MSc Big Data

Big data is increasingly important in today’s commercial landscape. As a data scientist specialising in big data, you’ll help companies make sense of large amounts of structured and unstructured data, providing rapid insights that enable them to make better, quicker decisions.

The MSc Big Data is a taught Masters degree covering the technology of Big Data and the science of data analytics. You’ll gain practical skills in big data technology, advanced analytics and industrial and scientific applications.

The course will teach you how to collect, manage and analyse big, fast moving data for science or commerce. You’ll learn skills in cutting-edge technology such as Data Analytics, R, Hadoop, NoSQL and Machine Learning. At the same time, you’ll delve into important maths and computing theory, and learn the advanced computational techniques you need to develop your career in data science.

Our MSc has been developed in partnership with global and local companies who employ data scientists. Since the course was launched in 2012 we have developed a great relationship with employers who are looking for the skills that we teach.

The University of Stirling is associated with The Data Lab, an Innovation Centre that aims to develop the data science talent and skills required by industry in Scotland. It also supports our students with funding, networking and routes into employment. We also have close links with the Scottish Informatics and Computing Science Alliance (SICSA).

As a graduate in Big Data you’ll be able to work in a wide range of sectors such as digital technologies, energy and utilities, financial services, public sector and healthcare. The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018 (Statista.com).

Scotland is a growing and dynamic country with an exciting future at the heart of the data science revolution. £661 million invested in vision of turning capital city into the ‘Data Capital of Europe’ (Source: Scottish Development International, 2020).

Course structure

Mathematical and Statistical Foundations

This course will equip students with some basic mathematical knowledge and problem-solving skills. The course is intended to give students:

• a basis for the analysis and interpretation of quantitative information;
• an understanding of the basic ideas underlying statistical methods at an introductory level;
• an understanding of how to overcome problems when analysing big data sets.

Relational and Non-Relational Databases

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

Machine Learning

• You'll learn the practicalities of big data analytics with techniques from data mining, machine learning, statistics, and data visualisation. You’ll explore how we’re training computers to understand the present and predict the future with data from finance, marketing and social media. You’ll learn how to apply machine learning techniques such as neural networks and decision trees to practical problems.

Cluster Computing

• This course covers distributed data processing with Hadoop and MapReduce in addition to the use of Condor for distributed computation.

Scientific and Commercial Applications

• This course presents a set of case studies of Big Data in action across science and industry. You'll learn how companies are using big data in fields such as banking, travel, telecoms, genetics and neuroscience.

For students interested in a January start, the duration of the course will be 21 months. For example, students starting in January 2023 will graduate in November 2024. This decision was made to allow students to learn flexibly and enhance other skills during the summer months when teaching is not available.