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This three-year BSc (Hons) Data Science programme will introduce you to the fascinating world of Data Science.
On this programme, you will be trained in the use of software tools and environments currently used by the industry, learn from real-life case scenarios, develop practical data science skills, and gain valuable work experience with a business or community organisation.
Through lectures, workshops, tutorials and labs, you will explore key areas in Data Science such as:
By the end of this Data Science degree, you will have gained the essential knowledge and skills needed to prepare you for a successful career in the field of Data Science. This course is also excellent preparation for those who wish to continue into further study or research in scientific areas of Computer and/or Data Science.
This course is offered in partnership with QA Higher Education who are approved by London Metropolitan University (London Met) to teach and assess the programme at locations in London City, Birmingham and Manchester. The course is also delivered by London Met at the main campus in London – please check the London Met main website for details. London Met sets the standards for the course, making sure the curriculum, teaching, assessments and overall student experience meet the University’s quality expectations.
On this course you will explore modules covering topics such as programming, mathematics, fundamentals of computing, data engineering, AI, big data, and many more exciting areas in the field of Data Science.
In taking these modules, you will be trained in the use of software tools and environments currently used by the industry, learn from real-life case scenarios and gain practical data science skills to prepare you for future employment.
This course also has a module dedicated to your future career prospects – the Career Development Learning module. On this module you will have the opportunity to undertake a professional activity with a business or community organisation, enabling you to gain valuable work experience, develop your critical thinking and problem-solving skills, and give you access to professional networks
You will be taught via lectures, tutorials, workshops and labs.
Module assessment typically consists of a combination of assessment types, including coursework, in-class tests, quizzes, MCQs, and unseen exams. Coursework can include solution modelling such as Data models, a Data Program code in addition to a written report/essay.
At London Metropolitan University, we’re focused on a digital future and your degree plays an important part in preparation for this, helping you to achieve your employability goals and life ambitions. That’s why we use a blended learning model, combining online and on-campus learning. You can find out more about our approach to blended learning, including the equipment you will need, on our blended learning page.
Alongside our daytime timetables, we also offer evening and weekend timetables. These options offer the same levels of study support with the flexibility to balance your full-time studies with personal commitments.
The above timings are indicative only; the exact timings of your teaching and information on which sessions are on-campus and which are live online will be confirmed at enrolment when you receive your timetable. As part of your course, you will also need to spend time on self-guided learning, including completing any assessments.
All modules are core and are worth 15 credits unless otherwise specified.
The aim of this module is to introduce you to methods of analysing data using appropriate statistical software.
You will learn how to identify different types of data, how to summarise and present data, and how to use sample data to make inference about population parameters. You will also gain the skills to use an appropriate software package such as Excel or SPSS to investigate and interpret data to make informed decisions.
Topics such as descriptive statistics, inferential statistics, discrete and continuous probability distributions and hypothesis testing for qualitative and quantitative data will be covered on this module.
On this module you will be introduced to the basic terminologies used in finance and develop the mathematical techniques needed to solve real-life problems in finance.
This module will provide introductory knowledge of Excel, and the Excel built functions in financial computing.
You will also develop your understanding of mathematical aspects of interest (simple, compound), learn how to apply AP for straight line depreciation and GP for declining balance depreciation in finance, and discover how to construct an amortisation schedule.
This module will introduce you to the principles of information processing and provide you with an overview of the information technologies for digital data processing using computational and communication devices.
You will gain the knowledge and skills needed to use second programming language and develop your understanding of the concepts of usability, quality, complexity, security and privacy of information.
On this module you will also design data structures and algorithms for digital information processing using sequential, iterative and recursive algorithms for solving typical problems in numerical and text processing.
On this module you will discover the role of information management and information systems within business and be introduced to the wider business environment.
You will be provided with an overview of the nature of organisations, their business models, and how key areas operate to meet business objectives. You will also be introduced to areas such as organisational culture, data, information and knowledge management and the role of information in organisational decision making.
By the end of the module, you will have an appreciation of the effect of ICT on organisational performance and understand the processes of developing and maintaining information systems, software products and services.
This module aims to explore a range of mathematical techniques such as set theory, logic, relations and functions, algebra, differentiation and integration.
