The unique structure of this course offers a truly part-time route for mature professionals to supplement their work with an accredited qualification. The MSc requires students to study one module per semester over a 3 year period. The timetable for this course is designed so that students are able to study alongside working, and contact time for each module is scheduled as a maximum of one day per week.
The course follows the successful model of the KDD MSc (Industry Based), which we have run for our partner Aviva for over 14 years. During this time we have produced 80 outstanding graduates for Aviva, bolstering their workforce with Master’s-level recognition.
The course was short-listed as one of the most innovative collaborations between Business and a University by the East of England Development Agency.
This MSc is one of the few similar academic qualifications to have been conceived and developed to meet the specific needs of industry partners.
The course teaches skills which are directly relevant to industry, as the sector increasingly seeks to expand its use of data analytics.
Students will be able to see the direct impact of the course on their vocational work, as some of the modules will involve projects using their company’s data. This will allow students to integrate their university projects into their job, putting the principles they have learnt from the classroom into practice.
Companies will directly benefit from a relationship with the university which will involve access to state-of-the-art expertise in topical subjects such as data mining, statistics and information retrieval as well as artificial intelligence and database manipulation.
Some modules consider the experience and knowledge which students have acquired at work, and may seek to involve line managers in assessing participants’ skills.
The MSc represents excellent value for money, as employers are able to part fund high quality training to incentivise staff and increase retention. The added bonus of only having to give participants a single day off per module makes the programme much more cost effective than other similar programmes.
This programme has full Chartered IT Professional accreditation (Further Learning Element) as well as leading to Chartered Engineer (CEng) status from the BCS (The Chartered Institute for IT).
95% of research activity was classified as internationally leading, excellent or recognised in the 2008 Research Assessment Exercise.
Our Masters programmes are accredited by the BCS - The Chartered Institute for IT to full Chartered IT Professional (CITP Further Learning Element) as well as leading to Chartered Engineer (CEng) status.
The School maintains close links with industry and many of our student assignments relate to real world problems.
The School of Computing Sciences leads the university in utilizing its internationally recognized research commercially, through SYS Consulting, its Consulting company and through Knowledge Transfer Partnerships.
A module that provides students with the training of some transferable skills, an overview of research methods used in computing sciences, and introduces individual students to background material preparatory to their dissertation project which they would not otherwise study systematically to the required depth. The learning objectives for this module are to enable students to approach the dissertation with the intellectual and practical skills necessary to successfully complete a masters dissertation in Computer Science. More...
Option A Study (20 credits)
Students will select 20 credits from the following modules:
This is an applied statistics module designed to give Masters students the opportunity to apply statistical methods in realistic situations. While no advanced knowledge of probability and statistics is required, we expect students to have done some background in probability and statistics before taking this course. The aim is to cover 4 topics from a list including: Estimating population abundance; Extremes and quantiles; Linear models; Bootstrap methods and their application; Sample surveys; Simulations; Subjective statistics (MCMC); Forecasing; and Clustering methods. If there is a demand for a specific topic we will always consider providing a project in that area if possible. The assessment will be by coursework. More...
This module introduces the student to core techniques in Artificial Intelligence and some topics in algorithmics. Topics to be covered include state space, search techniques, algorithmic paradigms, NP-completeness, metaheuristics, logic and knowledge representation and expert systems. More...
This module is designed for postgraduate students studying on MSc courses. The module explores the methodologies of Knowledge Discovery and Data Mining (KDD). It aims to cover each stage of the KDD process, including data gathering, preliminary data anaylsis or data exploration, data cleansing, pre-processing and the various data analysis tasks that fall under the heading of data mining. Through this module, students should gain knowledge of algorithms and methods for each stage of the process, as well as practical experience using leading KDD software packages throughout all of the stages of KDD process. More...
Option A Study (40 credits)
Students will select 40 credits from the following modules:
This module aims to establish a clear understanding of the Object Oriented Programming paradigm, including Encapsulation, Inheritance and Polymorphism. Simple data structures will be considered and classes will be indentified as vehicles for implementing abstract data types. The benefits of modular software design will be emphasised and the Unified Modelling Language (UML) will be introduced as a design tool for large software systems. Topics include: Data structures, programming, and program design. More...
This is an applied statistics module designed to give Masters students the opportunity to apply statistical methods in realistic situations. While no advanced knowledge of probability and statistics is required, we expect students to have done some background in probability and statistics before taking this course. The aim is to cover 4 topics from a list including: Estimating population abundance; Extremes and quantiles; Linear models; Bootstrap methods and their application; Sample surveys; Simulations; Subjective statistics (MCMC); Forecasing; and Clustering methods. If there is a demand for a specific topic we will always consider providing a project in that area if possible. The assessment will be by coursework. More...
This module introduces the student to core techniques in Artificial Intelligence and some topics in algorithmics. Topics to be covered include state space, search techniques, algorithmic paradigms, NP-completeness, metaheuristics, logic and knowledge representation and expert systems. More...
This module is designed for postgraduate students studying on MSc courses. The module explores the methodologies of Knowledge Discovery and Data Mining (KDD). It aims to cover each stage of the KDD process, including data gathering, preliminary data anaylsis or data exploration, data cleansing, pre-processing and the various data analysis tasks that fall under the heading of data mining. Through this module, students should gain knowledge of algorithms and methods for each stage of the process, as well as practical experience using leading KDD software packages throughout all of the stages of KDD process. More...
This module provides an overview of the philosophy and development of database technology. Practical experience of database manipulation is provided through the use of SQL and the Java JDBC interface on the IBM DB2 database management system. Database design is introduced using Entity-Relationship modelling and normalisation. More...
An introduction to Human Computer Interaction including user interfaces on conventional computers and small footprint devices (e.g. PDAs and smart mobile phones). Human-Computer interactions are approached from a variety of perspectives: cognitive, ethnographic, socio-technical and systems theoretic. The module covers aspects of cognitive psychology and ethnographic methods necessary to understand and evaluate HCI. It brings together established practice and newer perspectives in order to fully understand what is meant by the usability and accessibility of modern interactive systems. More...
The module explores the development of Information Retrieval technologies, which have been driven by large increases in on-line documents and the Internet search engines, surveys multimedia and cross-language IR, reviews, current NLP techniques and their role in IR. More...
In this module, students will be expected to undertake a project in data mining under faculty supervision. The project will consolidate the problem solving, data analysis and technical writing skills learned in the other modules, particularly CMPSMC6Y. The project can act as a feasibility study for a larger dissertation project. However, they must be substantially different in content. More...
This module is taken by students studying in Industry. This is a project based on the material of CMPSMC2Y and ideally involves a problem that should be of interest to the student in their work environment. More...
Compulsory Study (60 credits)
Students must study the following modules for 60 credits:
Reserved for postgraduates in the School of Computing Sciences, this module provides the student with a piece of individual work, with substantial research and practical elements. The subject of the dissertation will be determined by agreement between the student and his or her supervisor. The work may be undertaken as part of a large collaborative or group project. More...
Option A Study (40 credits)
Students will select 40 credits from the following modules:
This module aims to establish a clear understanding of the Object Oriented Programming paradigm, including Encapsulation, Inheritance and Polymorphism. Simple data structures will be considered and classes will be indentified as vehicles for implementing abstract data types. The benefits of modular software design will be emphasised and the Unified Modelling Language (UML) will be introduced as a design tool for large software systems. Topics include: Data structures, programming, and program design. More...
This is an applied statistics module designed to give Masters students the opportunity to apply statistical methods in realistic situations. While no advanced knowledge of probability and statistics is required, we expect students to have done some background in probability and statistics before taking this course. The aim is to cover 4 topics from a list including: Estimating population abundance; Extremes and quantiles; Linear models; Bootstrap methods and their application; Sample surveys; Simulations; Subjective statistics (MCMC); Forecasing; and Clustering methods. If there is a demand for a specific topic we will always consider providing a project in that area if possible. The assessment will be by coursework. More...
