Big Data

Study Big Data and graduate with a university degree

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Big Data
  • Type
  • Duration One year
  • Start date September

Kevin Swingler Computing Science and Mathematics
University of Stirling
+44 (0) 1786 467676

This is a one year, full time taught MSc. designed to lead to a job in data science or analytics.

Big Data skills are in high demand and they attract high salaries. The MSc Big Data at the University of Stirling is a taught advanced Master's degree covering the technology of Big Data and the science of data analytics.

The course is taught in the beautiful Stirling campus in the heart of Scotland with support from companies who recruit data scientists.

The course covers Big Data technology, advanced analytics and industrial and scientific applications. The syllabus includes:

  • Mathematics for Big Data
  • Big Data theory and computing foundations
  • Big databases and NoSQL
  • Analytics, machine learning and data visualisation
  • Optimisation and heuristics for big problems
  • Distributed and parallel systems
  • Scientific and commercial applications
  • Student projects

‌Enhance your employability whilst gaining work experience with our Making the Most of Masters initiative.

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Course objectives

  • An understanding of the issues of scalability of databases, data analysis, search and optimisation
  • The ability to choose the right solution for a commercial task involving big data, including databases, architectures and cloud services
  • An understanding of the analysis of big data including methods to visualise and automatically learn from vast quantities of data
  • An appreciation of the size of search spaces in large problems and the ability to choose an appropriate heuristic to find a near optimal solution
  • The programming skills to build simple solutions using big data technologies such as MapReduce and scripting for NoSQL, and the ability to write parallel algorithms for multi processor execution.

Entrance requirements

A minimum of a second class Honours degree or equivalent in a numerate subject such as maths, computing, engineering or an analytic science. Applicants without these formal qualifications but with significant appropriate work experience are encouraged to apply. 

English language requirements

If English is not your first language, you must provide evidence of your proficiency such as a minimum IELTS score of 6.0 (5.5 in all bands).

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.

Our range of pre-sessional courses.

Course start date


Application procedure

Entry to the MSc is at the start of the Autumn Semester in mid-September. There are no closing dates for applications to the course, but applicants should note it is not usually possible to complete the application formalities in under two weeks, so applications received after the start of September each year will usually be considered for entry the following year. International applicants are advised that visa formalities can take a number of months and hence a timely application is important.

Please click on the 'Apply Now' button at the right hand side to apply.

Structure and content

Our Big Data MSc is a mix of practical technology such as Hadoop, NoSQL, and Map-Reduce, important maths and computing theory, and advanced computational techniques. The course will teach you what you need to know to collect, manage and analyse big, fast moving data for science or commerce.

Foundation Maths and Computing

Our foundation maths and computing courses make sure you have the theoretical grounding to build on for the rest of the course.

Big Databases

After a recap of SQL, this course takes you through the various NoSQL databases such as document stores like MongoDB, column stores like Cassandra and graph databases like Neo4j. You'll learn to pick the right database for your application and how to build, search and distribute the data in them.

Big Data Analytics

Sometimes, the more data you have, the better hidden the important facts become. Distilling information from big data needs fast, parallel analytics. We guide you through machine learning, data visualisation, web analytics and sentiment analysis. You'll learn the practicalities of big data analytics with techniques from data mining, machine learning, statistics, data visualisation and web analytics. Learn how we are training computers to understand the present and predict the future with data from finance, marketing, and social media.

Parallel and Distributed Systems

Large scale systems need distributed resources. Databases need to be partitioned or sharded across many servers and programs need to run in parallel across many machines. This is where you will master Map-Reduce, learn to duplicate jobs with Condor and play with our Raspberry Pi cloud.

Heuristics for Big Problems

Many big problems from scheduling a large airport to routing a fleet of trucks cannot be solved to perfection. This course covers computational heuristics for function optimisation where the search space is far too large to search exhaustively.

Scientific and Commercial Applications

With guest lectures from science and industry, this course presents a set of case studies of Big Data in action. You'll learn first hand how companies are using big data in fields such as banking, travel, telecoms, genetics and neuroscience.


Each student will carry out a project using a Big Data technology of their choice. With support from our staff you will choose a specialist topic and become a real expert. You'll start with an indepth analysis of the topic and its technology. Then you'll build a solution that will showcase your skills to employers and give you the knowledge to win a high level, high pay job.


This is a practical course and the assessment reflects that. Each module has an assignment and an exam, but the emphasis is on the course work.


Here are some of the technologies you will learn about on our Big Data MSc:


Analytical and problem solving methods you will learn include:
  • Maths and Statistics
    • Probability and likelihood
    • Information theory
    • Linear algebra
    • Hilbert spaces and tensors
  • Data Mining
    • Neural networks
    • Bayesian networks
    • Decision Trees
    • EM Algorithm
  • Optimisation
    • Local search
    • Genetic algorithms
    • Particle swarm optimisation
    • Genetic programming

Why study Big Data at Stirling?

Course Director

Kevin Swingler


Industry and Employer Partnerships

The Stirling MSc in Big Data has been developed in partnership with global and local companies who employ data scientists. HSBC have a development centre in Stirling and have provided some very interesting Big Data projects to our students. Amazon’s development centre in Scotland is close by in Edinburgh, and they often recruit data scientists. The course features a long summer project, generally in partnership with a company or technology provider, that provides students with a showcase of their skills to take to employers or launch online.

Career opportunities

Demand for people with big data skills is projected to grow rapidly in the coming years. Average salaries are higher in Big Data jobs than the IT average and the skills shortage will make that gap bigger.

The Stirling Big Data MSc is run in partnership with industry and is designed to produce graduates with the skills that companies need. 


2015/16 Overseas £13500
2015/16 Home/EU £4500
2014/15 Overseas £12900
2014/15 Home/EU £4000

You should expect to pay fees for every year you are in attendance and be aware fees are subject to revision and may increase annually. Students on programmes of study of more than one year should take this into account when applying.

Tuition Fees for programmes of study starting in 2016/7 have not yet been set please check back here in December 2014.


For information on possible sources of funding, visit: