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.
Course details
The Data Science course is an online self-paced programme, with options to study for PGCert, PGDip and MSc.
Highlights
- The programme will teach research methods in data science and help you to understand contemporary issues in the field.
- You will discover methods of datamining, from the underlying core theory to practical understanding.
- The programme will help you to understand how to create effective information visualisations and how to engage critically with visual displays of data.
- You will 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.
- You will learn optimisation techniques, how to curate and utilise large quantities of data, and how to model and simulate complex systems of data.
Modules
The modules published below are examples of what has been taught in previous academic years and may be subject to change before you start your course. For more details of each module, including weekly contact hours, teaching methods and assessment, please see the module catalogue.
Students studying towards an MSc take the following compulsory modules and one optional module.
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.
Students studying for a PGCert take four modules from the following, those studying for a PGDip take eight modules from the following.
- 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.
Teaching
Teaching methods include lectures, seminars, tutorials and practical work. A self-led approach is taken, with students accessing modules and components at a pace and a timetable that suits their work and study environment.
Most modules are assessed through coursework exercises, presentations and tests.
Events
The St Andrews Computing Society (STACS) regularly organises hackathons and other events open to local and external participants, including MSc students. These are very popular events, often supported by industrial sponsors.
The Computer Science blog regularly publishes news and events.
Fees
- MSc (three years) £18,000 (charged £6,000 per year of study)
- PGDip (two years) £12,000 (charged £6,000 per year of study)
- PGCert (one year) £6,000
Fees will be charged evenly across each academic year, based on the maximum length of study confirmed for your specific programme. For example, an MSc student will be charged £6,000 per year for a three-year period. Students completing in a shorter length of time will have fees adjusted at relevant points in their programme so that the full fee has been charged prior to completion.
Funding and scholarships
The University of St Andrews is committed to attracting the very best students, regardless of financial circumstances.
15% Recent Graduate Discount
If you have graduated from the University within the last three academic years, you may be eligible for a 15% discount on postgraduate taught tuition fees. Terms and conditions apply.
After your degree
Careers
Alumni of the School of Computer Science have gone on to work in a variety of global, commercial, financial and research institutions, including:
- Amazon
- American Express
- Avaloq
- Barclays Capital
- BP
- Capricorn Ventis
- Hailo
- Hewlett Packard
- Hitachi Data Systems
- Microsoft
- Rockstar
- Royal Bank of Scotland, Tesco Bank, Lloyds
- Skyscanner
- Symantec
- TriSystems
The Careers Centre offers one-to-one advice to all students as well as a programme of events to assist students in building their employability skills.
Further study
Many graduates of the School of Computer Science continue their education by enrolling in PhD programmes at St Andrews.
Postgraduate researchContact us
- Phone
- +44 (0)1334 46 2150
- admissions@st-andrews.ac.uk
- Address
- School of Computer Science
Jack Cole Building
North Haugh
St Andrews
KY16 9SX