Mathematics and Data Science BSc Honours

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.

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 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

Year 2

Year 3

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.

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.