Why study this course?

This Data Science BSc course offers a comprehensive introduction to the most important areas of the discipline, including data programming, statistical modelling, business intelligence, machine learning and data visualisation.

Developed with input from industry experts, this course covers all the necessary skills and competencies required to delve deeper into this fascinating field. By the end of the BSc degree, you’ll be ready to apply for rewarding roles in the data science and big data industries, as well as the many sectors and organisations that increasingly require data scientists.

More about this course

Designed by academics from both Mathematics and Applied Computing backgrounds, this course is made up of fine-tuned modules which are prepared with your future in mind. The course will foster your learning development using a range of tools and big data platforms, allowing you to continue to specialise in data engineering, analytics, big data visualisation, statistical modelling and machine learning.

During your studies you’ll be encouraged to:

  • apply maths, statistics and science practice
  • recognise and exploit business opportunities using data science innovation
  • find a solution to domain-specific problems using data science capability
  • utilise a range of coding practices
  • build scalable data products for strategic or operational business and contribute through the product life cycle
  • use tools such as Spark, Kafka, Hadoop, Oracle, SQL Server, Linux, Apache Airflow, RStudio, Python - Jupyter, Tableau, and D3 technology

Your learning will see you attend a variety of scheduled sessions such as lectures, tutorials, and workshops. This will be further developed by your revision of module materials and learning exercises outside of scheduled teaching hours. Throughout your learning experience you’ll find the teaching team on hand to support you.

What’s more, we have a wealth of appropriate blended learning technologies, such as the University’s virtual learning environment WebLearn, our library’s e-books and our online databases. These will further facilitate and support your learning, in particular to:

  • deliver content
  • encourage your active learning
  • provide formative and summative assessments with prompt feedback
  • enhance your course engagement

The specialist nature of this course will allow you to explore and experience advanced techniques in data science and data analytics. You’ll acquire practical skills, often first-hand from an external organisation, which will prepare you for your future as a data scientist.

You can get a taste for life at our School of Computing and Digital Media by taking a look at our showcase of recent student work.


You’ll be provided with opportunities to develop an understanding of good academic practice, as well as the skills necessary to demonstrate this. In particular, you’ll be encouraged to complete weekly tutorial and workshop exercises as well as periodic formative diagnostic tests to enhance your learning. During tutorial and workshop sessions you’ll receive ongoing support and feedback on your work to promote engagement and provide the basis for tackling the summative assessments.

You’ll be assessed by a variety of methods throughout your studies. Module assessment typically consists of a combination of assessment methods including:

  • coursework
  • in-class tests
  • exams

Coursework can include an artifact such as an output of dataset analysis, application of algorithms, data trends or program code in addition to a written report/essay. The volume, timing and nature of assessment will enable you to demonstrate the extent to which you have achieved the intended learning outcomes.

Formative and summative feedback will be provided using a variety of methods and approaches, such as learning technologies and one to one and group presentations of the submitted work at various points throughout the teaching period.

Fees and key information

Course type
UCAS code D300
Entry requirements View
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Entry requirements

In addition to the University's standard entry requirements, you should have:

  • a minimum grade C in three A levels (or a minimum of 96 UCAS points from an equivalent Level 3 qualification, eg BTEC Level 3 Extended Diploma, Advanced Diploma, Progression Diploma or Access to Higher Education Diploma of 60 Credits)
  • English language and Mathematics GCSEs at grade C/4 or above (or equivalent)

Applicants with relevant professional qualifications or extensive professional experience will also be considered.

If you don’t have traditional qualifications or can’t meet the entry requirements for this undergraduate degree, you may still be able to gain entry to the four-year Data Science (including foundation year) BSc programme.

Accreditation of Prior Learning

Any university-level qualifications or relevant experience you gain prior to starting university could count towards your course at London Met. Find out more about applying for Accreditation of Prior Learning (APL).

