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Our MSc course in Investigative Ophthalmology and Vision Sciences brings together the research expertise in vision from The University of Manchester and Manchester Royal Eye Hospital. Read more
Our MSc course in Investigative Ophthalmology and Vision Sciences brings together the research expertise in vision from The University of Manchester and Manchester Royal Eye Hospital.

This course will provide you with a firm grounding in the knowledge needed to pursue a higher degree and to conduct high quality research in ophthalmology, optometry or vision sciences. It also gives an opportunity for vision-related professionals to advance their knowledge of the scientific foundations of ophthalmology and vision sciences.

The course is aimed at optometrists, ophthalmologists, orthoptists and nurses from the UK and overseas. It is suitable for:
-Individuals who are considering undertaking a research degree in the vision sciences
-Those interested in professional development
-Those interested in conducting research as part of their clinical training
-Ophthalmologists wishing to expand and extend their training into specialist areas
-Optometrists considering a career in the hospital eye service

Teaching and learning

The course has two different pathways:
1. Six taught units (15 credits each) and a project dissertation (90) credits.
2. Four taught units (15 credits each), a literature review (30 credits) and a dissertation (90 credits).

The six units are Research Methods, Cornea, Contact Lens, Vascular Disease, Macular Degeneration and Glaucoma.

In each of the units, learning will be based on a series of formal lectures on topics relating to ocular disease and treatments, and a series of more informal tutorials on current research. You will receive copies of presentations and direction to relevant literature for personal study.

Many projects have led to peer reviewed publications in the ophthalmic literature. Recent titles include the following:
-Optical coherence tomography measures of the retinal nerve fibre layer
-Development of a model cell assay to investigate the cellular processing of ARB mutant bestrophin-1
-Risk factors for late presentation of patients with primary open angle glaucoma
-Molecular analysis of autosomal recessive retinal dystrophies
-In vivo analysis of the wettability of silicon hydrogel contact lenses
-Can corneal densitometry be used to assess the treatment outcome after corneal transplantation
-A contact lens based technique delivering cultured stem cells onto the human corneal surface
-The use of corneal imaging to assessing treatment outcomes of LASIK and LASEK
-Addressing the physiological cues needed for trans-differentiation of dental pulp stem cells into limbal stem cells

Coursework and assessment

Assessment is via:
-Written examinations in January and May
-Coursework set during the taught units
-A research project dissertation

Career opportunities

This course is aimed at optometrists, ophthalmologists, orthoptists and nurses from the UK and overseas.

It is considered suitable for:
-Individuals interested in vision sciences
-Those interested in conducting research as part of their clinical training
-Optometrists considering a career in the hospital eye service
-Those interested in an academic career in ophthalmology/optometry/vision sciences
-Optometrists interested in professional development

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Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study Data Science at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Read more
Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study Data Science at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Postgraduate loans are also available to English and Welsh domiciled students. For more information on fees and funding please visit our website.

MSc in Data Science aims to equip students with a solid grounding in data science concepts and technologies for extracting information and constructing knowledge from data. Students of the MSc Data Science will study the computational principles, methods, and systems for a variety of real world applications that require mathematical foundations, programming skills, critical thinking, and ingenuity. Development of research skills will be an essential element of the Data Science programme so that students can bring a critical perspective to current data science discipline and apply this to future developments in a rapidly changing technological environment.

Key Features of the MSc Data Science

The MSc Data Science programme focuses on three core technical themes: data mining, machine learning, and visualisation. Data mining is fundamental to data science and the students will learn how to mine both structured data and unstructured data. Students will gain practical data mining experience and will gain a systematic understanding of the fundamental concepts of analysing complex and heterogeneous data. They will be able to manipulate large heterogeneous datasets, from storage to processing, be able to extract information from large datasets, gain experience of data mining algorithms and techniques, and be able to apply them in real world applications. Machine learning has proven to be an effective and exciting technology for data and it is of high value when it comes to employment. Students of the Data Science programme will learn the fundamentals of both conventional and state-of-the-art machine learning techniques, be able to apply the methods and techniques to synthesise solutions using machine learning, and will have the necessary practical skills to apply their understanding to big data problems. We will train students to explore a variety visualisation concepts and techniques for data analysis. Students will be able to apply important concepts in data visualisation, information visualisation, and visual analytics to support data process and knowledge discovery. The students of the Data Science programme also learn important mathematical concepts and methods required by a data scientist. A specifically designed module that is accessible to students with different background will cover the basics of algebra, optimisation techniques, statistics, and so on. More advanced mathematical concepts are integrated in individual modules where necessary.

