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Mathematics and Data Science BSc Honours

Our hands-on course will give you practical experience analysing big data sets to prepare you for exciting careers in this rapidly emerging field
September 2024
3 years full-time
6 years part-time
£9,250 (UK)*
£16,600 (INT)*
Course leader
Brendan Masterson

Data Scientist is in the top-ten of emerging jobs according to the LinkedIn emerging jobs report. Graduates that can combine their mathematical skills and statistical modelling to make sense of big data are in high demand.

Why choose Mathematics and Data Science BSc Honours at Middlesex?

Build on theory to deliver practical solutions to a variety of real-world big data problems. Our project-based approach to assessment will give you a practical education and help you apply your mathematical skills to one of the top emerging job sectors.

What will you gain?

You’ll learn to work with multi-faceted problems across a range of modules and develop your mathematical problem-solving and communication skills.

You'll gain a comprehensive understanding of data wrangling, data analysis and broaden your knowledge of programming and software design and engineering.

We have over 145 years of experience delivering professional, creative and technical education that prepares students – like you – for success in global careers, so find out more today.


The degree is accredited by the Institute for Mathematics and its Applications (IMA).  The IMA is the largest mathematical body in the UK and supports the advancement of mathematical knowledge and its applications to promote and enhance mathematical culture in the United Kingdom and elsewhere, for the public good.

Accreditation by the IMA allows our students to work towards becoming chartered mathematicians within the IMA framework and enhances employability. Visit the IMA.

What you will learn

After completing this course, you'll be able to combine your mathematical skills and statistical modelling to make sense of big data to check and enhance the digital economy.

You'll learn the mathematical theory underpinning data science and deliver practical solutions to a variety of real-world big data problems.

There will be scope to develop your programming and software skills, as well as learn new skills within a work environment through placement opportunities.

The project-based assessment will give you a practical education and prepare you to apply your mathematical skills to one of the top emerging job sectors.

By joining us on this course, you'll benefit from:

  • Modules that teach techniques from machine learning and artificial intelligence
  • Practical real-life experience of working and analysing big data
  • Teaching and support from staff who work and research in all areas of mathematics, with expert knowledge from the industry
  • Project and coursework-based assessment, no end-of-year exams
  • A large, 30-credit third-year project allows you to demonstrate the accumulation of your knowledge to develop a significant piece of work
  • Placement opportunities which add value to the current business within a work environment
  • Find employment in any number of different careers as a mathematics graduate including IT, finance and teaching.

3 great reasons to pick this course

  • Graduate success
    Our graduates work in companies such as Swiss Re and to Norton Rose Fulbright. Most of our graduates have gone on to careers in IT, finance and accounting, and teaching
  • Use top facilities
    You will use our state of the art problem-solving rooms that mimic real-life software development team working, as well as specialised support in our Maths Help Centre
  • Exciting work placement opportunities
    Enhance your employability with the option to take a year-long placement between your second and third years

Keep informed

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Our communications are designed to support you in deciding your future and keep you up to date about student finance, employment opportunities and student activities available at Middlesex University.

This is a three or four year degree, depending on an optional industry placement year. It can also be studied part-time over six years. The work placement year takes place between the second and third years of the degree. Your full-time study years are structured like this:

Year 1

Establishes the fundamental principles of mathematics and data science that will underpin the degree.

Year 2

Builds on the topics in year one, giving you a greater appreciation of statistics, data analysis, mathematics and programming.

Year 3

Make the degree your own. Choose from optional modules related to computing, finance, and mathematics and tailor your studies to your interests and career goals.


  • Year 1

    • Calculus and Geometry (30 credits) - Compulsory

      Following from your previous learning, this module studies calculus and its applications to problem solving. We take a more intuitive geometric approach to learn the techniques in more depth. We will also set the theory up more rigorously in order to fully understand this important mathematical tool.

    • Mathematical Thinking (15 credits) - Compulsory

      Bridging the gap from school or college to university level maths, this module introduces and studies important concepts like logic and sets that form the language of mathematics.