English language requirements

To study a degree at London Met, you must be able to demonstrate proficiency in the English language. If you require a Student visa you may need to provide the results of a Secure English Language Test (SELT) such as Academic IELTS. This course requires you to meet our standard requirements.

If you need (or wish) to improve your English before starting your degree, the University offers a Pre-sessional Academic English course to help you build your confidence and reach the level of English you require.

Modular structure

The modules listed below are for the academic year 2023/24 and represent the course modules at this time. Modules and module details (including, but not limited to, location and time) are subject to change over time.

Year 1 modules include:

This module develops the mathematical and statistical tools that are used in the mathematics of finance. It also introduces methods of analysing data using appropriate statistical software.
This module introduces the basic terminologies used in finance and develops the mathematical techniques to solve problems in the area of finance. Descriptive statistics and statistical techniques that are useful to present, analyse and make inferences about data are also introduced. A selection of suitable software (e.g. Excel, R, SPSS) will enable students to analyse data in order to make informed decisions.

This module currently runs:
  • spring semester - Tuesday afternoon

Students will receive an introduction to the principles of information processing and an overview of the information technologies for digital data processing using computational and communication devices, including an initial understanding of the requirements for usability, quality, complexity, security and privacy of the developed solution. The students will obtain initial practical skills in modelling, design, implementation and testing of software systems for real-world application using a suitable programming language.

This module currently runs:
  • autumn semester - Tuesday afternoon

Students will receive an introduction to the business environment and the role of information management and information systems within business.
The module develops an understanding of the Information Systems, the Software Development process and the basic technology underpinning these systems. This will include database management systems and the Internet. Students which will develop key skills and knowledge in the aspects of an information system, including databases, websites, and scripts with particular regard to usability.
• The module aims to provide an overview of the nature of organisations, their business models, and how key areas operate to meet business objectives. It introduces students to organisational culture, data, information and knowledge management, and the role of information in organisational decision making.
• Within the module the students will be given an appreciation of the effect of ICT on organisational performance, and a basic understanding of the processes of developing and maintaining information systems, software products and services.
• An introduction to underlying technologies (e.g., databases, Internet and Web) is embedded in the module, which also seeks to develop basic competence and confidence in the use of appropriate tools, techniques and academic and communication skills, with an underlining awareness of legal, social, ethical and professional issues.

This module currently runs:
  • all year (September start) - Thursday morning

This module develops a range of mathematical techniques including set theory, logic, relations and functions, algebra, differentiation and integration. The techniques provide the foundation for further study of Mathematics, Computer Science and Computer Games Programming and Computer Systems Engineering.

This module currently runs:
  • all year (September start) - Tuesday morning

This is an introductory programming module, designed to develop interest, ability and confidence in using a programming language. Students will gain the basic knowledge and experience to solve simple programming problems using established techniques in program design, development and documentation. It is expected that on completion of this module, students will be able to design, implement and test object-oriented programs. The module also enables to self-study a popular programming language and obtain a completion certificate. The student is also expected to develop their confidence needed to program solutions to problems through a series of practical programming exercises.

Assessment: Multiple choice test (30%) + Programming certificate(10%) +Coursework (60%) [Pass on aggregate]

Year 2 modules include:

This module currently runs:
  • autumn semester - Tuesday morning

This module introduces fundamental concepts and techniques of data analytics. The module covers descriptive statistics for exploratory data analysis, correlation analysis and linear regression model. A substantial practical element is integrated into the module to enable students to apply data analytics techniques for real world data analytical problems.

The aims of this module are to enable students to:
• gain a thorough understanding of fundamental concepts of data analytics
• acquire knowledge of descriptive statistics, correlation analysis, and linear regression analysis
• have knowledge of and gain understanding of the data analytics lifecycle
• develop practical data analytical skills to resolve real world data analytical problems

This module currently runs:
  • spring semester - Monday morning

This module provides an understanding of data engineering concepts, techniques and tools. It covers the basics of data modelling, storage, retrieval, and processing for data analysis needs. The module aims to provide a set of building blocks through which a complete architecture for modelling, storing and processing data can be constructed. It aims to enable students to apply the practical skills of data engineering techniques in the real world.