The MSc Data Science programme delivers the practical components using a number of programming languages and software packages, such as Hadoop, Python, Matlab, C++, OpenGL, OpenCV, and Spark. Students will also be exposed to a range of closely related subject areas, including pattern recognition, high performance computing, GPU processing, computer vision, human computer interaction, and software validation and verification. The delivery of both core and optional modules leverage on the research strength and capacity in the department. The modules are delivered by lecturers who are actively engaged in world leading researches in this field. Students of the Data Science programme will benefit from state-of-the-art materials and contents, and will work on individual degree projects that can be research-led or application driven.

Modules

Modules for the MSc Data Science programme include:

- Visual Analytics
- Data Science Research Methods and Seminars
- Big Data and Data Mining
- Big Data and Machine Learning
- Mathematical Skills for Data Scientists
- Data Visualization
- Human Computer Interaction
- High Performance Computing in C/C++
- Graphics Processor Programming
- Computer Vision and Pattern Recognition
- Modelling and Verification Techniques
- Operating Systems and Architectures

Facilities

The Department of Computer Science is well equipped for teaching, and is continually upgrading its laboratories to ensure equipment is up-to-date – equipment is never more than three years old, and rarely more than two. Currently, our Computer Science students use three fully networked laboratories: one, running Windows; another running Linux; and a project laboratory, containing specialised equipment. These laboratories support a wide range of software, including the programming languages Java, C# and the .net framework, C, C++, Haskell and Prolog among many; integrated programme development environments such as Visual Studio and Netbeans; the widely-used Microsoft Office package; web access tools; and many special purpose software tools including graphical rendering and image manipulation tools; expert system production tools; concurrent system modelling tools; World Wide Web authoring tools; and databases.

As part of the expansion of the Department of Computer Science, we are building the Computational Foundry on our Bay Campus for computer science and mathematical science.

Career Destinations

- Data Analyst
- Data mining Developer
- Machine Learning Developer
- Visual Analytics Developer
- Visualisation Developer
- Visual Computing Software Developer
- Database Developer
- Data Science Researcher
- Computer Vision Developer
- Medical Computing Developer
- Informatics Developer
- Software Engineer

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Technologies based on the intelligent use of data are leading to great changes in our everyday life. Data Science and Engineering refers to the know-how and competence required to effectively manage and analyse the massive amount of data available in a wide range of domains. Read more
Technologies based on the intelligent use of data are leading to great changes in our everyday life. Data Science and Engineering refers to the know-how and competence required to effectively manage and analyse the massive amount of data available in a wide range of domains.

We offer a two-year Master of Science in Computer Science centered on this emerging field. The backbone of the program is constituted by three core units on advanced data management, machine learning, and high performance computing. Leveraging on the expertise of our faculty, the rest of the program is organised in four tracks, Business Intelligence, Health & Life Sciences, Pervasive Computing, and Visual Computing, each providing a solid grounding in data science and engineering as well as a firm grasp of the domain of interest.

By blending standard classes with recitations and lab sessions our program ensures that each student masters the theoretical foundations and acquires hands-on experience in each subject. In most units credit is obtained by working on a final project. Additional credit is also gained through short-term internship in the industry or in a research lab. The master thesis is worth 25% of the total credit.

TRACKS

• Business Intelligence. This track builds on first hand knowledge of business management and fundamentals of data warehousing, and focuses on data mining, graph analytics, information visualisation, and issues related to data protection and privacy.
• Health & Life Sciences. Starting from core knowledge of signal and image processing, bioinformatics and computational biology, this track covers methods for biomedical image reconstruction, computational neuroengineering, well-being technologies and data protection and privacy.
• Pervasive Computing. Security and ubiquitous computing set the scene for this track which deals with data semantics, large scale software engineering, graph analytics and data protection and privacy.
• Visual Computing. This track lays the basics of signal & image processing and of computer graphics & augmented reality, and covers human computer interaction, computational vision, data visualisation, and computer games.