    • Introduction to Programming (15 credits) - Compulsory

      Programming as a way of studying and working with mathematics is becoming a fundamental tool in mathematical problem solving. In this module you’ll be introduced to programming in informal and supportive labs. No prior knowledge of computing is expected.

    • Probability and Data Analysis (30 credits) - Compulsory

      Understanding chance and uncertainty is the core idea behind probability. This module introduces the theory or probability and teaches you how it can be applied to analyse data and base conclusions on it. This is at the heart of data science.

    • Mathematical Models (15 credits) - Compulsory

      Mathematical models help us understand real-life systems and make predictions about their behaviour. In this module you’ll learn to understand the process of mathematical modelling and be introduced to many important models in data science. You’ll learn to make useful prediction about their behaviour and provide solutions where possible.

    • Linear Algebra (15 credits) - Compulsory

      To understand the high-dimensional structures that model data you need to develop the language that describes them. This module teaches you to think and work confidently in higher dimensions and to understand, geometrically, the spaces described.

  • Year 2

    • Problem Solving and Communication (30 credits) - Compulsory

      This module trains you to think correctly about problems, to formulate successful strategies to solve them and to communicate their solutions to others, from professional mathematicians to public communication.

    • Software Design (15 credits) - Compulsory

      This is a hands-on module that will continue to develop your programming skills. You will learn to design your own efficient algorithms, data structures and other aspects of design. In this module you’ll learn to integrate your software with large real-life datasets and databases.

    • Discrete Mathematics (15 credits) - Compulsory

      Discrete objects are used to describe many things you use daily. For example, when you ask your computer to find a route from your home to university what is it doing? In this module you’ll study the theoretical ideas and build up a clearer understanding of their use.

    • Mathematics of Machine Learning (15 credits) - Compulsory

      In this module you will learn to manipulate data in order to produce usable training sets. You will then learn the main classifiers used to learn from your training data. Taught in a practical way, the techniques will be underpinned by a theoretical grounding in the mathematical techniques behind these techniques.

    • Mathematical Statistics (30 credits) - Compulsory

      Following from the probability learned in the first year, you will, in this module, learn how to make sense of data. This can mean modelling the data using probability models and estimating important parameters or using techniques such as regression to estimate trends. The theory will be taught in a practical way using real data to give you invaluable experience of working in this setting.

    • Advanced Calculus (15 credits) - Compulsory

      Dealing with big data means you will often be working in spaces with hundreds of dimensions. This module will, amongst other things, teach you how to generalise calculus to these high dimensional spaces. You will learn how to differentiate and integrate functions in higher dimensions. You will also find out how you can find local minimums and maximums of functions when you can. This will be vital in finding the best solutions to problems in data science.

  • Year 3

    • Neural Networks and Deep Learning (30 credits) - Compulsory

      Neural networks mimic the neurons of the brain to solve problems in artificial intelligence. This module expands on your machine learning module in the second year to produce models that can self-learn. The deep learning section of the module will then apply neural networks to solve a number of highly complex problems.

    • Mathematical Techniques for Optimisation (15 credits) - Optional

      Many problems in data science involve finding an optimal solution to some system – for example finding optimal routing through a network. In this module you will learn a number of techniques for solving these kinds of problems

    • Data Mining (15 credits) - Optional

      Making sense of large datasets and databases can be a combination of art and science. This module will introduce you to the main methods for dealing with data and making sensible conclusions by examining relationships inherent in the data.

    • Time Series (15 credits) - Optional

      A time series is a sequence of values that depend on time, for example stock price. In this module you’ll be taught to analyse the main components of a time series and how to model them.

    • Cryptography and Blockchain (15 credits) - Optional

      Blockchains are the record-keeping technologies behind Bitcoin and other cryptocurrencies. In this module you will learn the main ideas behind the technology and its influence of the financial sector.

    • Stochastic Processes for Finance (15 credits) - Optional

      Modelling stock prices, derivatives and other financial instruments need a good understanding of the probability models that underlie them. This module will introduce these so-called stochastic processes and study their properties, and you’ll learn to use them to make predictions using real-world financial data.