The aims of this module are to:
• provide students with an understanding of data engineering concepts and techniques
• enable students to appreciate various modern data engineering tools
• enable students to acquire fundamental knowledge and skills of data modelling, storage, retrieval, and processing for data analysis
• develop students with practical skills in applying tools and techniques to solve real world problems

This module currently runs:
  • autumn semester - Wednesday morning

Introduces techniques for analysing, designing and implementing database systems. An understanding of data modelling and design concepts is provided and database programming language skills are taught. The practical aspect of developing database systems is emphasised and use is made of a widely-used commercial database system (e.g. Oracle) for this purpose.

The module will enable students to give an introduction to the issues governing the design and implementation of database systems. Theoretical aspects of designing sound database systems, as well as the practical aspects of implementing such systems are presented. This therefore allows students to understand, and put into practice, the techniques available for analysing, designing and developing database systems.

This module currently runs:
  • spring semester - Thursday afternoon

This module focuses on professional, social, ethical issues within the context of social responsibility and covers relevant computer laws (LSEPI) underpinning the Computing discipline. The focus of the module is empowering student to take their place in society as socially responsible professionals and allowing the exploration of self-awareness, empathy, self-efficacy and engagement in students

Assessment: Coursework (100%)

The aims of this module are to:
• Expose students to a range of professional and ethical issues to prepare them to develop their own response to working with a professional outlook.
• Prepare students for the world of work and equip them with the knowledge and appreciation of professional bodies, code of conducts and professional certifications.
• Provide students with knowledge and understanding of the regulations governing the digital environment (e.g. Internet) and social, ethical and professional issues (LSEPI) underpinning the Computing discipline.
Introduce students to academic research and research ethics, and to academic writing.

This module currently runs:
  • autumn semester - Tuesday afternoon

The module is designed to introduce data programming through various programming concepts related to data. The module covers data structures, selection, iteration, data input and output with error handling. In particular the module focuses on creating data science solutions for business applications. Programming language Python is integrated into the module practical element to prepare, analyse, process and present data science solutions.

  1. Students will gain knowledge and skills of the principles of data programming, design and coding.
  2. Develop programming skills for data manipulation and presentation.
  3. Develop skills to create data solutions for business applications using programming.
  4. Enhance skills for logical reasoning, problem solving and evaluation.
This module currently runs:
  • spring semester - Friday morning

This module will enable students to understand the fundamental concepts of data science and appreciate key techniques of data science and its applications in a wide range of business context. Students will be exposed to data understanding, preparation, modelling, results evaluation and data visualisation techniques that can assist businesses in making effective data-driven decisions to improve productivity and consumer satisfaction. Students will be introduced to the practical application of tools and techniques required to perform data science projects in a modern business environment.

This module currently runs:
  • all year (September start) - Monday afternoon

The module covers mathematical and statistical modelling techniques that are applied in making decisions in areas of finance. It also enables the student to investigate real-life statistical data. This module introduces important financial concepts and develops statistical modelling techniques. Statistical regression models are applied to financial data (e.g., credit scoring, default time analysis) and mathematical modelling of stock and option prices is investigated. A selection of suitable software (e.g., Excel, R, SPSS) will enable students to analyse data in order to make informed decisions. The students will develop skills in statistical and mathematical modelling of real data to aid future employability

Year 3 modules include:

This module currently runs:
  • spring semester - Wednesday morning

This module surveys essential principles, methods, and techniques in AI and machine learning. It covers a broad range of AI topics such as problem solving, knowledge representation, logical and probabilistic inference, and machine learning using methods of automata theory, logics, probability theory and statistics. It discusses examples of intelligent systems and studies how to develop applications that can learn from experience such as expert systems, automatic classifiers and autonomous systems planning their actions and communicating in natural language. Students will be offered lectures, which introduce key concepts, explain main principles and techniques in AI, and demonstrate how to apply them in areas such as image recognition and price forecasting.