PROSPECTIVE CAREER

Senior expert in Data Science and Engineering. You will be at the forefront of the high-tech job market since all big companies are investing on data driven approaches for decision making and planning. The Business Intelligence area is highly regarded by consulting companies and large enterprises, while the Health and Life Sciences track is mainly oriented toward biomedical industry and research institutes. Both the Pervasive and the Visual Computing tracks are close to the interests of software companies. For all tracks a job in a start-up company or a career on your own are always in order.

Senior computer scientist.. By personalizing your plan of study you can keep open all the highly qualified job options in software companies.

Further graduate studies.. In all cases, you will be fully qualified to pursue your graduate studies toward a PhD in Computer Science.

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Data science is an emerging new area of science. With City’s MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights. Read more
Data science is an emerging new area of science. With City’s MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights.

Who is it for?

This programme is for students who have a numerate first degree or can demonstrate numerate skills. Students are often at the early stages of their careers in diverse professions including economics, statistics and computer science.

Students will have a curiosity about data, and will want to learn new techniques to boost their career and be part of exciting current industry developments. The MSc in Data Science includes some complex programming tasks because of the applied nature of the course, so many students have a mathematics or statistics background and enjoy working with algorithms.

Objectives

The demand for data scientists in the UK has grown more than ten-fold in the past five years *. The amount of data in the world is growing exponentially. From analysing tyre performance to detecting problem gamblers, wherever data exists, there are opportunities to apply it.

City’s MSc Data Science programme covers the intersection of computer science and statistics, machine learning and practical applications. We explore areas such as visualisation because we believe that data science is about generating insight into data as well as its communication in practice.

The programme focuses on machine learning as the most exciting technology for data and we have learned from our own graduates that this is of high value when it comes to employment within the field. At City, we have excellent expertise in machine learning and the facilities students need to learn the technical aspects of data analysis. We also have a world-leading centre for data visualisation, where students get exposed to the latest developments on presenting and communicating their results – a highly sought after skill.

Placements

There is the opportunity to do an internship as part of the programme. The final project, which is normally three months for a full-time student, can be extended to six months if you want to study within a specific organisation. When it comes to the big data and data science area, we have established relationships with organisations including the BBC, Microsoft and The British Library so you can be confident that with City, your access to professional experience is unparalleled. One recent student undertook an internship with Google and has since secured a job within the company.

Academic facilities

The School's computer science laboratories are equipped with the latest up-to-date hardware and software. From Oracle’s leading commercial object-relational database server to PCs with state-of-the-art NVidia GPUs for computer graphics, you will have access to an array of tools to support your learning.

The MSc Data Science programme offers two (three by mid 2016) dedicated computer servers for the Big Data module, which you can also use for your final project to analyse large data sets. We give you the opportunity to undertake training in MATLAB, the most popular numerical and technical programming environment, while you study.

Scholarships

A scholarship for the full fees of the MSc will be offered to an outstanding applicant. The scholarship is available to UK/EU and overseas students, studying full-time. To be considered for the scholarship, please include with your full application a one-page essay with your answer to the question:

'What are the challenges that Data Science faces and how would you address those challenges?'

The submission deadline for anyone wishing to be considered for the scholarship is: 1 MAY 2017

Teaching and learning

The teaching and learning methods we use mean that students’ specialist knowledge and autonomy increase as they progress through each module. Active researchers guide your progress in the areas of machine learning, data visualization, and high-performance computing, which culminates with an individual project. This is an original piece of research conducted with academic supervision, but largely independently and, where appropriate, in collaboration with industrial partners.

Taught modules are delivered through a series of 20 hours of lectures and 10 hours of tutorials/laboratory sessions. Lectures are normally used to:
-Present and exemplify the concepts underpinning a particular subject.
-Highlight the most significant aspects of the syllabus.
-Indicate additional topics and resources for private study.

Tutorials help you develop the skills to apply the concepts we have covered in the lectures. We normally achieve this through practical problem solving contexts.

Laboratory sessions give you the opportunity to apply concepts and techniques using state-of-the-art software, environments and development tools.

In addition to lectures, laboratory sessions and tutorial support, you also have access to a personal tutor. This is an academic member of staff from whom you can gain learning support throughout your degree. In addition, City’s online learning environment Moodle contains resources for each of the modules from lecture notes and lab materials, to coursework feedback, model answers, and an interactive discussion forum.

