonly faced in professional environments.

What does it mean for a machine to be intelligent, and how close are we to achieving it? In this module, you will delve into the intriguing question of whether machines, particularly computers, can truly be intelligent—and if so, what does that means in practical terms? In the first half, you’ll focus on environments where everything is predictable, like laboratory conditions or puzzle-solving scenarios exploring how machines represent knowledge, solve problems, and plan actions under these ideal settings. The second half introduces uncertainty, teaching you how machines adapt to opponents, incomplete information, and unpredictable situations. You’ll also discover how intelligent agents learn and make decisions. By the end, you'll gain a deeper understanding of how machines perform tasks that seem intelligent across different contexts.

Building on your foundational knowledge of statistics, this module introduces a range of advanced statistical techniques, which can be used to help solve real-life problems involving the analysis of data and interpretation of results. You will explore sampling theory and estimation of mean and variance, hypothesis testing, significance testing, measures of independence and goodness of fit. You will also study linear, multiple and stepwise regression, correlation and analysis of variance tests. Throughout the module, you’ll apply these techniques to practical datasets, using powerful tools such as R and Minitab to carry out analysis and interpret results.

n this module, we’ll support you in enhancing your programming skills to cover a range of standard data structures (such as lists, trees and graphs) and algorithms (including searching, sorting and traversals) for both sequential and concurrent systems. You will also study how to analyse systems to determine their complexity, correctness and safety, and to calculate their efficiency.

In this module, you will explore how recent advances in information technology have paved the way for data-driven Artificial Intelligence (AI). You’ll gain a solid understanding of cutting-edge machine learning techniques used to build intelligent systems that can recognise, classify and make decisions. You’ll examine widely used approaches like deep learning, and investigate their typical applications as well as their limitations. Along the way, you’ll learn how to select the right techniques for different learning problems, weighing their benefits and drawbacks. Through hands-on experience with industry-standard tools, you’ll dive into exciting real-world applications, including medical imaging and natural language processing, giving you the practical skills to apply machine learning in a variety of contexts

Recent advances in information technology have enabled the extensive collection of textual data across various domains, driving significant developments in text analytics and natural language processing (NLP) approaches. During this module, you’ll gain a fundamental understanding of these approaches, particularly advanced techniques for processing and analysing textual data to extract meaningful insights. We will explore several widely known methods, including text classification, clustering, and sentiment analysis, and investigate typical applications and potential limitations. We will guide you in selecting the right techniques for different text analysis challenges, weighing the strengths and weaknesses of each approach. You will have the opportunity to apply this knowledge using industry-standard tools and delve into high-profile applications such as conversational agents and information retrieval systems

The exponential growth of digital data is transforming how organisations manage and utilise information. In this module, you will explore why traditional databases are no longer sufficient to meet modern demands and discover alternative data modelling techniques. You’ll dive into approaches including hierarchical, network, object-oriented and object-relational models, gaining a clear understanding of their applications and benefits. By the end of the module, you’ll be equipped with the knowledge to tackle complex data challenges in an increasingly data-driven world.

In this module, you will explore how data science has revolutionised the way organisations harness data to achieve business success. You’ll examine how data analytics can shape business strategies, uncover opportunities for innovation, and deliver a competitive edge. The module highlights the importance of planning, executing and managing data science projects using agile methods and project management tools. You’ll also develop the skills to lead and collaborate with cross-functional teams, ensuring clear communication between data scientists and stakeholders. By applying analytical skills, you’ll learn to interpret data insights and make strategic management decisions to boost organisational performance.

This module offers you the opportunity to study and investigate a specific mathematical topic in depth, providing you a hands-on experience in carrying out a mathematical project, mirroring the process that would take place in a professional environment. You will be allocated a project supervisor who will direct you through the process of developing project objectives, project planning, performing background research, and carrying out the project work to a satisfactory conclusion. The project will focus on a data science challenge relevant to your chosen career pathway. The project work aims to extend your knowledge and capabilities in the specific field associated with the project, and allow you to demonstrate your initiative, commitment, and mathematical capability to a professional level.

Teaching and Assessment

Discover what to expect from your tutor contact time, assessment methods, and feedback process.

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Global Professional Award

At Huddersfield, you’ll study the award-winning Global Professional Award (GPA) alongside your degree* — so you’re ready for the career you want, whatever subject you choose.

