MSc Mathematics and Data Science
The MSc combines Mathematics with technology from Big Data and analytics, giving you a practical application in financial, business and medical systems, as well as the tools for studying data networks.
Key facts
- Award Masters / MSc, Postgraduate Diploma, Postgraduate Certificate
- Start date September 2021, January 2022
- Duration MSc full-time: 12 months, MSc part-time: 24 months
- Mode of study Part-time, Full-time, Campus based, Stand-alone modules
Changes at Stirling
Find out about important changes including how you'll be taught, start dates and how we're making campus safer.
Overview
The field of Data Science has seen rapid growth in recent years, with vast amounts of data now being generated by major companies and service providers.
At the same time, it’s recognised that there’s a shortage of qualified analysts, both in the UK and globally, to make the most of this data. Crucially, there’s now a shortage of mathematics graduates with the data analysis skills needed to meet the demands of industry.
Yet, there is a recognised shortage of qualified analysts, both in the UK and globally, to make the most of this data. In particular, the demand is for graduates who can both manage the data (the computing skills), and analyse the data to extract patterns, build models and make predictions (the mathematics skills). It is only with these analytical skills can the full value of data be extracted.
The COVID-19 pandemic has shown the importance of combining Data Science and Mathematics, with
The course will provide you with a strong foundation in the mathematical analysis of data-driven systems and help you develop your computing skills, including Artificial Intelligence and Machine Learning, to apply the techniques you learn on a large scale. You’ll learn the techniques used to approach data using computational analysis and understand the mathematics underpinning these techniques. The key outcome of this course is to ensure you are confident in your skills before you contunue your career.
Stirling is a member of The Data Lab, an Innovation Centre that aims to develop the data science talent and skills required by industry in Scotland. It facilitates industry involvement and collaboration, and provides funding and resources for students.
Course objectives
The aim of the course is to teach you the techniques for approaching data sets using computational analysis, and to help you understand the mathematics underpinning these techniques.
On successful completion of the MSc Mathematics and Data Science, you'll be able to:
- analyse and solve real-world problems using different mathematical and computational approaches
- expand mathematical methods to approach more complex problems
- be competant in industry-relevent programming languages, including R, Python and Matlab
- demonstrate skills in data analytics, machine learning and Artificial Intelligence (AI)
- analyse small and large-scale data sets using mathematical and computational approaches
- be confident in your own research and data science skills through real-life based projects
Work placements
The course features a long summer project, generally in partnership with a company or technology provider.
Flexible learning
If you’re interested in studying a module from this course, the Postgraduate Certificate or the Postgraduate Diploma then please email Graduate Admissions to discuss your course of study.
Research overview
Our team of academics in the department of Computing Science and Mathematics investigate novel and effective approaches to dynamic and uncertain real-world problems in complex systems and environments. They explore the interdisciplinary connections between computer science, mathematics, life sciences, social sciences and management.
We work collaboratively with a number of organisations, including The Data Lab (Scotland’s Data Science Innovation Centre, which supports students with funding, networking and routes to employment) and the Scottish Informatics and Computing Science Alliance (SICSA), to ensure our students have the best platform to succeed.
- 90% of our research is 'Internationally Excellent' with top 5% judged as 'World-leading' (Research Excellence Framework (REF) 2014)