The workshop will provide practical sessions to help students understand the content of the lectures and build the necessary skills to develop AI-applications using suitable problem descriptions and datasets.

This module currently runs:
  • autumn semester - Thursday morning

This module provides an understanding of big data processing and visualisation approaches and challenges along with various techniques and technologies. It covers big data processing and basic visualisation concepts, different type of big data and real-time log analysis using charts, graphs, diagrams of 2D data. A substantial practical element is integrated into the module to enable students to apply big data processing, querying and visualisation techniques for real-world problems using cloud and desktop technologies.

The module aims are to:

• Enable students to gain further understanding of data streaming, processing and querying.
• Enable students to gain understanding of the fundamental concepts of data visualisation.
• Develop students’ practical skills in applying data processing and visualisation techniques for real world big data problems.
• Expose student’s expertise in data model visualisation techniques of different types of data and types of tools and their methods.

This module currently runs:
  • spring semester - Wednesday afternoon
  • autumn semester - Wednesday afternoon

The module enables students to undertake an appropriate, short professional activity related to their course at level 6 with a business or community organisation and to gain credit for their achievements. The activity can be professional training or certification, a volunteering activity, employment through internal or external work-based placements, research-related activities, business start-up projects, entrepreneurship programs and more. Please see the complete list of accepted activities on WebLearn.

Students are expected to engage in any one or combination of career development learning activities for a total of ~70 hours which should be recorded clearly in a tri-weekly learning log – part of the portfolio. The ~70 hours can be completed in ~30 working days in FT mode or spread over a semester in PT mode.

Students are expected to complete a total of ~150 hours, 70 hours of which is direct engagement in any one or combination of career development learning activities. Progress should be recorded clearly in tri-weekly learning logs which are part of the portfolio. The ~150 hours can be completed in ~35 working days in FT mode or spread over a semester in PT mode.

Students should register for the module to be briefed, undergo induction and module planning and have their career development learning activity approved before they take up the opportunity. Students must be made aware that both the "Learning Agreement" (LA) and relevant "Health and Safety (H&S) checklist", where applicable, must be approved before starting the learning activity. Activities started without prior explicit supervisor approval will not be accepted.

The module aims to provide students with the opportunity to:
• Gain a valuable experience of the working environment and the career opportunities available upon graduation.
• Sharpen critical thinking, creative problem-solving and the ability to articulate solutions correctly to decision-makers and budget-holders alike.
• Undertake a career development learning activity appropriate to their academic level to gain exposure and access to professional networks.
• Extend learning experience by applying and building on their academic skills and abilities by tackling real-life problems through enrichment and extracurricular programs related to student subject areas.
• Enhance existing skills and master new ones through a structured personal and Professional Development Plan (PDP).

This module currently runs:
  • all year (September start) - Wednesday afternoon

The module enables students to demonstrate their acquired knowledge and skills through a systematic and creative investigation of a project work in accordance with their course requirements. The topic of investigation will cover a broad spectrum of various analysis and techniques and will lead to a comprehensive and concise academic/industry-related report. Students will be assisted in exploring areas that may be unfamiliar to them and encouraged to develop innovative ideas and techniques. Students will be able to choose a project that may require the 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 their field of study. Collaboration with outside agencies and projects with industrial, business or research partners/ sponsors will be encouraged.

Assessment: Project Report Interim Submission(25%) + Project process (25%) + Project Report Final Submission(40% -Pass on component) + Viva (10% -Pass on component).

The module aims to develop a wide range of subject specific cognitive abilities and skills relating to intellectual tasks, including practical skills and additional transferable skills of a more general nature and applicable in many other contexts.