    • Graph Theory (15 credits) - Optional

      This module will continue some of the work you studied in the second year. You will, in this module, learn about the properties of graphs and networks. You will find that these can be used to model complex relationships and can be used to understand connections.

    • Project (30 credits) - Compulsory

      The major project is the culmination of your learning. You will, in this module, get the opportunity to apply all your learning to a significant piece of work that you will be able to use to demonstrate your skills to potential employers.

To find out more about this course, please download the Mathematics and Data Science BSc Honours specification (PDF).

How we'll teach you

Modules are taught using a problem-based approach, giving you time and space to deepen your understanding of the content. The supportive environment in classes and in the Maths Help Centre will encourage you to discuss your work with peers and academics.

You'll be taught by an experienced teaching team with a wide range of expertise and professional experience.

You will learn by attending lectures, seminars and practical workshops. Seminars and workshops are a great opportunity to discuss what you have learnt in lectures and through independent study with your peers and tutors. Most seminar groups have about 25-30 students. For one-to-one support, you will meet with either your personal tutor or module leader.

Your work will be divided into credits of approximately 10 hours of study time. You will need to complete 120 credits per year, which are broken down into modules of typically 30 credits.

Where will I study?

You will be studying at our Hendon Campus in north London.


Whether you are studying full or part-time – your course timetable will balance your study commitments on campus with time for work, life commitments and independent study.

We aim to make timetables available to students at least 2 weeks before the start of term. Some weeks are different due to how we schedule classes and arrange on-campus sessions.

Typical weekly breakdown

During your first year, your weekly timetable will typically consist of:

  • 5 hours of lectures
  • 8 hours of seminars.

Independent learning

Outside of teaching hours, you’ll learn independently through self-study which will involve reading articles and books, working on projects, undertaking research, and preparing for assessments including coursework, presentations and exams.

Teaching vs independent learning

Here is an indication of how you will split your time:

Year 1



Typical activity



Teaching, learning and assessment



Independent learning

Year 2



Typical activity



Teaching, learning and assessment



Independent learning

Year 3



Typical activity



Teaching, learning and assessment



Independent learning

Academic support

Our excellent teaching and support teams will help you develop the skills relevant to your degree from research and practical skills to critical thinking. Our Sheppard Library is open 24 hours a day during term time. And we offer free 24-hour laptop loans with full desktop software, free printing and Wi-Fi to use on or off campus, even over the weekend.

Coursework and exams

The practical nature of this course means it is based on 100% on project and coursework coursework. assessed. There are no end-of-year exams.


We'll test your understanding and progress with informal and formal tests.

The informal tests usually take place at least once per module, from which you’ll receive feedback from your tutor. The grades from these tests don’t count towards your final marks.

There are formal assessments for each module, usually at the end, which will count towards your module and your final marks.

Assessments are reviewed annually and may be updated based on student feedback or feedback from an external examiner.


To help you achieve the best results, we will provide regular feedback.

Student support

We offer lots of support to help you while you're studying including financial advice, wellbeing, mental health, and disability support.

Additional needs

We'll support you if you have additional needs such as sensory impairment or dyslexia. And if you want to find out whether Middlesex is the right place for you before you apply, get in touch with our Disability and Dyslexia team.


Our specialist teams will support your mental health. We have free individual counselling sessions, workshops, support groups and useful guides.

Work while you study

Our Middlesex Unitemps branch will help you find work that fits around uni and your other commitments. We have hundreds of student jobs on campus that pay the London Living Wage and above. Visit the Middlesex Unitemps page.

Financial support

You can apply for scholarships and bursaries and our MDX Student Starter Kit to help with up to £1,000 of goods, including a new laptop or iPad.

We have also reduced the costs of studying with free laptop loans, free learning resources and discounts to save money on everyday things. Check out our guide to student life on a budget.

How can the BSc Mathematics and Data Science support your career?

Data scientist is in the top ten of emerging jobs according to the LinkedIn emerging jobs report.

Graduates that can combine their mathematical skills and statistical modelling to make sense of big data are in high demand in many sectors, including business and finance where focused product marketing and matching is state-of-the-art. London is still the best place for emerging roles in data science.