We expect you to study independently and complete coursework for each module. This should amount to approximately 120 hours per module if you are studying full time. Each module is assessed through a combination of written examination and coursework, where you will need to answer theoretical and practical questions to demonstrate that you can analyse and apply data science methods and techniques.

The individual project is a substantial task. It is your opportunity to develop a research-related topic under the supervision of an academic member of staff. This is the moment when you can apply what you have learnt to solve a real-world problem using large datasets from industry, academia or government and use your knowledge of collecting and processing real data, designing and implementing big data methods and applying and evaluating data analysis, visualisation and prediction techniques. At the end of the project you submit a substantial MSc project report, which becomes the mode of assessment for this part of the programme.

Course content

Data science is the area of study concerned with the extraction of insight from large collections of data.

The course covers the study, integration and application of advanced methods and techniques from:
-Data analysis and machine learning
-Data visualisation and visual analytics
-High-performance, parallel and distributed computing
-Knowledge representation and reasoning
-Neural computation
-Signal processing
-Data management and information retrieval.

It gives you the opportunity to specialise so, once you graduate, you can apply data science to any sector from health to retail. By engaging with researchers and industrial partners during the programme, you can develop your knowledge and skills within a real-world context in each of the above areas.

Core modules
-Principles of data science (15 credits)
-Machine learning (15 credits)
-Big Data (15 credits)
-Neural computing (15 credits)
-Visual analytics (15 credits)
-Research methods and professional issues (15 credits)

Elective modules
-Advanced programming: concurrency (15 credits)
-Readings in computer science (15 credits)
-Advanced databases (15 credits)
-Information retrieval (15 credits)
-Data visualisation (15 credits)
-Digital signal processing and audio programming (15 credits)
-Cloud computing (15 credits)
-Computer vision (15 credits)
-Software agents (15 credits)

Individual project - (60 credits)

Career prospects

From health to retail, and from the IT industry to government, the Data Science MSc will prepare you for a successful career as a data scientist. You will graduate with specialist skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation, which are in high demand.

City's unique internships, our emphasis on machine learning and visual analytics, together with our links with the industry and Tech City, should help you gain employment as a specialist in data analysis and visualization. Graduates starting a new business can benefit from City's London City Incubator and City's links with Tech City, providing support for start-up businesses.

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Data Science brings together computational and statistical skills for data-driven problem solving. Read more
Data Science brings together computational and statistical skills for data-driven problem solving. This rapidly expanding area includes machine learning, deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence.

Degree information

The programme comprises core machine learning methodology and an introduction to statistical science, combined with a set of more specialised and advanced options covering computing and statistical modelling. Projects are offered both within UCL Computer Science and from a wide range of industry partners.

Students undertake modules to the value of 180 credits.

The programme consists of three compulsory modules (45 credits), five optional modules (75credits) and a dissertation/report (60 credits).

Core modules
-Applied Machine Learning
-Introduction to Supervised Learning
-Introduction to Statistical Data Science

Optional modules - students choose a minimum of 30 credits and a maximum of 60 credits from the following optional modules:
-Cloud Computing (Birkbeck)
-Machine Vision
-Information Retrieval & Data Mining
-Statistical Natural Language Processing
-Web Economics

Students choose a minimum of 0 credits and a maximum of 30 credits from these optional Statistics modules:
-Statistical Design of Investigations
-Applied Bayesian Methods
-Decision & Risk

Students choose a minimum of 15 credits and a maximum of 15 credits from these elective modules:
-Supervised Learning
-Graphical Models
-Bioinformatics
-Affective Computing and Human-Robot Interaction
-Computational Modelling for Biomedical Imaging
-Stochastic Systems
-Forecasting

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning
The programme is delivered though a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed through a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

Careers

Data science professionals are increasingly sought after as the integration of statistical and computational analytical tools becomes more essential to organisations. A thorough understanding of the fundamentals required from the best practitioners, and this programme's broad base, assists data scientists to adapt to rapidly evolving goals. This is a new degree and information on graduate destinations is not currently available. However, MSc graduates from across the department frequently find roles with major tech and finance companies including:
-Google Deepmind
-Microsoft Research
-Dunnhumby
-Index Ventures
-Last.fm
-Cisco
-Deutsche Bank
-IBM
-Morgan Stanley

Why study this degree at UCL?

