Bachelor of Science (Honours) - Mathematics for Data Science (Placement Year) (G1NK)

This programme brings together a range of techniques that modern data analyst needs. Students will study blocks in mathematics, statistics, data analysis and computing, and tackle a variety of interesting and engaging problems from business and industry. A good grounding in all these subjects is essential for creating and using algorithms and systems that identify patterns and extract value from masses of data.

The course will also develop key graduate skills such as problem-solving and communication, with a third of the credits at each level based on project-oriented work where students will develop their knowledge, professionalism and creativity in a supportive environment.

As an example, in the second year, students will be introduced to neural networks and deep learning. This important topic is at heart a powerful blend of linear algebra, nonlinear activation functions, vector calculus chain rule for gradients, and steepest descent optimisation with sampling. These fundamental building blocks will be brought together in theory and in software so that they will be able to build their own deep learning neural net, and be able to explain the function of every part of the algorithm. The emphasis throughout will be on the practical rigour associated with getting deep learning to work.

Follow the four-year ‘Professional Placement’ degree programme and students will benefit from their extensive experience in helping students to find well-paid work placements with blue-chip companies. Brunel's sandwich students find that their mathematical and transferable skills are in demand in many sectors.