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Technology and System Requirements

As a Distance Learning student, you must provide and have access to certain IT equipment and facilities to access your Virtual Learning Environment (VLE) and to fully participate on your course.

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Where could this lead you?

Your Career

The top five job titles advertised in the UK for graduate roles associated with courses in this subject area are: Programmers and Software Development Professionals, IT Managers, IT Business Analysts, Architects and Systems Designers, Information Technology Professionals n.e.c. and IT User Support Technicians.

Source: LightcastTM data - job postings from May 2024 to April 2025 showing jobs advertised associated with a selection of relevant graduate roles.

£55k
The median advertised salary for a Data Scientist in the UK

* Lightcast: based on job postings May 2024 - April 2025

94%
94% of our undergraduate students go on to work and/or further study within fifteen months of graduating.

* HESA Graduate Outcomes 2022/23, UK domiciled. Graduates not reporting work or study but reporting ot

How much will it cost?

Fees and Finance

£17,500

This information is for Home and International students applying to study at the University of Huddersfield in the academic year 2026/27.

Modules credits can range from 15 to 60, dependent on the content of the module. Read more about total credits required for a range of degrees, to allow you to calculate the potential total cost.

The tuition fee for the PBA route (60 credits) is £2,917. If you progress onto Data Science BSc(Hons), this will be deducted from the total tuition fee.

Please note that tuition fees for subsequent years may rise in line with inflation (RPI-X) and/or Government policy and/or to ensure our distance learning fees are competitive.

Tuition fees will cover the cost of your study at the University. Read more about what is and is not covered by Tuition Fees including compliance for Goods and Services Tax for International Students studying an online course.

For detailed information please visit our Distance Learning fees and finance page.

£17,500

This information is for Home and International students applying to study at the University of Huddersfield in the academic year 2026/27.

Modules credits can range from 15 to 60, dependent on the content of the module. Read more about total credits required for a range of degrees, to allow you to calculate the potential total cost.

The tuition fee for the PBA route (60 credits) is £2,917. If you progress onto Data Science BSc(Hons), this will be deducted from the total tuition fee.

Please note that tuition fees for subsequent years may rise in line with inflation (RPI-X) and/or Government policy and/or to ensure our distance learning fees are competitive.

Tuition fees will cover the cost of your study at the University. Read more about what is and is not covered by Tuition Fees including compliance for Goods and Services Tax for International Students studying an online course.

For detailed information please visit our Distance Learning fees and finance page.

Flexible payments

Learn how to pay your fees including flexible instalment options

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Funding options

Explore ways to fund your course with exclusive discounts and government loans

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If you have any questions about Fees and Finance, please email the Student Finance Team.

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Why study Data Science?

Thinking about a future in Data Science? This video covers everything from course content and career prospects to the application process, perfect for anyone considering this exciting and fast-growing field.

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Student Profiles

Discover what our distance learning students have to say about their experiences at the University of Huddersfield by checking out their profiles.

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Distance Learning Blogs

Learn more about distance learning by visiting our blog, where students, staff, and academics share valuable insights on a wide range of interesting topics.

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Virtual Learning on Brightspace

Watch our short film to discover the fantastic range of features of Brightspace, our online workspace for distance learning students at the University of Huddersfield. Study online, from anywhere, any device.

Why Hud

Explore the unique opportunities and resources that make our institution a top choice for students seeking a well-rounded and future-focused education.

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Teaching Excellence

Our online courses are delivered by the same highly qualified academic staff who teach in-person at the University of Huddersfield. This means you’ll benefit from the same high standards in teaching excellence as our on-campus students.

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Affordable Study

Online study can be a cost effective way to obtain a prestigious qualification. There are no travel, accommodation or visa expenses and we don’t charge application or administrative fees. Plus, you can pay our competitive tuition fees in instalments, and spread the costs over time.

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Your Student Experience

Our online courses are designed to be interactive, engaging and inclusive. You’ll have the freedom to learn at your own pace, in your own time, while benefiting from live sessions and contact time with your Module Tutor, Personal Academic Tutor, Academic Skills Tutor and/or Project Supervisor.

