The Data Analytics MSc will teach you the core mathematical principles of data analysis and how to apply these to real world scenarios. Building on the statistical foundations of machine learning you’ll then choose from module options which explore the financial, business and scientific applications; such as in trading and risk systems, optimisation of business processes, and relationships across complex systems.
Data science is the driving force behind today’s most successful businesses. In our data-driven economy, companies are seeking highly numerate data experts who can use statistical techniques and the latest technologies to extract clear insights to inform every aspect of their strategy and operations. From financial corporations, to AI start-ups, and across the technology, retail and healthcare industries, highly sought-after Data Scientists can earn over £56k per year on average (according to Indeed.co.uk, UK figures).
We’re looking for students with an interest in problem solving and some understanding of probability or statistics. You don’t need to be a programming expert before you join us; you’ll get to discover a variety of industry-standard tools (such as R and Python) to allow you to choose which technologies you want to specialise in.
You’ll be taught by our expert academics, who include former industry practitioners with many years’ experience. Many of them are Fellows of the Alan Turing Institute and members of Queen Mary’s Institute of Applied Data Science.
Over the summer you will work on a research project in an area of interest, developing strong applied data science research skills and putting your learning into practice. You may choose to collaborate with one of our industry partners on a real-world data problem.
We offer £3000 Global Excellence scholarships to top overseas applicants for 2019 entry. Find our more on our scholarships webpages.
This MSc programme is directed by Dr Primoz Skraba, Senior Lecturer in Applied and Computational Topology. Primoz has a PhD in Electrical Engineering from Stanford University, and his current research interests are focused on topological data analysis. This includes: