Data-Intensive Analysis (MSc) 2020 entry
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.
Course type
Postgraduate, leading to a Master of Science (MSc)
Course dates
- Start date: 7 September 2020
- End date: 30 September 2021
Information about all programmes from previous years of entry can be found in the archive.
Course duration
One year full time or two years part time
Entry requirements
- 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. See English language tests and qualifications.
The qualifications listed are indicative 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.
Tuition fees
UK and EU: £9,450
Overseas: £23,090
Application deadline
Wednesday 12 August 2020. Applicants should apply as early as possible to be eligible for certain scholarships and for international visa purposes. To maintain staff-student ratios, the University reserves the right to stop accepting applications once the programme is full.
Application requirements
- CV
- 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).
For more guidance, see supporting documents and references for postgraduate taught programmes.
Course information
The MSc in Data-Intensive Analysis is a one-year taught programme run collaboratively by the Schools of Mathematics and Statistics and Computer Science. 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.
Highlights
- 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.
Teaching format
The taught part of this MSc programme includes four compulsory modules in statistics and data analysis, plus a choice of a further four modules in Computer Science and Mathematics and Statistics. 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.
Modules
The modules in this programme have varying methods of delivery and assessment. For more details about each module, including weekly contact hours, teaching methods and assessment, please see the latest module catalogue, which is for the 2019–2020 academic year; some elements may be subject to change for 2020 entry.
Students take four compulsory modules.
- Introductory Data Analysis: provides coverage of essential statistical concepts, data manipulation and analysis methods, and software skills in widely used analysis packages.
- 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.
- Knowledge Discovery and Datamining: covers many of the methods found under the banner of "Datamining", building from a theoretical perspective but ultimately teaching practical application.
- Applied Statistical Modelling using GLMs: 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 four of the following optional modules.
- Computing in Statistics: teaches computer programming skills, including principles of good programming practice, with an emphasis on statistical computing.
- Software for Data Analysis: covers the practical aspects of statistical data analysis, focusing on packages most widely used in the commercial sector.
- Data-Intensive Systems: presents the programming paradigms, algorithmic techniques and design principles for large-scale distributed systems, such as those utlised by companies such as Google, Amazon and Facebook.
- Information Visualisation and Visual Analytics: 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: introduces and revised object-oriented modelling, design and implementation to reinforce study in other modules.
- Programming Principles and Practice: introduces computational thinking and problem-solving skills to students who have no or little previous programming experience.
Optional modules are subject to change each year and require a minimum number of participants to be offered; some may only allow limited numbers of students (see the University's position on curriculum development).
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. Supervisors and topics may be from either of the schools of Computer Science or Mathematics and Statistics and many are in collaboration with companies or other external bodies.
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 2020 entry. Take a look at the most up-to-date modules in the module catalogue.
Conferences and events
The School of Computer Science organises a regular programme of colloquia, talks and seminars by external and internal speakers from both industry and academia. The talks are aimed at bringing the diversity, excitement and impact of computer science from around the globe to staff and students within the School.
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.
Funding
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.
After the MSc
Research degrees
In addition to the MSc, the School offers a two-year Master of Philosophy (MPhil) degree option in Data-Intensive Analysis.
The EngD programme in Computer Science is a 4-year Engineering Doctorate involving an industrial partner and incorporating a 30-week taught component and a 170-week individual research component. Students who have already completed an MSc may be able to proceed directly to the individual research component of the EngD.
Many graduates continue their education by enrolling in PhD programmes at St Andrews. The School of Computer Science is highly rated for its theoretical and practical research in areas such as AI, symbolic computation, networking, computer communication systems, human-computer interaction, and systems engineering, and offers research opportunities leading to a PhD in Computer Science.
Careers
Alumni of Computer Science MSc programmes have gone on to work in a variety of global, commercial, financial and research institutions, including:
- Amadeus
- Amazon
- American Express
- Atlas
- Avaloq
- Barclays Capital
- BP
- BT Openreach
- Capricorn Ventis
- FactSet
- Hailo
- Hewlett Packard
- Hitachi Data Systems
- Microsoft
- OpenBet
- Rockstar
- Royal Bank of Scotland, TSB, Bank of Scotland, Tesco Bank, Lloyds
- Sky
- Skyscanner
- Symantec
- TriSystems
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 to build their employability skills.
Contact
School of Mathematics and Statistics
University of St Andrews
Mathematical Institute
North Haugh
St Andrews
KY16 9AL
Phone: +44 (0)1334 46 2344
Email: maths-pgstats@st-andrews.ac.uk
Mathematics and Statistics website
School of Computer Science
University of St Andrews
Jack Cole Building
North Haugh
St Andrews
KY16 9SX
Phone: +44 (0)1334 46 3253
Email: msc-admin-cs@st-andrews.ac.uk
Updated fees
The overseas fees for this programme were previously incorrectly published. The correct fee was updated on 17 September 2019 and is now listed correctly on this page.
Policies
Admission to the University of St Andrews is governed by our admissions policy.
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 (PDF, 72 KB).
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 (PDF, 84 KB).