Overview
Big Data skills are in high demand and they attract high salaries. If you want to become a data scientist, this is the course for you. The MSc Big Data at the University of Stirling is a taught advanced Masters degree covering coding skills, the technology of Big Data and the science of data analytics.
The syllabus includes:
- Mathematics and Statistics for Big Data
- Python scripting
- business and scientific applications of Big Data
- big databases and NoSQL including MongoDB, Cassandra and Neo4J
- analytics, machine learning and data visualisation using Python, Orange and ScikitLearn
- cluster computing with Hadoop, Spark, Hive and MapReduce
- student projects including possible internships with companies or technology providers
This is a 100% online course which gives you the opportunity to study in a way that suits your personal and professional needs. A combination of video lectures, live sessions and online materials are used to teach this course enabling you to access them whenever is the right time for you.
Scotland is a growing and dynamic country with an exciting future at the heart of the data science revolution. £661 million is being invested to turn Edinburgh into the ‘Data Capital of Europe’ (Source: Scottish Development International).
Top reasons to study with us
Course objectives
On successful completion of this programme, you should be able to demonstrate:
- an understanding of the issues of scalability of databases, data analysis, and search;
- 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;
- 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.
Work placements
There are opportunities for students to carry out their three month summer dissertation project as a placement with a company or technology provider. Students who are currently in work are supported in finding a project with their current employer if they wish to do so.
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.
Entry requirements
Academic requirements
A minimum of a second class honours degree (2.1 preferred) or equivalent in a numerate subject such as maths, computing, engineering or an analytic science. Applicants without these formal
International entry requirements
English language requirements
If English is not your first language you must have one of the following qualifications as evidence of your English language skills:
- IELTS Academic or UKVI 6.0 with a minimum of 5.5 in each sub-skill.
- Pearson Test of English (Academic) 60 overall with a minimum of 59 in each sub-skill.
- IBT TOEFL 78 overall with a minimum of 17 in listening, 18 in reading, 20 in speaking and 17 in writing.
See our information on English language requirements for more details on the language tests we accept and options to waive these requirements.
Course details
Students studying on this course will be introduced Big Data concepts such as following.
Representing and Manipulating Data
This course provides an introduction to coding in Python and then covers the important data manipulation packages such as NumPy, Pandas, NLTK, OpenCV and Seaborn.
Mathematical Foundations
This course provides the basic mathematical tools required by any data scientist. It covers basic linear algebra, probability and set theory.
Statistics With R
This course provides students an introduction to statistics using the programming language, R. Students will learn basic statistical methods used to analyse quantitative data.
Relational and Non-Relational Databases
This course takes you through relational database design and SQL programming and 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 data in a commercial system.
Personal and Professional Development
The Personal and Professional Development (PPD) module on the Programme is designed to facilitate your self-awareness, communication, critical thinking, and team-working competencies that are vital to becoming an effective and resilient manager within the field of Data Science.
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 will learn how to apply machine learning in a commercial setting and study the social and ethical considerations when doing so.
Cluster Computing
This course covers distributed data processing on cloud computing systems with Hadoop and MapReduce.
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.
Modules
Course Details
Teaching
How you'll learn online
You’ll learn online through a combination of video lectures, live webinars and course materials that are easy to access wherever you are, whenever it works for you. Our intuitive learning hub also offers collaborative tools that make it easy to connect with tutors and fellow students, creating a rich online environment built around human-centred learning. You'll also have access to an extensive database of eBooks and journals, and free software such as Office 365.
Course project
You’ll carry out a project using a Big Data technology of your choice. With support from our staff, you’ll choose a specialist topic and become a real expert. You'll start with an in-depth 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 salary job.
Assessment
Assignments will vary from module to module, with several technical assignments and some essay writing.
Course director
Fees and funding
Fees and costs
| 2024/25 | 2025/26 | |
|---|---|---|
| Students from the UK and Republic of Ireland | £11,025 | £12,100 |
| International (including EU) students | £11,025 | £12,100 |
Fees shown are for a full-time, one-year Masters course.
If you need to extend your period of study, you may be liable for additional fees.
If you are studying part time, the total course fee will be split over the years that you study. The total fee will remain the same and will be held at the rate set in your year of entry.
For more information on courses invoiced on an annual fee basis, please read our tuition fee policy.
Fees shown are for a full-time, one-year Masters course.
If you need to extend your period of study, you may be liable for additional fees.
If you are studying part time, the total course fee will be split over the years that you study. The total fee will remain the same and will be held at the rate set in your year of entry.
For more information on courses invoiced on an annual fee basis, please read our tuition fee policy.
Postgraduate tuition fee loans
This course is eligible for a postgraduate tuition fee loan from one of the UK’s governments. See the Scholarships and funding section, below, for more details.
Additional costs
There are some instances where additional fees may apply. Depending on your chosen course, you may need to pay additional costs, for example for field trips. Learn more about additional fees.
Scholarships and funding
Postgraduate tuition fee loans
Scottish students may be eligible to apply to the Students Award Agency for Scotland (SAAS) for loans of up to £11,500 to cover tuition fees and associated living costs.
English students can apply for a loan of up to £12,167 each year as part of the Postgraduate Masters Loan Scheme.
Welsh students can apply for financial support of up to £18,770 as a combination of grant and loan from Student Finance Wales.
Northern Irish students can apply for a postgraduate tuition fee loan of up to £6,500 from Student Finance NI.
International students may be able to gain additional funding from loan providers.
Payment options
We aim to be as flexible as possible, and offer a wide range of payment methods - including the option to pay fees by instalments. Learn more about how to pay
After you graduate
Big data skills are in high demand. You will have opportunities with data-driven companies from a wide variety of sectors and command a salary that’s typically higher than the IT average. Students graduating from this programme have found jobs everywhere from global companies to small startups. Here are a few of the sectors where our graduates now work:
- banking and finance
- marketing
- data consultancy
- healthcare
- sport
- education
Our Masters in Big Data has been developed in partnership with companies that employ data scientists and includes the possibility of an internship that will enable you to make industry connections that could benefit your future career.
We’re part of the DataLab MSc, which supports our students with funding, networking and routes into employment.
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