98% of our postgraduate leavers are in employment or further study within six months of graduating (Destinations of Leavers from Higher Education survey 2015/16).
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.
Our MSc Mathematics and Data Science is one of the first courses to link the two key areas of mathematics and data science, making it uniquely positioned to help our graduates meet this demand.
The course will provide you with a solid foundation in the mathematical analysis of data-driven systems and help you develop your computing skills 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 MSc combines the technology from Big Data and analytics and will provide you with a practical application in financial, business and medical systems, as well as the tools for studying data networks.
The course covers:
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:
Studying for a degree means learning in different ways; managing your own time; conducting research; mastering new computer skills. We have the facilities and advice on hand to help you do all this - and do it well.
Of the many reasons students come to Stirling, such as academic reputation and research standards, one factor is always cited: the outstanding beauty of the University's Stirling campus. View our online films to get a picture of what it's like to live and study on our beautiful campus.
A minimum of a second class Honours degree, or equivalent, in either a mathematics (joint or single honours) or other numerate subject, e.g. physics. Other degrees will also be taken into account, if it can be shown that some mathematical study took place, e.g. taken and passed advanced mathematics modules. Applicants without these formal qualifications but with significant and relevant work experience are encouraged to apply.
If English is not your first language you must have one of the following qualifications as evidence of your English language skills:
For more information, see our English language requirements
If you don’t meet the required score you may be able to register for one of our pre-sessional English courses. To register you must hold a conditional offer for your course and have an IELTS score 0.5 or 1.0 below the required standard. View our range of pre-sessional courses.
If you are interested in studying a module from this course, the Postgraduate Certificate or the Postgraduate Diploma then please email firstname.lastname@example.org to discuss your course of study.
Please apply online from this page. There's no closing date for applications to the course. However, applicants should note that it’s not usually possible to complete the application process in under two weeks, so applications received after the start of September will usually be considered for entry the following year. International applicants are advised to start the application process early as visa formalities can take a number of months to complete.
Dr Andrew Hoyle Computing Science and Mathematics
University of Stirling
FK9 4LA01786 email@example.com
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The MSc Mathematics and Data Science is taught by Computing, Science and Mathematics in the Faculty of Natural Sciences. You'll learn from lectures and practical labs during the first two semesters from September to April. After the spring exams, you'll spend the summer carrying out a dissertation project. The project may be of your own design, or (where possible) as part of a placement or consultancy project for a company.
Here’s an overview of the subjects you'll learn about on the course:
This will introduce the methods used to analyse small and large data sets, the problems with data sets, and ways to maximise the value of 'messy' or partial data sets.
The aim is to introduce the theory and practice of mathematical network science. Topics will include modelling of complex networks and network processes, identifying and calculating properties of networks, and recognising and using networks to understand and interpret data.
This will use mathematics and data analysis to look at how we can translate real-life data into mathematical models. This will include case studies from financial markets, and show how (e.g. medical) data can be translated into mathematical models and how these in turn are used to predict diagnosis and treatment protocols.
Here you'll learn and understand how to manipulate data using tools such as Python, and different data representations such as JSON and HTML.
This will use statistical techniques, such as Bayesian methods and MCMC, to parameterise mathematical models from data. It will then expand onto more general optimisation processes, studying both mathematical optimisation techniques and computational methods.
This module teaches methods for applying data mining, machine learning and analytical techniques to industrial and commercial problems. The mathematical principles behind a number of techniques are presented but the focus is on the practical application of those techniques.
Here you'll identify the differences between a relational database and a non-relational database; how to choose a suitable database for an application, and how to program databases to store and retrieve data and perform aggregation functions
You'll learn about the need for distributing processing across a cluster of computers to carry out Big Data analysis. Also, you will cover the difference between distributed data processing and distributed computation, and the practicalities of using Hadoop and Condor.
In REF2014 Stirling was placed 6th in Scotland and 45th in the UK with almost three quarters of research activity rated either world-leading or internationally excellent.
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In their "The Class of 2017 Job Outlook Report", recruitment agency iCIMS Inc. said: "Working with Big Data (Data Science) requires experts who have a mathematical and computing background, people who are already in short supply".
A need has also been identified for Data Science courses across all disciplines that come into contact with mathematics and statistics.
Employment rates among our Data Science students are very high and careers are varied. Students are working in banks, the NHS, marketing companies, the oil industry and in government. In addition, our Mathematics graduates often go on to work in the financial sector, with many either in Edinburgh or London, Logistics, engineering and various government bodies.
This course is one of the first to link two very important subjects: mathematics and data science. Mathematics has always been a key subject, in great demand in both academia and industry. Data Science is a rapidly growing field, where it's predicted there will be a 160% increase in jobs between 2013 and 2020.
Our new MSc Mathematics and Data Science was designed with employability firmly in mind. This has shaped the modules and how the course is assessed.
Here at Stirling, you'll be offered workshops on CV writing, interview technique and how to secure that dream job. Our relationship with employers and recruiters is strong and they regularly attend events at the University with the intention of recruiting staff.