Data-Intensive Analysis MSc
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I love the stimulating environment the university offers along with a wide variety of academic programmes.
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Master
In St Andrews
Description
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Type
Master
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Location
St andrews (Scotland)
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Duration
2 Years
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.
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About this course
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.
Alumni of Computer Science MSc programmes have gone on to work in a variety of global, commercial, financial and research institutions, including:
Amadeus
Amazon
Atlas
Avaloq
Barclays Capital
BP
BT Openreach
Capricorn Ventis
FactSet
Hailo
Hewlett Packard
Hitachi Data Systems
Microsoft
OpenBet
Rockstar
Royal Bank of Scotland
Sky
Skyscanner
Symantec
TriSystems
A good 2.1 Honours undergraduate degree, plus evidence of some previous programming experience.
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I love the stimulating environment the university offers along with a wide variety of academic programmes.
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This centre has featured on Emagister for 14 years
Subjects
- Programming
- Computational
- Visualisation
- Data-Intensive
- Information Visualisation
- Orientated Modelling
- Design
- Problem Solving
- Computing
- Data analysis
Course programme
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 & Datamining: 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.
- Data-Intensive Systems
- 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.
Additional information
Data-Intensive Analysis MSc