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. Studying BSc Mathematics and Data Science with us will provide a platform for you to enter this important sector. Additionally, as a Mathematics graduate you can find employment in any number of different careers including IT, finance and teaching.
We believe strongly that the work you do must be relevant to the world of work – that's why our course has a strong practical slant. The core focus of your degree will be understanding the mathematical theory underpinning data science and learning within a practical setting to develop key skills for future employment.
This degree offers an opportunity to obtain practical real-life experience of working and analysing big data, in addition to being taught and supported by staff that work and research in all areas of mathematics, with expert knowledge from the industry.
You’ll build on theory to deliver practical solutions to a variety of real-world big data problems. 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.
Throughout your degree there will be scope to develop your programming and software skills, as well as learn new skills within a work environment through placement opportunities.
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Your studies will focus on probability and statistics, data analysis and the theory that underpins this. You’ll build your Mathematics knowledge for machine learning and artificial intelligence. You’ll also learn programming and software development.
You’ll learn to work with multi-faceted problems across a range of modules and develop your mathematical problem solving and communication skills. You will gain a comprehensive understanding of data wrangling, data analysis and broaden your knowledge of programming, software design and engineering.
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.
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.
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.
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 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.
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.
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.
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 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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
You can find more information about this course in the programme specification. Optional modules are usually available at levels 5 and 6, although optional modules are not offered on every course. Where optional modules are available, you will be asked to make your choice during the previous academic year. If we have insufficient numbers of students interested in an optional module, or there are staffing changes which affect the teaching, it may not be offered. If an optional module will not run, we will advise you after the module selection period when numbers are confirmed, or at the earliest time that the programme team make the decision not to run the module, and help you choose an alternative module.
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.
The practical nature of this programme means it is assessed entirely through project work and coursework. There are no end-of-year exams on this programme.
We are back on campus for the majority of teaching in Autumn 2021, as long as restrictions allow. Your timetable will be built around on campus sessions using our professional facilities, with online sessions for some activities where we know being virtual will add value. We’ll use technology to enhance all of your learning and give you access to online resources to use in your own time.
In case of any changes to government guidance, we‘ll be ready to move to teaching with more restrictions in place and continue to give you an excellent learning experience. In this scenario, on campus teaching should continue although more of your course will take place online.
The table below gives you an idea of what your learning will look like across a typical week. Some weeks might be different due to how we schedule classes and arrange on campus sessions.
This information may change slightly as we receive further guidance from the government. You’ll receive final arrangements for your teaching and a full course timetable before you start.
Scenario A: Without social distancing
Live in-person on campus learning
Contact hours per week, per level:
Year 1: 6 hours|
Year 2: 6 hours
Year 3: 6 hours
Live online learning
Average hours per week, per level:
Year 1: 6 hours
Scenario B: With social distancing and/or with restrictions on travel to campus
Live in-person on campus learning
Contact hours per week, per level:
Year 1: 0 hours
Live online learning
Average hours per week, per level:
Year 1: 12 hours
Outside of these hours, you’ll be expected to do independent study where you read, listen and reflect on other learning activities. This can include preparation for future classes. In a year, you’ll typically be expected to commit 1200 hours to your course across all styles of learning. If you are taking a placement, you might have some additional hours.
Read more about our scenarios for returning to campus and what they might mean for your teaching and learning experience, and how you’ll be able to access student support.
Future plans for teaching
We’re developing our plans for in-person on campus teaching following government advice to keep you safe. If more restrictions are put in place in the future, or there is another lockdown, we’ll deliver your learning and support fully online for a temporary period. We’ll make alternative arrangements for any required placements if they can’t go ahead as planned. We’ll always give you notice of any changes that we make.
Definitions of terms
You’ll have a strong support network available to you to make sure you develop all the necessary academic skills you need to do well on your course.
Our support services will be delivered online and on campus and you’ll have access to a range of different resources so you can get the help you need, whether you’re studying at home or have the opportunity to come to campus.
You’ll have access to one to one and group sessions for personal learning and academic support from our library and IT teams, and our network of learning experts. Our teams will also be here to offer financial advice, and personal wellbeing, mental health and disability support.
More on teaching for your subject in 2021/22
Read our guide to what you can look forward to when you study your subject with us including more information about your teaching experience this autumn.
Data Scientists are in high demand in multiple sectors, not least in commerce and finance where focussed product marketing and matching is become the state of the art. London is still the best place for emerging roles in data science.
Previous graduates from Mathematics have gone on to such places as Swiss Re and Norton Rose Fulbright.
Dr. Jones studied undergraduate mathematics at Lancaster University and University of Maryland, College Park, USA, before completing his PhD at University College London. He works in complex analysis on Riemann surfaces, functional analysis and operator theory.
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 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.