The 2014 Research Excellence Framework ranked UCL first in the UK for computer science. 61% of its research work is rated as world-leading and 96% as internationally excellent.

UCL Computer Science staff have research interests ranging from foundational machine learning and large-scale data analysis to commercial aspect of business intelligence. Our extensive links to companies provide students with opportunities to carry out the research project with an industry partner.

The department also enjoys strong collaborative relationships across UCL; and exposure to interdisciplinary research spanning UCL Computer Science and UCl Statistical Science will provide students with a broad perspective of the field. UCL is home to regular machine learning masterclasses and big data seminars.

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If you are intrigued by the acquisition, processing, analysis and understanding of computer vision, this Masters is for you. The programme is offered by Surrey's Department of Electrical and Electronic Engineering, recognised for world-leading research in multimedia signal processing and machine learning. Read more
If you are intrigued by the acquisition, processing, analysis and understanding of computer vision, this Masters is for you.

The programme is offered by Surrey's Department of Electrical and Electronic Engineering, recognised for world-leading research in multimedia signal processing and machine learning.

PROGRAMME OVERVIEW

This degree provides in-depth training for students interested in a career in industry or in research-oriented institutions focused on image and video analysis, and deep learning.

State-of-the-art computer-vision and machine-learning approaches for image and video analysis are covered in the course, as well as low-level image processing methods.

Students also have the chance to substantially expand their programming skills through projects they undertake.

PROGRAMME STRUCTURE

This programme is studied full-time over 12 months and part-time over 48 months. It consists of eight taught modules and a standard project.

The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.
-Digital Signal Processing A
-Object Oriented Design and C++
-Image Processing and Vision
-Space Robotics and Autonomy
-Satellite Remote Sensing
-Computer Vision and Pattern Recognition
-AI and AI Programming
-Advanced Signal Processing
-Image and Video Compression
-Standard Project

EDUCATIONAL AIMS OF THE PROGRAMME

The taught postgraduate degree programmes of the Department of Electronic Engineering are intended both to assist with professional career development within the relevant industry and, for a small number of students, to serve as a precursor to academic research.

Our philosophy is to integrate the acquisition of core engineering and scientific knowledge with the development of key practical skills (where relevant). To fulfil these objectives, the programme aims to:
-Attract well-qualified entrants, with a background in Electronic Engineering, Physical Sciences, Mathematics, Computing and Communications, from the UK, Europe and overseas.
-Provide participants with advanced knowledge, practical skills and understanding applicable to the MSc degree
-Develop participants' understanding of the underlying science, engineering, and technology, and enhance their ability to relate this to industrial practice
-Develop participants' critical and analytical powers so that they can effectively plan and execute individual research/design/development projects
-Provide a high level of flexibility in programme pattern and exit point
-Provide students with an extensive choice of taught modules, in subjects for which the Department has an international and UK research reputation

Intended capabilities for MSc graduates
-Know, understand and be able to apply the fundamental mathematical, scientific and engineering facts and principles that underpin computer vision, machine learning as well as how they can be related to robotics
-Be able to analyse problems within the field computer vision and more broadly in electronic engineering and find solutions
-Be able to use relevant workshop and laboratory tools and equipment, and have experience of using relevant task-specific software packages to perform engineering tasks
-Know, understand and be able to use the basic mathematical, scientific and engineering facts and principles associated with the topics within computer vision, machine learning
-Be aware of the societal and environmental context of his/her engineering activities
-Be aware of commercial, industrial and employment-related practices and issues likely to affect his/her engineering activities
-Be able to carry out research-and-development investigations
-Be able to design electronic circuits and electronic/software products and systems

Technical characteristics of the pathway
This programme in Computer Vision, Robotics and Machine Learning aims to provide a high-quality advanced training in aspects of computer vision for extracting information from image and video content or enhancing its visual quality using machine learning codes.

Computer vision technology uses sophisticated signal processing and data analysis methods to support access to visual information, whether it is for business, security, personal use or entertainment. The core modules cover the fundamentals of how to represent image and video information digitally, including processing, filtering and feature extraction techniques.

