Mathematics for Data Science

About this programme

Data analytics (Big Data) is a major phenomenon in the 21st century, there is an increasing demand for data analysts trained in this area who can collate, interpret and draw value from complex data sets. This programme brings together a range of techniques that modern data analyst needs. You 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 your second year, you 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 you will be able to build your own deep learning neural net, and be able to explain the function of every part of the algorithm. This last aspect of being able to explain the software’s function is key to the role of a mathematician as an understander as well as a user of methods, as opposed to just a consumer of software. The em