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Data Science (Distance Learning) BSc(Hons)

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    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.

    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

    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.

    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 3 years or 6 years. You’ll need to dedicate a minimum of 10 hours study per 20 credit module or 30 hours per week (full-time) or, on average, 15 hours per week (part time). This will cover all aspects of guided independent study, including seminars, interaction with discussion forums, as well as 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.

    Teaching 

    Our Distance Learning courses have a range of start dates throughout the academic year, with assessments and examinations (if applicable) based on these. Start dates can be found on each course page. Information about University term dates including vacation dates are available here. Our Distance Learning courses are taught online so you have the flexibility to choose when and how you study. Unless it says otherwise in a module description, teaching on this course will be 100% online.

    Feedback 

    Feedback (usually written) is normally provided on all coursework submissions within three term time weeks – unless the submission was made towards the end of the session in which case feedback would be available on request after the formal publication of results. Feedback on final results is available on request after the publication of results.

    Progression 

    You may progress to the next stage of your course or research degree, subject to meeting University assessment criteria and professional, statutory or regulatory body guidelines.

    Further information 

    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.

    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 joint first in the country for National Teaching Fellowships, which mark the UK’s best lecturers in Higher Education, winning a total of 23 since 2008 (2024 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).

    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.

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    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.

    Changes to a course you have applied for

    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.

    Cancellation of a course you have applied for

    Although we always try and run all of the course we offer, 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 to ensure you have a good learning experience. Where this is the case we will notify you as soon as reasonably possible and we will contact you to discuss other suitable courses with us we can transfer your application to. If we notify you that the course you have applied to has been withdrawn or combined, and 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

    We will always try to deliver your course and other services as described. However, sometimes we may have to make changes as set out below:

    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 a range of options to choose from and 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 the core curriculum of a course or to our services if it is necessary for us to do so and provided such changes are reasonable.A major change in this context is a change that materially changes the services available to you; or 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), classes, 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; to meet the latest requirements of a commissioning or accrediting body; to improve the quality of educational provision; in response to student, examiners’ or other course evaluators’ feedback; and/or to reflect academic or professional changes within subject areas.Major changes may also be necessary because of circumstances outside our reasonable control, such as a key member of staff leaving the University or being unable to teach, where they have a particular specialism that can’t be adequately covered by other members of staff; or due to damage or interruption to buildings, facilities or equipment.

    Major changes would usually be made with effect from the next academic year, but this may not always be the case. We will notify you as soon as possible should we need to make a major change and will carry out suitable consultation with affected students.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 contact us in writing before the change takes effect you can cancel your registration and withdraw from the University without liability to the University for future tuition fees. We will provide reasonable support to assist you with transferring to another university if you wish to do so.

    Termination of course

    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 and we will notify you as soon as possible about what your options are, which may include transferring to a suitable replacement course for which you are qualified, being provided with individual teaching to complete the award for which you were registered, or claiming an interim award and exiting the University.If you do not wish to take up any of the options that are made available to you, then you can cancel your registration and withdraw from the course without liability to the University for future tuition fees and you will be entitled to a refund of all course fees paid to date.We will provide reasonable support to assist you with transferring to another university if you wish to do so.

    When you enrol as a student of the University, your study and time with us will be governed by a framework of regulations, policies and procedures, which form the basis of your agreement with us. These include regulations regarding the assessment of your course, academic integrity, your conduct (including attendance) and disciplinary procedure, fees and finance and compliance with visa requirements (where relevant). It is important that you familiarise yourself with these as you will be asked to agree to abide by them when you join us as a student. You will find a guide to the key terms here, along with the Student Protection Plan, where you will also find links to the full text of each of the regulations, policies and procedures referred to. You should read these carefully before you enrol. Please note that this information is subject to change and you are advised to check our website regularly for any changes before you enrol at the University. A person who is not party to this agreement shall not have any rights under or in connection with it. Only you and the University shall have any right to enforce or rely on the agreement.

    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.

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