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Graduate on campus

Our Distance Learning students are invited to our in-person graduation ceremonies so you can celebrate your success and meet your tutors and classmates. You’ll also have the opportunity to explore our beautiful campus.

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Reduced Inequalities

We’re ranked 1st in the world for Reduced Inequalities in the Times Higher Education Impact Rankings 2025, which celebrate commitment to the Sustainable Development Goals set by the United Nations.

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Careers support

We know you’re coming to university to study on your chosen subject, meet new people and broaden your horizons. However, we also help you to focus on life after you have graduated to ensure that your hard work pays off and you achieve your ambition.

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Student support

At the University of Huddersfield, you will find support networks and services to help you get ahead in your studies. Our Distance Learning Unit Team are at hand to make your online learning journey a positive, rewarding and successful one.

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Teaching Excellence

Great teaching is engaging and inspiring — it helps you reach your full potential and prepares you for the future. We don’t just teach well — we excel — and we have the awards and recognition to prove it.

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Inspiring Academics

Our researchers carry out world-leading work that makes a real difference to people’s lives. Staff within the Department of Computer Science may teach you on this course.

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Research Excellence

You’ll be taught by staff who want to support your learning and share the latest knowledge and research.

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Further Study

If you want to continue your learning beyond your undergraduate degree, there is a range of financial support available for postgraduate study, including discounts for Huddersfield graduates.

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Legal information

When you enrol as a student of the University, your study and time with us will be governed by our terms and conditions, Handbook of Regulations and associated policies. It is important that you familiarise yourself with these as you will be asked to agree to them when you join us as a student. You will find a guide to the key terms here, along with the Student Protection Plan.

Although we always try and ensure we deliver our courses as described, sometimes we may have to make changes for the following reasons:

Changes to a course you have applied for but are not yet enrolled on

If we propose to make a major change to a course that you are holding an offer for, then we will tell you as soon as possible so that you can decide whether to withdraw your application prior to enrolment. We may occasionally have to withdraw a course you have applied for or combine your programme with another programme if we consider this reasonably necessary to ensure a good student experience, for example if there are not enough applicants. Where this is the case we will notify you as soon as reasonably possible and if you are unhappy with the change we will discuss with you other suitable courses we can transfer your application to. If you do not wish to transfer to another course with us, you may cancel your application and we will refund you any deposits or fees you have paid to us.

Changes to your course after you enrol as a student

Changes to option modules

Where your course allows you to choose modules from a range of options, we will review these each year and change them to reflect the expertise of our staff, current trends in research and as a result of student feedback or demand for certain modules. We will always ensure that you have an equivalent range of options to that advertised for the course. We will let you know in good time the options available for you to choose for the following year.

Major changes

We will only make major changes to non-optional modules on a course if it is necessary for us to do so and provided such changes are reasonable. A major change is a change that substantially changes the outcomes, or a significant part of your course, such as the nature of the award or a substantial change to module content, teaching days (part time provision), type of delivery or assessment of the core curriculum. For example, it may be necessary to make a major change to reflect changes in the law or the requirements of the University’s regulators or a commissioning or accrediting body. We may also make changes to improve the course in response to student, examiners’ or other course evaluators’ feedback or to ensure you are being taught current best practice. Major changes may also be necessary because of circumstances outside our reasonable control, such as a key member of staff being unable to teach due to illness, where they have a particular specialism that can’t be adequately covered by other members of staff; or due to pandemics, other disasters (such as fire, flood or war) or changes made by the government.

Major changes would usually be made with effect from the next academic year, but may happen sooner in an emergency. We will notify you as soon as possible should we need to make a major change and will consult with affected groups of students and any changes would only be made in accordance with our regulations. If you reasonably believe that the proposed change will cause you detriment or hardship we will, if appropriate, work with you to try to reduce the adverse effect on you or find an appropriate solution. Where an appropriate solution cannot be found and you let us know before the change takes effect you can cancel your registration and withdraw from the University without liability to the University for any additional tuition fees. We will provide reasonable support to assist you with transferring to another university if you wish to do so and you may be eligible for an exit award depending on how far through your course you are.

In exceptional circumstances, we may, for reasons outside of our control, be forced to discontinue or suspend your course. Where this is the case, a formal exit strategy will be followed in accordance with the student protection plan.

The Office for Students (OfS) is the principal regulator for the University.

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