The MSc in Data-Intensive Analysis is an interdisciplinary course providing students with an understanding of how data is used to gain useful insights in all areas of scientific endeavour. The programme has a substantive statistical component – both theory and practice – allied to computational data science and visualisation.
The MSc in Data-Intensive Analysis is a one-year taught programme run collaboratively by the Schools of Computer Science and Mathematics and Statistics. The course consists of two semesters of taught modules followed by an 11-week project leading to the submission of a 15,000-word dissertation in August.
The course develops practical skills in derivation, validation and deployment of predictive models based on collected data, and provides training in the use of industry- and research-standard technologies and techniques.
Students undertake a significant project including a wide-ranging investigation leading to their dissertation, which enables them to consolidate and extend their specialist knowledge and critical thinking.
Students have 24-hour access to modern computing laboratories, provisioned with dual-screen PC workstations and group-working facilities.
The taught part of this MSc programme includes five compulsory modules in statistics and data analysis, plus a choice of two from four modules in Computer Science. Teaching methods include lectures, seminars, tutorials and practical classes. Most modules are assessed through practical coursework exercises and examinations. Class sizes typically range from 10 to 50 students.
All students are assigned an advisor who meets with them at the start of the year to discuss module choices and is available to assist with any academic difficulties during the year. A designated member of staff provides close supervision for the MSc project and dissertation.
The modules in this programme have varying methods of delivery and assessment.
Students take five compulsory modules.
Advanced-Data Analysis: covers modern modelling methods for situations where the data fails to meet the assumptions of common statistical models and simple remedies do not suffice.
Computing in Statistics: introduces and provides experience with the software package SAS and the statistical language and environment R.
Data Analysis: provides coverage of essential statistical concepts, data manipulation and analysis methods, and software skills in commercial analysis packages.
Knowledge Discovery & Data mining: covers many of the methods found under the banner of "Datamining", building from a theoretical perspective but ultimately teaching practical application.
Statistical Modelling: covers the main aspects of linear models and generalized linear models, including model specification, various options for model selection, model assessment and tools for diagnosing model faults.
Students choose two of the following optional modules.
Information Visualisation: explores how to utilise visual representations to make information accessible for exploration and analysis.
Masters Programming Projects: reinforces key programming skills gained during the first programming module of the programme and offers increasing depth and scope for creativity.
Object-Orientated Modelling, Design and Programming
Programming Principles and Practice: introduces computational thinking and problem-solving skills to students who have no or little previous programming experience.
During the second semester, students work with staff to define and agree upon a topic for the extended project, which they will work on during the final three months of the course, and which culminates in a 15,000-word dissertation. Dissertation projects may be group-based or completed individually (students are assessed individually in either case).
The dissertation typically comprises a review of related work; the extension of existing or the development of new ideas; software implementation and testing; analysis and evaluation. Students are required to give a presentation of their work in addition to the written dissertation.
Each project is supervised by one or two members of staff, typically through regular meetings and reviews of software and dissertation drafts.
If students choose not to complete the dissertation requirement for the MSc, there is an exit award available that allows suitably qualified candidates to receive a Postgraduate Diploma instead, finishing the course at the end of the second semester of study.
The modules listed here are indicative, and there is no guarantee they will run for 2019 entry.
Alumni of Computer Science MSc programmes have gone on to work in a variety of global, commercial, financial and research institutions, including:
Hitachi Data Systems
Royal Bank of Scotland
The Careers Centre offers one-to-one advice to all students on a taught postgraduate course and offers a programme of events to assist students in building their employability skills.
A good 2.1 Honours undergraduate degree, plus evidence of some previous programming experience.
If you studied your first degree outside the UK, see the international entry requirements.
English language proficiency.
The qualifications listed are indicative of minimum requirements for entry. Some academic Schools will ask applicants to achieve significantly higher marks than the minimum. Obtaining the listed entry requirements will not guarantee you a place, as the University considers all aspects of every application including, where applicable, the writing sample, personal statement, and supporting documents.
Personal statement (optional).
Two original signed academic references.
Academic transcripts and degree certificates.
Evidence of English language proficiency (required if English is not your first language).
There are many potential scholarships and support schemes available to postgraduates.
Recent Graduate Discount
The University of St Andrews offers a 10% discount in postgraduate tuition fees to students who are eligible to graduate or who have graduated from St Andrews within the last three academic years and are starting a postgraduate programme with the University of St Andrews.