Why study this course?
Gain skills in demand by global, commercial, financial and research institutions.
This course covers a mix of practical and theoretical topics essential to careers in many data-driven sectors. You will learn how to approach real-world data problems and to apply your skills in critical thinking, problem solving, and analysis.
The Data Science course is an online self-paced programme, with options to study for MSc, PGCert and PGDip.
- Study research methods in data science and understand contemporary issues in the field
- Discover methods of datamining, from the underlying core theory to practical understanding
- Learn how to create compelling information visualisations and how to verify visual displays of data
- Use industry-standard computing resources to employ the full data science workflow from data acquisition and processing, through model development and selection, to final deployment and maintenance
- Study optimisation techniques, how to curate and use large quantities of data, and how to model and simulate complex systems of data
Teaching
Lectures, seminars, tutorials and practical work.
Assessment
A mix of coursework exercises, presentations and tests.
Dissertation project
MSc students will submit a software artefact and a detailed description of its context in the area of study.
Schedule
You will access modules and components at a pace and on a timetable that suits your work and study environment.
Modules
Those studying towards an MSc take the compulsory modules and one optional module.
Those studying for a PGCert take four modules, while those studying for a PGDip take eight modules.
For more details, including weekly contact hours, teaching methods and assessment, please see the module catalogue. The modules are examples from previous academic years and may be subject to change before you start your course.
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Compulsory modules
- Complex systems modelling and simulation: introduces a range of techniques and their applications to different classes of problems, with a practical focus on modern network-based models and simulation.
- Data and Information Visualisation: focuses on the question of how to utilise visual representations to make information accessible for exploration and analysis.
- Data-Driven Systems: is an advanced research-focused module that presents the foundations of distributed systems and techniques that process data.
- Discrete Optimisation: covers the theory, tools and technologies developed and used to solve problems in Integer Programming and Combinatorial Optimisation.
- End-to-End Machine Learning: focuses on using python packages to perform end-to-end data-driven analyses.
- Machine Learning Algorithms: covers the essential theory and algorithms, including mathematical foundations, and methodological approaches, using a variety of regression, classification and unsupervised approaches.
- Programming in Python: introduces and revises modelling, design and implementation in Python.
Optional modules
- Numeric Optimisation: takes linear algebra and optimisation as the primary topics of interest and solutions to machine learning problems as the applications of this of the resulting tools, techniques and algorithms.
- Research methods in Data Science: introduces the skills necessary for the planning, data gathering, data analysis and dissemination stages of Data Science research.
Dissertation project
In addition, students will submit a dissertation in Data Science, comprising of a detailed software artefact that implements and evaluates a workflow and a detailed description of the artefact and its context in the area of study. This module involves regular one-to-one contact with the Academic Supervisor.
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- Complex systems modelling and simulation: introduces a range of techniques and their applications to different classes of problems, with a practical focus on modern network-based models and simulation.
- Data and Information Visualisation: focuses on the question of how to utilise visual representations to make information accessible for exploration and analysis.
- Data-Driven Systems: is an advanced research-focused module that presents the foundations of distributed systems and techniques that process data.
- Discrete Optimisation: covers the theory, tools and technologies developed and used to solve problems in Integer Programming and Combinatorial Optimisation.
- End-to-End Machine Learning: focuses on using python packages to perform end-to-end data-driven analyses.
- Machine Learning Algorithms: covers the essential theory and algorithms, including mathematical foundations, and methodological approaches, using a variety of regression, classification and unsupervised approaches.
- Numeric Optimization: takes linear algebra and optimization as the primary topics of interest and solutions to machine learning problems as the applications of this of the resulting tools, techniques and algorithms.
- Programming in Python: introduces and revises modelling, design and implementation in Python.
- Research methods in Data Science: introduces the skills necessary for the planning, data gathering, data analysis and dissemination stages of Data Science research.
What it will lead to
Careers
Data Science is one of the world's fastest growing job types. Qualified data scientists are highly sought after in a wide range of sectors, including business and finance, intelligence services, cybersecurity, healthcare, conservation, the arts, and more.
We are committed to supporting your career aspirations, whatever stage your career is at. We offer:
- Access to our extensive global alumni community for advice and mentoring
- One-to-one guidance covering everything from career choice to application support and interview coaching
- Employer connections, global vacancies, and practical experiences
- Academic and professional skills development
Elevate your career
Graduates from the School of Computer Science work in a wide variety of data roles, including:
- Lloyds Banking Group
- Microsoft
- Securities and Futures Commission (Hong Kong)
- UK Civil Service
Further your education
Many graduates of the School of Computer Science continue their education by enrolling in PhD programmes at St Andrews.
Go your own way
Our Entrepreneurship Centre offers training and start-up support, gives you access to expert mentors and an investor network, as well as one-to-one advice to help you realise your commercial potential.
Why St Andrews?
Wherever you are, you can take St Andrews with you. Online Masters at the University of St Andrews combine all the benefits of studying at one of the world's oldest and best universities, with all the advantages of flexible, personalised learning.
Alumni
Whether you join us online or in person, when you graduate you become a member of the University's worldwide alumni community. Benefit from access to alumni clubs, the Saint Connect networking and mentoring platform, and careers support.
Entry requirements
For entry onto the MSc: A 2.1 undergraduate Honours degree in any subject from the UK or the equivalent international qualification. If you studied your first degree outside the UK, see the English language tests and qualifications.
Application requirements
- CV that includes your personal details with a history of your education and employment to date
- personal statement
- two original signed references on headed paper
- academic transcripts and degree certificates
If you do not already have a degree, you will be required to provide a 500-word (minimum) statement detailing your professional learning and experience.
For more guidance, see supporting documents and references for postgraduate taught programmes.
English language proficiency
If English is not your first language, you may need to provide an English language test score to evidence your English language ability. See approved English language tests and scores for this course.
Fees and funding
MSc: £18,000
PGDip: £12,000
PGCert: £6,000
Scholarships and funding
We are committed to supporting you through your studies, regardless of your financial circumstances.
Successful entrants starting online studies at Masters level can apply for scholarships of up to £6000 towards the course fees.
- St Leonard's funding opportunities
- Graduate discount (15% off tuition fees)
Start your journey
Legal notices
Admission to the University of St Andrews is governed by our Admissions policy
Information about all programmes from previous years of entry can be found in the course archive.
Curriculum development
As a research intensive institution, the University ensures that its teaching references the research interests of its staff, which may change from time to time. As a result, programmes are regularly reviewed with the aim of enhancing students' learning experience. Our approach to course revision is described online.
Tuition fees
The University will clarify compulsory fees and charges it requires any student to pay at the time of offer. The offer will also clarify conditions for any variation of fees. The University’s approach to fee setting is described online.
Page last updated: 3 April 2025