This module introduces the student to core techniques in Artificial Intelligence and some topics in algorithmics. Topics to be covered include state space, search techniques, algorithmic paradigms, NP-completeness, metaheuristics, logic and knowledge representation and expert systems. More...
This module is designed for postgraduate students studying on MSc courses. The module explores the methodologies of Knowledge Discovery and Data Mining (KDD). It aims to cover each stage of the KDD process, including data gathering, preliminary data anaylsis or data exploration, data cleansing, pre-processing and the various data analysis tasks that fall under the heading of data mining. Through this module, students should gain knowledge of algorithms and methods for each stage of the process, as well as practical experience using leading KDD software packages throughout all of the stages of KDD process. More...
This module provides an overview of the philosophy and development of database technology. Practical experience of database manipulation is provided through the use of SQL and the Java JDBC interface on the IBM DB2 database management system. Database design is introduced using Entity-Relationship modelling and normalisation. More...
An introduction to Human Computer Interaction including user interfaces on conventional computers and small footprint devices (e.g. PDAs and smart mobile phones). Human-Computer interactions are approached from a variety of perspectives: cognitive, ethnographic, socio-technical and systems theoretic. The module covers aspects of cognitive psychology and ethnographic methods necessary to understand and evaluate HCI. It brings together established practice and newer perspectives in order to fully understand what is meant by the usability and accessibility of modern interactive systems. More...
The module explores the development of Information Retrieval technologies, which have been driven by large increases in on-line documents and the Internet search engines, surveys multimedia and cross-language IR, reviews, current NLP techniques and their role in IR. More...
In this module, students will be expected to undertake a project in data mining under faculty supervision. The project will consolidate the problem solving, data analysis and technical writing skills learned in the other modules, particularly CMPSMC6Y. The project can act as a feasibility study for a larger dissertation project. However, they must be substantially different in content. More...
This module is taken by students studying in Industry. This is a project based on the material of CMPSMC2Y and ideally involves a problem that should be of interest to the student in their work environment. More...
Disclaimer
Whilst the University will make every effort to offer the modules listed, changes may sometimes be made arising from the annual monitoring, review and update of modules and regular (five-yearly) review of course programmes. Where this activity leads to significant (but not minor) changes to programmes and their constituent modules, there will normally be prior consultation of students and others. It is also possible that the University may not be able to offer a module for reasons outside of its control, such as the illness of a member of staff or sabbatical leave. Where this is the case, the University will endeavour to inform students.
Entry Requirements
Degree Subject:
Computing, Mathematics or a related subject.
Degree Classification:
Good first degree (minimum 2.1 or equivalent).
Students for whom English is a Foreign language
We welcome applications from students whose first language is not English. To ensure such students benefit from postgraduate study, we require evidence of proficiency in English. Our usual entry requirements are as follows:
IELTS: 6.5 (minimum 6.0 in all components) TOEFL: Internet-based score of 88 (minimum 18 in listening, 21 speaking, 19 writing and 20 reading) PTE (Pearson): 62 (minimum 55 in all components) Test dates should be within two years of the course start date.
Other tests such as TOEIC and the Cambridge Certificate of Advanced English are also accepted by the university. Please check with the Admissions Office for further details including the scores or grades required.
INTO UEA and INTO UEA London run pre-sessional courses which can be taken prior to the start of your course. For further information and to see if you qualify please contact intopre-sessional@uea.ac.uk (INTO UEA Norwich) or pseuealondon@into.uk.com (INTO UEA London).
Fees and Funding
Tuition Fees 2012/13
UK/EU £5,000
International £12,500
Funding
International applicants applying to this course can be considered for one Faculty of Science half fees scholarship or one £2000 scholarship. The deadline is 1st April 2013.
Faculty of Science Scholarships Students wishing to apply should submit an essay answering the following question in 1000 words: 'Is it OK to let the data speak for itself'? Essays should be emailed to the Admissions Office. Please ensure you include your full name, the course you have applied to, and your applicant number in your email.
For more information please contact the Computing Sciences Postgraduate Admissions Office (cmp.pgt.admiss@uea.ac.uk).
Applications for Postgraduate Taught programmes at the University of East Anglia should be made directly to the University.