An important aspect of the programme is the software implementation of such processes. Students will be able to tailor their learning experience through selection of elective modules to suit their career aspirations.

Key to the programme is cross-linking between core methods and systems for image and video analysis applications. The programme has strong links to current research in the Department of Electronic Engineering’s Centre for Vision, Speech and Signal Processing.

PROGRAMME LEARNING OUTCOMES

The Department's taught postgraduate programmes are designed to enhance the student's technical knowledge in the topics within the field that he/she has chosen to study, and to contribute to the Specific Learning Outcomes set down by the Institution of Engineering and Technology (IET) (which is the Professional Engineering body for electronic and electrical engineering) and to the General Learning Outcomes applicable to all university graduates.

General transferable skills
-Be able to use computers and basic IT tools effectively
-Be able to retrieve information from written and electronic sources
-Be able to apply critical but constructive thinking to received information
-Be able to study and learn effectively
-Be able to communicate effectively in writing and by oral presentations
-Be able to present quantitative data effectively, using appropriate methods

Time and resource management
-Be able to manage own time and resources
-Be able to develop, monitor and update a plan, in the light of changing circumstances
-Be able to reflect on own learning and performance, and plan its development/improvement, as a foundation for life-long learning

Underpinning learning
-Know and understand scientific principles necessary to underpin their education in electronic and electrical engineering, to enable appreciation of its scientific and engineering content, and to support their understanding of historical, current and future developments
-Know and understand the mathematical principles necessary to underpin their education in electronic and electrical engineering and to enable them to apply mathematical methods, tools and notations proficiently in the analysis and solution of engineering problems
-Be able to apply and integrate knowledge and understanding of other engineering disciplines to support study of electronic and electrical engineering

Engineering problem-solving
-Understand electronic and electrical engineering principles and be able to apply them to analyse key engineering processes
-Be able to identify, classify and describe the performance of systems and components through the use of analytical methods and modelling techniques
-Be able to apply mathematical and computer-based models to solve problems in electronic and electrical engineering, and be able to assess the limitations of particular cases
-Be able to apply quantitative methods relevant to electronic and electrical engineering, in order to solve engineering problems
-Understand and be able to apply a systems approach to electronic and electrical engineering problems

Engineering tools
-Have relevant workshop and laboratory skills
-Be able to write simple computer programs, be aware of the nature of microprocessor programming, and be aware of the nature of software design
-Be able to apply computer software packages relevant to electronic and electrical engineering, in order to solve engineering problems

Technical expertise
-Know and understand the facts, concepts, conventions, principles, mathematics and applications of the range of electronic and electrical engineering topics he/she has chosen to study
-Know the characteristics of particular materials, equipment, processes or products
-Have thorough understanding of current practice and limitations, and some appreciation of likely future developments
-Be aware of developing technologies related to electronic and electrical engineering
-Have comprehensive understanding of the scientific principles of electronic engineering and related disciplines
-Have comprehensive knowledge and understanding of mathematical and computer models relevant to electronic and electrical engineering, and an appreciation of their limitations
-Know and understand, at Master's level, the facts, concepts, conventions, principles, mathematics and applications of a range of engineering topics that he/she has chosen to study
-Have extensive knowledge of a wide range of engineering materials and components
-Understand concepts from a range of areas including some from outside engineering, and be able to apply them effectively in engineering projects

Societal and environmental context
-Understand the requirement for engineering activities to promote sustainable development
-Relevant part of: Be aware of the framework of relevant legal requirements governing engineering activities, including personnel, health, safety and risk (including environmental risk issues
-Understand the need for a high level of professional and ethical conduct in engineering

Employment context
-Know and understand the commercial and economic context of electronic and electrical engineering processes
-Understand the contexts in which engineering knowledge can be applied (e.g. operations and management, technology development, etc.)
-Be aware of the nature of intellectual property
-Understand appropriate codes of practice and industry standards
-Be aware of quality issues
-Be able to apply engineering techniques taking account of a range of commercial and industrial constraints
-Understand the basics of financial accounting procedures relevant to engineering project work
-Be able to make general evaluations of commercial risks through some understanding of the basis of such risks
-Be aware of the framework of relevant legal requirements governing engineering activities, including personnel, health, safety and risk (including environmental risk) issues

