Skip to main content

Data Science (Distance Learning) BSc(Hons)

    • Apply now
    {{menu.previous.name}}
    {{menu.center.name}}
    {{menu.next.name}}

    Ask a question

    Overview

    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).

    Entry requirements

    BCC at A Level . We require one of those qualifications to be in STEM subject (Mathematics, Engineering, Computer Science, Physics, Chemistry or Biology).

    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).

    Merit in T Level in either Science or Engineering.

    MMM in BTEC Level 3 National Extended Diploma in either Applied Sciences, Computing 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.

    Course Detail

    Modules:

    Calculus

    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.

    Probability Theory and Statistical Analysis

    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.

    Linear Algebra

    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.

    Computing Science and Mathematics

    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.

    Programming for Data Science

    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.

    Modules:

    Data Analytics in the Age of Big Data

    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.

    Data Analysis in Practice

    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.

    Relational Databases and Web Integration

    This module aims to equip you with the knowledge and skills needed to design, implement and query a relational database. You’ll be supported in gaining an understanding of the functionality necessary to enable web pages to interact with a database. You’ll be given the opportunity to become familiar with web architectures and the design considerations necessary for implementing a database driven web application.

    Introduction to Artificial Intelligence

    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.

    Advanced Statistical Methods

    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.

    Algorithms and Data Structures

    In 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.

    Modules:

    Machine and Deep Learning

    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

    Text Analytics and Language Processing

    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.

    Database Applications for Big Data

    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.

    Leadership in Data Science

    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.

    Individual Project

    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.

    This 360 credit course is taught over 6 years part-time or 3 years full-time. You’ll need to dedicate approximately 10-16 hours study per module per week. This may include guided independent study such as live sessions and discussion forums as well as independent study including engaging with online materials e.g. assignments, reading and revision. Contact time with tutors is typically in small groups with peers and/or on a 1:1 basis. You’ll be supported throughout your studies by your module tutors and personal academic tutors. 

    The course will be assessed through a variety of forms including portfolios of work, essays, reports, presentations, computer-based tests and short tests.

    The course is designed to be accessible and inclusive.

    Teaching on this course will be delivered by University of Huddersfield academic staff. Teaching and learning activities will be hosted on our partner platform – Coursera.

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

    • Personal computer. Courses are designed for personal computers. Some content may not be fully accessible via mobile devices including but not limited to assessments and where there is a need install supplementary software on your personal computer.
    • Internet connection with sufficient bandwidth to allow video streaming (4Mps minimum).
    • Microphone and webcam.

    Browser

    Your course can be accessed using the latest versions of Google Chrome, Mozilla Firefox, Apple Safari, or Microsoft Edge. Full functionality cannot be guaranteed in older or less commonly used browsers. JavaScript must be enabled in your preferred browser.

    Minimum Specifications

    Your personal computer and webcam will require the following minimum specifications to access your course and study materials:

    Hardware

    • Headphones, soundcard and speakers, microphone, and webcam. (If you are uncertain if your system meets the requirements, please check with the manufacturer or at the place of purchase).
    • Minimum Intel Core i5 (Minimum Dual Core 2 GHz) or AMD A10 or equivalent, 4GB of RAM (recommended 8 GB for better overall experience), with a screen resolution of at least 1280x800px.

    Operating systems

    • Windows: Windows 10 or later. Mac: OS X 10.13 or later.
    • Linux: 64-bit Linux distribution of Ubuntu 16.04+LTS, Fedora 30+ Workstation, RHEL 8+ Workstation or CentOS 8+.
    • Android: OS 10, 11 or 12.
    • iOS: iOS13 or iOS14.

    Internet connection

    Our distance learning courses can be studied from any location, however we recommend a broadband/high speed connection of 10 Mbps download and 5 Mbps upload speed. (If you are unsure if your internet connection speeds meet requirements, please speak with your internet service provider for clarification).

    Other software requirements

    • Microsoft Teams, Adobe Creative Cloud and Office 365. Access to these applications will be provided to you as a University of Huddersfield student. Please note that some software, including Adobe Creative Cloud, will need to be downloaded and installed. You may be required to have full administrator rights to do this, which could be restricted if using an employer’s computer or shared device.

    Mobile App

    The University’s VLE, Brightspace, has a mobile Pulse app that can help students stay connected and on track with their course in Brightspace. The Pulse app only works with some core features of Brightspace. The Pulse app may not work on all Brightspace features. Third-party tools may not work as well. Mobile devices are not suitable for all courses, or some coursework. Users may need to complete some tasks on other non-mobile devices meeting the system requirements.