Particularly, the module aims to:

•Provide an opportunity to learn, through supervised experience, how to plan and carry out a project through a systematic and creative approach;

•Encourage innovation and originality in approach to investigating a problem in an area that may be unfamiliar to the student;

•Provide opportunity for in depth study of some specialised area of suitable scale and complexity relevant to their course of study;

•Raise awareness in potential business development opportunities in connection to the project work undertaken and of any ethical, legal and professional issues;

•Develop reporting skills as well as the ability to communicate results, conclusions, and the knowledge and rationale underpinning these, to specialists and non-specialist’s audiences, clearly and unambiguously;

•Encourages reflection upon the relationship of design decisions to the appropriateness of the finished task;

•Enhance professional and personal development.

This module currently runs:
  • autumn semester - Wednesday afternoon
  • spring semester - Wednesday afternoon

This module serves as a core module for all maths students (and optional for Data Science students) to do a one-semester project in the broader sense. The feature of the module is summarised as follows.
1. Students will follow their own interest to pursue an individualised study independently under staff supervision.
2. Students taking this module with the same supervisor may study the same subject but the assessments should be individualised.
3. The allocation of supervisors to students should be done at the end of year two. Students can take this module in either autumn or spring period.
The programme of study is very much individualised and there is a variety of format. The following are just two typical examples: (a) Pursue an investigative study on a particular topic, with an assessment of written report plus viva, and (b) A self-negotiated study in any subject area following a printed textbook or online material, assessed by a coursework consisting of a mixture of solutions to exercise questions, a written report, and a viva (oral presentation). In the later case, there must be an “investigative and independent factor” in the study. Any other innovative format is encouraged.

The module aims to
1. Provide students with an opportunity to pursue an academic area of interest independently, subject to the availability of an appropriate supervisor, where a taught module is not available.
2. Develop students’ ability to search the internet and library for useful information.
3. Enrich students’ experience of self-negotiated study.
4. Improve students’ employability by enhancing their skills through report writing and reflection on independent learning.

This module builds upon the student's general understanding of database design and implementation from prior learning. It discusses the key issues underpinning database management systems and their development, provides a strong coverage to advanced SQL which helps preparing for professional certification, and introduces some current topics in database technology. In addition, the module contains a substantial practical element utilising advanced SQL and database application development tools (e.g. Oracle SQL developer, Oracle.NET developer), enabling students to gain transferable skills in designing and developing relatively complex ‘real life’ database applications.

The module will enable students to:-
• gain in-depth understanding of various key issues pertinent to the management and development of modern database applications.
• acquire skills in advanced SQL which provides an opportunity for gaining professional certification.
• be introduced to current developments in database technology thereby raising students’ awareness and understanding of the future trend in database systems development.
• design and develop relatively complex business database systems and applications using industry-standard database products (e.g. Oracle SQL developer, Oracle.NET developer).

This module currently runs:
  • autumn semester - Thursday afternoon

This module provides an introduction to the field of Artificial Intelligence, from its historical context to its current state. Students will research an aspect of AI and work in teams to design an intelligent system and develop a simple prototype.

The module aims to
• to build students’ knowledge and understanding of AI and its range of applications;
• to enable students to use their skills and knowledge to design a contemporary intelligent system;
• to develop students’ critical faculties with respect to the ethics and the issues surrounding AI;
• to build skills in software engineering and prototype development

This module currently runs:
  • spring semester - Friday afternoon

The module is an introduction to modern ideas in cryptography. It proves the background to the essential techniques and algorithms of cryptography in widespread use today, as well as the essentials of number theory underlying them.

The module looks at symmetric ciphersystems and their use in classical cryptography as well as public key systems developed to support internet commerce and deliver data security for private individuals.

The module will enable students to understand the mathematics underpinning key algorithms, how they operate using small values and how computer packages such as MAPLE allow us to apply them at a more realistic scale.

This module currently runs:
  • spring semester - Thursday morning

This module is designed to develop understanding, knowledge and skills associated with the various malicious hacking attacks targeting computer systems and the appropriate safeguards needed to minimise such attacks.

The module aims are to:

1.Provide students with knowledge and understanding of the various hacking methods used in attacking computer systems and networks.

