Application deadline
Thursday 10 August 2023 for September 2023 entry
Monday 4 December 2023 for January 2024 entry
Applicants should apply as early as possible.
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 international entry requirements. We will also consider applicants who do not have an undergraduate degree. In these circumstances we expect candidates to have at least five years of relevant professional learning. The Admissions team will holistically assess your application and determine the best route of entry for you. In some cases, this may be onto the PGCert in the first instance where students will have the opportunity to progress and graduate with a full Masters degree. Students are also required to have a desired level of English language proficiency. See English language tests and qualifications.
Application requirements
- CV or resumé
- 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 a PGCert, PGDip and an 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 in this programme have varying methods of delivery and assessment. More details about each module, including weekly contact hours, teaching methods and assessment, will be released later in 2023. If you have any questions about the programme, please get in touch with admissions@st-andrews.ac.uk.
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 Optimization: covers the theory, tools and technologies developed and used to solve problems in Integer Programming and Combinatorial Optimization.
- Knowledge Discovery and Data Mining: covers many of the methods found under the banner of "datamining", building from a theoretical perspective but ultimately teaching practical application.
- 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.
The modules listed here are indicative, and there is no guarantee they will run for 2023 or 2024 entry. Details of modules will be added to the module catalogue in due course.
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 Optimization: covers the theory, tools and technologies developed and used to solve problems in Integer Programming and Combinatorial Optimization.
- Knowledge Discovery and Data Mining: covers many of the methods found under the banner of "datamining", building from a theoretical perspective but ultimately teaching practical application.
- 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 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.
- 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.
The modules listed here are indicative, and there is no guarantee they will run for 2023 or 2024 entry. Details of modules will be added to the module catalogue in due course.
The modules listed here are indicative, and there is no guarantee they will run for 2024 entry. Take a look at the most up-to-date modules in the module catalogue.
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)
- PG Dip (two years) £12,000 (charged £6,000 per year of study)
- PG Cert (one year) £6,000
Fees will be charged annually, based on the maximum length of study confirmed for your specific programme. Students completing in a shorter length of time may 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.
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 researchWhat to do next
Online information events
Join us for one of our information events where you can find out about different levels of study and specific courses we run. There are also sessions available for parents and college counsellors.
Postgraduate virtual days
We encourage all students who are thinking of applying to the University to attend one of our online visiting days.
Contact us
- Phone
- +44 (0)1334 46 2344
- admissions@st-andrews.ac.uk
- Address
- School of Computer Science
Jack Cole Building
North Haugh
St Andrews
KY16 9SX