You will discover the meaning of mathematical definitions of sets/propositions and learn how to perform set/logic operations.
On this module you will also develop your skills in formulating, manipulating and solving algebraic equations, and further your understanding of vector algebra, differentiation’s and integration in technology and computing.
This module aims to enhance your interest, ability and confidence in using a programming language.
In taking this module, you will gain the basic knowledge and experience needed to solve simple programming problems using established techniques in program design, development and documentation.
You will learn how to design, implement and test object-orientated programs and will be given the opportunity to self-study a popular programming language and obtain a completion certificate.
On this module you will develop your understanding of data engineering concepts, techniques and tools and cover the basics of data modelling, storage, retrieval, and processing for data analysis needs.
You will learn how to apply the practical skills of data engineering tools and techniques to solve real world problems as part of a team and develop your awareness of the latest developments in data engineering.
This module is designed to introduce you to data programming through various programming concepts related to data.
On this module you will cover topics such as data structures, selection, iteration, data input and output with error handling and gain skills in data programming, design and coding.
You will also utilize the programming language Python to prepare, analyse, process and present data science solutions for business applications.
This module aims to provide you with the knowledge of fundamental concepts and techniques of data analytics, covering topics such as descriptive statistics for exploratory data analysis, correlation analysis and the linear regression model.
On this module you will discover and gain an understanding of the data analytics lifecycle and develop your practical data analytical skills to resolve real world data analytical problems.
On this module you will understand and put into practice the techniques available for analysing, designing and developing database systems.
You will learn database programming language skills, develop your understanding of data modelling and design concepts, and will discover the issues governing the design and implementation of database systems.
By the end of this module, you will have the knowledge to design and implement a database system from a conceptual data model and be able to extract and manipulate data using relational algebra and SQL.
This module will introduce you to the Legal, Social, Ethical and Professional Issues (LSEPI) underpinning the computing discipline and cover social responsibility.
You will discover the importance of ethical issues underpinning academic research and professional accountability and explore current regulations and professional body guidelines governing the computing discipline.
You will also develop your understanding of professional bodies, code of conducts and professional certifications to become more prepared for the world of work.
The main aim of this module is to introduce you to the fundamental concepts and key techniques of data science and understand its applications in a wide range of business contexts.
You will explore areas such as data understanding, preparation, modelling, results evaluation and data visualisation techniques which are helpful in assisting businesses make effective data-driven decisions.
You will be introduced to the practical application of tools and techniques needed to perform data science projects in a modern business environment.
On this module you will investigate real-life statistical data and discover the mathematical and statistical modelling techniques which are applied when making decisions in areas of finance.
In using a selection of suitable software such as Excel, R and SPSS, you will learn how to fit statistical models to data and investigate and interpret the results to make informed decisions.
This module will also enable you to develop your skills in statistical and mathematical modelling of real financial data to enhance your future employability.
This module will introduce you to the key concepts, principles and techniques in AI, and demonstrate how to apply them in areas such as image recognition and price forecasting.
You will explore topics such as problem solving, knowledge representation, logical and probabilistic inference and machine learning using methods of automata theory, logics, probability theory and statistics.
You will also learn how to build and design basic AI programs which have intelligent behaviour, rational thinking, and can learn from experience and discover and comment on the impact of AI on individuals, organisations and society as a whole.
This module enables you to undertake a professional activity with a business or community organisation, enabling you to gain valuable work experience, develop your critical thinking and problem-solving skills, and give you access to professional networks.
The activity can be:
A complete list of accepted activities can be found on WebLearn. All career development learning activities must be approved before they are taken up.
You will have the opportunity to engage in any one or combination of career development learning activities for a total of 150 hours, 70 hours of which is direct engagement in any one or combination of career development learning activities. For this you will track your progress in a tri-weekly learning log.
On this module you will build upon your prior learning of database design and implementation and explore advanced SQL and current topics in database technology such as NoSQL.
Using web database technologies, you will gain transferable skills by designing and developing complex ‘real life’ database applications for a given business scenario. You will also explore key issues underpinning database management systems and discover current developments in database technologies.
This module will also provide you with the opportunity to prepare for the first stage of Oracle professional certification.
This module will introduce you to the strategic relevance and effective management of Big Data and Artificial Intelligence (BDAI) in a business context.