Research and development
-Understand the use of technical literature and other information sources
-Be aware of the need, in appropriate cases, for experimentation during scientific investigations and during engineering development
-Be able to use fundamental knowledge to investigate new and emerging technologies
-Be able to extract data pertinent to an unfamiliar problem, and employ this data in solving the problem, using computer-based engineering tools when appropriate
-Be able to work with technical uncertainty

Design
-Understand the nature of the engineering design process
-Investigate and define a problem and identify constraints, including environmental and sustainability limitations, and health and safety and risk assessment issues
-Understand customer and user needs and the importance of considerations such as aesthetics
-Identify and manage cost drivers
-Use creativity to establish innovative solutions
-Ensure fitness for purpose and all aspects of the problem including production, operation, maintenance and disposal
-Manage the design process and evaluate outcomes
-Have wide knowledge and comprehensive understanding of design processes and methodologies and be able to apply and adapt them in unfamiliar situations
-Be able to generate an innovative design for products, systems, components or processes, to fulfil new needs

Project management
-Be able to work as a member of a team
-Be able to exercise leadership in a team
-Be able to work in a multidisciplinary environment
-Know about management techniques that may be used to achieve engineering objectives within the commercial and economic context of engineering processes
-Have extensive knowledge and understanding of management and business practices, and their limitations, and how these may be applied appropriately

FACILITIES, EQUIPMENT AND SUPPORT

To support your learning, we hold regular MSc group meetings where any aspect of the programme, technical or non-technical, can be discussed in an informal atmosphere. This allows you to raise any problems that you would like to have addressed and encourages peer-based learning and general group discussion.

We provide computing support with any specialised software required during the programme, for example, Matlab. The Faculty’s student common room is also covered by the University’s open-access wireless network, which makes it a very popular location for individual and group work using laptops and mobile devices.

Specialist experimental and research facilities, for computationally demanding projects or those requiring specialist equipment, are provided by the Centre for Vision, Speech and Signal Processing (CVSSP).

CAREER PROSPECTS

Computer vision specialists are be valuable in all industries that require intelligent processing and interpretation of image and video. This includes industries in directly related fields such as:
-Multimedia indexing and retrieval (Google, Microsoft, Apple)
-Motion capture (Foundry)
-Media production (BBC, Foundry)
-Medical Imaging (Siemens)
-Security and Defence (BAE, EADS, Qinetiq)
-Robotics (SSTL)

Studying for Msc degree in Computer Vision offers variety, challenge and stimulation. It is not just the introduction to a rewarding career, but also offers an intellectually demanding and exciting opportunity to break through boundaries in research.

Many of the most remarkable advancements in the past 60 years have only been possible through the curiosity and ingenuity of engineers. Our graduates have a consistently strong record of gaining employment with leading companies.

Employers value the skills and experience that enable our graduates to make a positive contribution in their jobs from day one.

Our graduates are employed by companies across the electronics, information technology and communications industries. Recent employers include:
-BAE Systems
-BT
-Philips
-Hewlett Packard
-Logica
-Lucent Technologies
-BBC
-Motorola
-NEC Technologies
-Nokia
-Nortel Networks
-Red Hat

INDUSTRIAL COLLABORATIONS

We draw on our industry experience to inform and enrich our teaching, bringing theoretical subjects to life. Our industrial collaborations include:
-Research and technology transfer projects with industrial partners such as the BBC, Foundry, LionHead and BAE
-A number of our academics offer MSc projects in collaboration with our industrial partners

RESEARCH PERSPECTIVES

This course gives an excellent preparation for continuing onto PhD studies in computer vision related domains.

GLOBAL OPPORTUNITIES

We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.

In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.

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See the department website - http://www.cis.rit.edu/graduate-programs/master-science. The master of science program in imaging science prepares students for positions in research in the imaging industry or in the application of various imaging modalities to problems in engineering and science. Read more
See the department website - http://www.cis.rit.edu/graduate-programs/master-science

The master of science program in imaging science prepares students for positions in research in the imaging industry or in the application of various imaging modalities to problems in engineering and science. Formal course work includes consideration of the physical properties of radiation-sensitive materials and processes, the applications of physical and geometrical optics to electro-optical systems, the mathematical evaluation of image forming systems, digital image processing, and the statistical characterization of noise and system performance. Technical electives may be selected from courses offered in imaging science, color science, engineering, computer science, science, and mathematics. Both thesis and project options are available. In general, full-time students are required to pursue the thesis option, with the project option targeted to part-time and online students who can demonstrate that they have sufficient practical experience through their professional activities.