Graduate job roles

As a mathematics and data science graduate, you can find employment in any number of different careers including IT, finance and teaching.

Graduate employers

Previous graduates have gone on to such places as Swiss Re and Norton Rose Fulbright.


Our employability service, MDXworks will launch you into the world of work from the beginning of your course, with placements, projects and networking opportunities through our 1000+ links with industry and big-name employers in London and globally.

Our dedicated lifetime career support, like our business start-up support programme and funding for entrepreneurs, has been recognized with the following awards:

The top 20 UK universities for business leaders and entrepreneurs – Business Money, 2023 

A top 10 university for producing CEOs  – Novuana, 2023

Global network

You’ll study with students from 122 countries who’ll hopefully become part of your global network. And after you graduate, we'll support you through our alumni network to help you progress in your chosen career.

Work placements

Placements and internships greatly improve graduate employment prospects, and those who take part achieve excellent academic results through applying their learning in a professional setting.

There is the option to do a year-long placement between the second and final years of study on this course. You'll work with our MDXWorks service to gain placements.

Our specialist employability service will help you find placement opportunities.

  1. UK entry
  2. International entry
  3. How to apply

The fees below are for the 2024/25 academic year.

UK students1

Full-time: £9,250

Part-time: £77 per taught credit

International students2

Full-time students: £16,600

Part-time students: £138 per taught credit

Additional costs

The following study tools are included in your fees:

  • Free loan of iPad and Apple Pencil for the duration of your degree
  • Free laptop loans for a maximum of 24 hours
  • Free access to everything on your reading list
  • Free specialist software for your course
  • Free printing for academic paperwork
  • Free online training with LinkedIn Learning.

The following course-related costs are not included in the fees, and you will need to budget for these:

Scholarships and bursaries

To help make uni affordable, we do everything we can to support you including our:

  • MDX Excellence Scholarship offers grants of up to £2,000 per year for UK students
  • Regional or International Merit Awards which reward International students with up to £2,000 towards course fees
  • Our MDX Student Starter Kit to help with up to £1,000 of goods, including a new laptop or iPad.

Find out more about undergraduate funding and all of our scholarships and bursaries.

Fees disclaimers

1. UK fees: The university reserves the right to increase undergraduate tuition fees in line with changes to legislation, regulation and any government guidance or decisions. The tuition fees for part-time UK study are subject to annual review and we reserve the right to increase the fees each academic year by no more than the level of inflation.

2. International fees: Tuition fees are subject to annual review and we reserve the right to increase the fees each academic year by no more than the level of inflation.

Any annual increase in tuition fees as provided for above will be notified to students at the earliest opportunity in advance of the academic year to which any applicable inflationary rise may apply.

Dr Brendan Masterson
Programme Leader and Lecturer in Mathematics

Dr Masterson studied a joint BA (Hons) in mathematics and psychology, a masters in mathematics, and a PhD at the National University of Ireland, Galway. He works in computational group theory and representation theory.

Dr Alison Megeney
Faculty Head of Learning, Teaching and Student Experience and Associate Professor in Mathematics

Dr. Megeney studied undergraduate mathematics, a masters in stochastic processes, achieving a distinction, and a PhD at University College London. She worked on packing and covering theorems in higher dimensions for her PhD; she has since worked in mathematics education and is interested in the interaction of mathematics and art.

Dr Thomas Bending
Director of Programmes for Mathematics, Statistics, and Aviation and Associate Professor in Mathematics

Dr Bending studied mathematics at Cambridge University, achieving an MA and a distinction in Part III before studying for a PhD at Queen Mary and Westfield College, London. Thomas works in combinatorics, graph theory, and finite geometries.

We’ll carefully manage any future changes to courses, or the support and other services available to you, if these are necessary because of things like changes to government health and safety advice, or any changes to the law.

Any decisions will be taken in line with both external advice and the University’s Regulations which include information on this.

Our priority will always be to maintain academic standards and quality so that your learning outcomes are not affected by any adjustments that we may have to make.

At all times we’ll aim to keep you well informed of how we may need to respond to changing circumstances, and about support that we’ll provide to you.

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