Digitalisation has enabled businesses to address increasingly complex challenges through advanced technologies. This trend has been driven by the availability of large quantities of data – big data (BD) – and the improved opportunities for using this data through artificial intelligence (AI).
On this module you will explore BDAI management theories through workshops and utilise real-world cases to further your understanding of how these theories can be applied to practice.
You will also discover how to lead successful BDAI initiatives by prioritizing the right opportunities such as building a diverse team, shaping strategies and strategic experiments, and managing business solutions to benefit organisations.
On this module you will explore how projects are analysed, developed and managed in a business setting via case studies and discover the different types of project methodologies that are used.
You will engage in using a methodology such as Agile in a team setting to understand the different roles within the Agile methodology.
By using this methodology, you will examine the role and value of UML (Unified Modelling Language) and other project management tools.
This module will enable you to demonstrate your recently acquired knowledge and skills through a final project.
On this module you will enhance your professional and personal development in learning how to plan and carry out a project and develop your reporting, communication and project management skills.
You will choose a project that may require a solution to a specific problem, creation of an artefact in a real-world environment or an investigation of innovative ideas and techniques related to an area within your field of study. All proposals must be submitted through an approval process.
You will be allocated a supervisor who will be your main contact for advice on your project.
The course information displayed on this page is correct for the academic year 2026/27. We aim to run the course as advertised; however, changes may be necessary due to updates to the curriculum (due to academic or industry developments), student demand, or UK compliance reasons.
To study this programme, you will need to meet the following entry requirements:
Additionally, during the admissions process, you will be asked to attend either an academic or admissions interview.
We encourage and will consider applications from mature students who haven’t recently undertaken a formalised course of study at A-level or equivalent, but who can demonstrate workplace experience, indicating their ability to complete the course successfully on a case-by-case basis.
If you do not meet these entry requirements, we also have a Data Science (including foundation year) BSc (Hons) that offers a more supported route into undergraduate study.
Please note: We are not currently able to sponsor International students to study this programme at London Metropolitan University Centres, therefore if you require sponsorship to study as an International student, this course will be unavailable to you.
If you are an international student interested in this course and would like to discuss alternative options available to you, please contact 020 3944 1243.
£9,790 per annum*
*Please note yearly fees may increase in line with inflation & Government guidelines.
Your tuition fees cover the cost of teaching, access to resources, registration costs, and Student Support Services. They do not include the cost of course books, stationery and photocopying/printing costs, accommodation, living costs, travel, hobbies, sports or other leisure activities.
Access to a laptop/PC with a microphone, speakers, webcam and a reliable internet connection is required for accessing your live online sessions and to work on assignments.
In addition to the tuition fees, you should be prepared to buy some of the course texts which are around £30 each. This would average around £200 per annum.
Students from the UK may be able to receive financial support from the Government to help fund your studies, subject to your eligibility. You can learn more about Student Finance and check your eligibility on the Student Finance website.
For more information on tuition fees, student finance, and payments, please visit our Finance page.
This course will prepare you to work in various fields including data analytics, data programming, data visualisation, IT data consultation, big data solution designing, data solution development, and more.
Upon successful completion of this course, typical job roles you can apply for include roles such as:
This course is also excellent preparation for those who wish to continue into further study or research in scientific areas of Computer and/or Data Science.
You can apply online to study this programme through the application links on this page.
As part of your application, you are required to provide some supporting documents (examples below):
Next application deadline: View Important Dates
Select your chosen intake, location and study timetable and apply online using the links below to the QA Higher Education application portal.
November 2026 intake
Birmingham
London
Manchester
We welcome applications from disabled students and are committed to ensuring an equal and accessible application journey. Your application will be considered on an equal basis to all other applications. Please contact us if you require any assistance. This website is continually optimised to adhere to accessibility best practice guidelines; tools to assist users with specific accessibility requirements have also been provided. More information is available in our accessibility statement.
£9,790 per annum (26/27)
Undergraduate
3 years
November
April
August
96 UCAS points (or equivalent) and GCSE English & Maths grade C/4.
GCSE English Language at grade C (grade 4) or above (or equivalent).
Full-time blended learning: Daytime or Evening and Weekend delivery
Coursework, in-class tests and exams
London
Birmingham
Manchester