2.Enable students to use appropriate tools and techniques to identify, analyse, evaluate and test computer security vulnerabilities prone to hacking attacks, and develop appropriate procedures, solutions and countermeasures to defend and minimise such attacks.

3.To develop students’ awareness of ethical, professional, and legal issues connected with hacking.

4.Develop students’ knowledge, transferable skills and confidence in the subject leading to further academic and professional progression in this area.

This module currently runs:
  • all year (September start) - Tuesday afternoon

The module introduces the students to financial forecasting using modern statistical modelling techniques. Its aim is to prepare the student for work in a quantitative commercial or scientific environment. Students will be developing problem solving skills. For each given problem, the process of dealing with it includes, searching for appropriate data sets, establishing the right statistical/financial techniques to use, fitting appropriate models, critically appraising the models using diagnostic model tools and finally interpreting the models and drawing conclusions.

The module introduces a model based approach to the construction of software systems using formal specification languages as a basis for the software development. It will provide students with the knowledge and skills to produce formal specifications from informal descriptions and to implement them using appropriate programming techniques.

The module aims are to:

• Introduce model based formal specification languages
• Provide students with the knowledge and skills to construct formal specifications from informal descriptions
• Provide understanding of techniques used in the design and implementation of software systems and relating the principles to real world and practical examples
• Refine formal specifications for implementation and implement them

What our students say

This is a new course, and we are so excited to welcome our first cohort. On hearing about this new Data Science BSc, this is what one of our Data Analytics MSc students had to say:

"I definitely think students would benefit from a data science undergraduate course from the same tutors. It’s great that future students will have the opportunity to build and develop their skills at an early stage in their academic career."
Maya Pun, Data Analytics MSc graduate 2021. Read about Maya's experience at London Met.

"I was intrigued by the accreditations for the Data Analytics MSc, that is, the Chartered IT Professional status by the British Computer Society (BCS), which allows graduates to obtain accreditation that entitles them to professional membership of the BCS." Asya Katanani, graduate. Read about Asya's experience at London Met.

"The opportunity to learn from experienced professors and engage in practical, hands-on learning was highly appealing to me. London Met University is also located in one of the world's most vibrant and diverse cities." George Essel, graduate. Read about George's experience at London Met. 

Where this course can take you

This course will prepare you to work as a data analyst or in the fields of data programming, data visualisation, IT data consultation, big data solution designing or data solution development.

This degree award can put you in a position to apply to companies such as Facebook, Mastercard, Amazon, Microsoft or the BBC for roles such as Junior Data Scientist, Data Science Operational Officer or Associate Data Analyst. 

This course is also excellent preparation for further study or research.

Additional costs

Please note, in addition to the tuition fee there may be additional costs for things like equipment, materials, printing, textbooks, trips or professional body fees.

Additionally, there may be other activities that are not formally part of your course and not required to complete your course, but which you may find helpful (for example, optional field trips). The costs of these are additional to your tuition fee and the fees set out above and will be notified when the activity is being arranged.

Discover Uni – key statistics about this course

Discover Uni is an official source of information about university and college courses across the UK. The widget below draws data from the corresponding course on the Discover Uni website, which is compiled from national surveys and data collected from universities and colleges. If a course is taught both full-time and part-time, information for each mode of study will be displayed here.

How to apply

If you're a UK applicant wanting to study full-time starting in September, you must apply via UCAS unless otherwise specified. If you're an international applicant wanting to study full-time, you can choose to apply via UCAS or directly to the University.

If you're applying for part-time study, you should apply directly to the University. If you require a Student visa, please be aware that you will not be able to study as a part-time student at undergraduate level.

When to apply

The University and Colleges Admissions Service (UCAS) accepts applications for full-time courses starting in September from one year before the start of the course. Our UCAS institution code is L68.

If you will be applying direct to the University you are advised to apply as early as possible as we will only be able to consider your application if there are places available on the course.

To find out when teaching for this degree will begin, as well as welcome week and any induction activities, view our academic term dates.

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