Data-Intensive Analysis (MSc) 2023 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.

Start date
September 2023
End date
September 2024
Duration
One year full time
School
School of Computer Science

Application deadline

Thursday 10 August 2023

Applicants should apply as early as possible to be eligible for certain scholarships and for international visa purposes.

“At St Andrews you are in a friendly and team-working environment where you feel that you are a student with many exceptional mentors. It has been amazing to learn about the statistical world in an applied way on real-life examples and scenarios rather than just the theory.”
Andreas
- Paphos, Cyprus

Entry requirements

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.

Application requirements

  • CV 
  • personal statement (optional) 
  • two original signed academic references 
  • academic transcripts and degree certificates .

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 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.

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 2022-2023 academic year; some elements may be subject to change for 2023 entry. 

Students take four compulsory modules.  

  • Introductory Data Analysis: covers essential statistical concepts and analysis methods relevant for commercial analysis. 
  • 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 generalised 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.  
  • Data-Intensive Systems: presents the programming paradigms, algorithmic techniques and design principles for large-scale distributed systems, such as those utilised by companies such as Google, Amazon and Facebook. 
  • 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: introduces and reinforces object-orientated modelling, design and implementation to provide a common basis of skills, allowing students to complete programming assignments within other MSc modules.  
  • Programming Principles and Practice: introduces computational thinking and problem-solving skills to students who have no or little previous programming experience.  
  • Software for Data Analysis: covers the practical computing aspects of statistical data analysis focusing on widely used packages, including data-wrangling and visualisation.  

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 2023 entry. Take a look at the most up-to-date modules in the module catalogue.

Teaching

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, mathematics and statistics. 

Teaching methods include lectures, seminars, tutorials and practical classes.

Class sizes typically range from 10 to 50 students. 

Most modules are assessed through practical coursework exercises and examinations.

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.

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.  

Fees

Home
£11,120

Overseas
£27,230

More information on tuition fees can be found on the postgraduate fees and funding page.

Funding and scholarships

The University of St Andrews is committed to attracting the very best students, regardless of financial circumstances. Find out more about the scholarships and postgraduate loans available.

All taught postgraduate scholarships

After your degree

Careers

Alumni of Computer Science MSc programmes 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 continue their education by enrolling in PhD programmes at St Andrews.

In addition to the MSc, the School of Computer Science 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. 

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.

The School of Mathematics and Statistics has active research groups in: 

  • Applied Mathematics (Vortex Dynamics Group, Solar and Magnetospheric Theory Group, Mathematical Biology Group) 
  • Pure Mathematics (Analysis Group, Algebra and Combinatorics Group) 
  • Mathematical Biology (Mathematical Oncology, Cell Migration and Tissue Growth) 
  • Statistics (Statistical Ecology, Statistical Medicine and Molecular Biology, and Statistical Methodology).  
Postgraduate research

What 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
Email
maths-pgstats@st-andrews.ac.uk
Address
School of Computer Science
Jack Cole Building
North Haugh
St Andrews
KY16 9SX

School of Computer Science website