Course information

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.

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 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.

Further particulars regarding curriculum development.

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 2016–2017 academic year; some elements may be subject to change for 2017 entry.

Compulsory modules

Students take five compulsory modules. 

• 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.
• 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.
• 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.
• Knowledge Discovery & Datamining: covers many of the methods found under the banner of "Datamining", building from a theoretical perspective but ultimately teaching practical application.

Optional modules

Students choose two of the following optional modules. 

• Programming Principles and Practice: introduces computational thinking and problem solving skills to students who have no or little previous programming experience.
• Masters Programming Projects: reinforces key programming skills gained during the first programming module of the programme and offers increasing depth and scope for creativity.
• Information Visualisation and Visual Analytics: explores how to utilise visual representations to make information accessible for exploration and analysis.

The modules listed ran in the academic year 2016–2017 and are indicative of this course. There is no guarantee that these modules will run for 2017 entry. Take a look at the most up-to-date modules in the module catalogue.

Dissertation

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.

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. 

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).