    Costs

    The costs of IT equipment and internet access are not included in the tuition fees and are your sole responsibility. For more information visit What’s included in your tuition fee? - University of Huddersfield.

    1. The University of Huddersfield has been rated Gold in all three aspects of the Teaching Excellence Framework (TEF) 2023. We were the only university in Yorkshire and the Humber and the North West to achieve Gold ratings in all three aspects of the TEF among those announced in September 2023. In fact only 13 Universities, out of the 96 that were announced in September 2023, were Gold in all three ratings.

    2. Our teaching staff rank first in England for the proportion with higher degrees and teaching qualifications, as well as being top five for those holding doctorates (HESA 2025). So you’ll learn from some of the best, helping you to be the best.

    3. We are second in the country for National Teaching Fellowships, which mark the UK’s best lecturers in Higher Education, winning a total of 24 since 2008 (2025 data).

    4. We won the first Global Teaching Excellence Award, recognising the University’s commitment to world-class teaching and its success in developing students as independent learners and critical thinkers (Higher Education Academy, 2017).

    Visit ‘Our experts’ page where you’ll find in-depth profiles of all our academic staff

    At Huddersfield, you'll study the Global Professional Award (GPA) alongside your degree* so that you gain valuable qualities and experiences that could help you to get the career you want, no matter what your field of study is. On completion of the Award, you'll receive a GPA certificate from the University of Huddersfield, alongside the specialist subject skills and knowledge you gain as part of your degree, which may help to set you apart from other graduates.

    Giving students access to the Global Professional Award is one of the reasons the University won ‘Best University Employability Strategy’ award at the National Graduate Recruitment Awards 2021. Find out more on the Global Professional Award webpage.

    *full-time, undergraduate first degrees with a minimum duration of three years. This does not include postgraduate, foundation, top-up, accelerated or apprenticeship degrees.

    Study anywhere on any device
    Fit your studies around your lifestyle
    Experience teaching excellence

    Discover more about the course

    Your Career

    Discover the job roles our graduates are working in now.

    Careers advice

    Check out the personalised guidance we offer you.

    Student Support

    Discover all the support available so you can thrive.

    Further Study

    Learn about pursuing a Master’s or PhD at Huddersfield.

    Research Excellence

    See how our innovative research shapes what you'll learn.

    Important information

    We will always try to deliver your course as described on this web page. However, sometimes we may have to make changes as set out below.

    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.

    Fair Processing Notice

    The University partners with ‘Coursera’ for the delivery of this Distance Learning course. The Data collected in the University’s application portal will be used by the Distance Learning Unit to support you through your application and enrolment. We will share your data with Coursera who may contact you to provide support and guidance throughout your application, enrolment and studies. This may mean that we share your data outside the EU. It will not be shared further without your consent. Information about how Coursera processes your data is available on their Privacy notice here.

    The University will store and use the information you provide in accordance with the University’s privacy policy which you can find here.

    You may also be interested in...

    Computer Science BSc(Hons)

    Full-time

    Undergraduate

    Study a range of topics from software engineering to artificial intelligence or distributed computing, to help you build your knowledge of computer science principles and develop your computing skills.

    Find out more How to apply

    Computer Science with Artificial Intelligence BSc(Hons)

    Full-time

    Undergraduate

    This course will give you the opportunity to learn how to build intelligent systems that transform everyday lives.

    Find out more How to apply

    Computer Science with Cyber Security BSc(Hons)

    Full-time

    Undergraduate

    The monetary value of data has led to an increase in cyber-crime, meaning computing professionals with cyber security skills are in high-demand.

    Find out more How to apply

    Computer Science with Games Programming BSc(Hons)

    Full-time

    Undergraduate

    This course covers general computing and software engineering knowledge that apply across the computing industry, as well as games programming skills.

    Find out more How to apply

    Computing BSc(Hons)

    Full-time

    Undergraduate

    Our course cover topics including software engineering, cyber security, plus a foundation of knowledge in of all aspects of computing.

    Find out more How to apply

    Information Technology BSc(Hons)

    Full-time

    Undergraduate

    On our course, you will be supported by our team to develop your creativity and imagination, with our aim being to help you pursue a career within IT.

    Find out more How to apply
    View more courses