Faculty within the Center for Imaging Science supervise thesis research in areas of the physical properties of radiation-sensitive materials and processes, digital image processing, remote sensing, nanoimaging, electro-optical instrumentation, vision, medical imaging, color imaging systems, and astronomical imaging. Interdisciplinary efforts are possible with other colleges across the university.

The program can be completed on a full- or a part-time basis. Some courses are available online, specifically in the areas of color science, remote sensing, medical imaging, and digital image processing.

Plan of study

All students must earn 30 credit hours as a graduate student. The curriculum is a combination of required core courses in imaging science, elective courses appropriate for the candidate’s background and interests, and either a research thesis or graduate paper/project. Students must enroll in either the research thesis or graduate paper/project option at the beginning of their studies.

Core courses

Students are required to complete the following core courses: Fourier Methods for Imaging (IMGS-616), Image Processing and Computer Vision (IMGS-682), Optics for Imaging (IMGS-633), and either Radiometry (IMGS-619) or The Human Visual System (IMGS-620).

Speciality track courses

Students choose two courses from a variety of tracks such as: digital image processing, medical imaging, electro-optical imaging systems, remote sensing, color imaging, optics, hard copy materials and processes, and nanoimaging. Tracks may be created for students interested in pursuing additional fields of study.

Research thesis option

The research thesis is based on experimental evidence obtained by the student in an appropriate field, as arranged between the student and their adviser. The minimum number of thesis credits required is four and may be fulfilled by experiments in the university’s laboratories. In some cases, the requirement may be fulfilled by work done in other laboratories or the student's place of employment, under the following conditions:

1. The results must be fully publishable.

2. The student’s adviser must be approved by the graduate program coordinator.

3. The thesis must be based on independent, original work, as it would be if the work were done in the university’s laboratories.

A student’s thesis committee is composed of a minimum of three people: the student’s adviser and two additional members who hold at least a master's dgeree in a field relevant to the student’s research. Two committee members must be from the graduate faculty of the center.

Graduate paper/project option

Students with demonstrated practical or research experience, approved by the graduate program coordinator, may choose the graduate project option (3 credit hours). This option takes the form of a systems project course. The graduate paper is normally performed during the final semester of study. Both part- and full-time students may choose this option, with the approval of the graduate program coordinator.

Admission requirements

To be considered for admission to the MS in imaging science, candidates must fulfill the following requirements:

- Hold a baccalaureate degree from an accredited institution (undergraduate studies should include the following: mathematics, through calculus and including differential equations; and a full year of calculus-based physics, including modern physics. It is assumed that students can write a common computer program),

- Submit a one- to two-page statement of educational objectives,

- Submit official transcripts (in English) of all previously completed undergraduate or graduate course work,

- Submit letters of recommendation from individuals familiar with the applicant’s academic or research capabilities,

- Submit scores from the Graduate Record Exam (GRE) (requirement may be waived for those not seeking funding from the Center for Imaging Science), and

- Complete a graduate application.

- International applicants whose native language is not English must submit scores from the Test of English as a Foreign Language. Minimum scores of 600 (paper-based) or 100 (Internet-based) are required. Students may also submit scores from the International English Language Testing System. The minimum IELTS score is 7.0. International students who are interested in applying for a teaching or research assistantship are advised to obtain as high a TOEFL or IELTS score as possible. These applicants also are encouraged to take the Test of Spoken English in order to be considered for financial assistance.

Applicants seeking financial assistance from the center must have all application documents submitted to the Office of Graduate Enrollment Services by January 15 for the next academic year.

Additional information

- Bridge courses

Applicants who lack adequate preparation may be required to complete bridge courses in mathematics or physics before matriculating with graduate status.

- Maximum time limit

University policy requires that graduate programs be completed within seven years of the student's initial registration for courses in the program. Bridge courses are excluded.

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The accredited Master of Science program in Computer Science is a two-year program that has been designed for international and German graduate students. Read more
The accredited Master of Science program in Computer Science is a two-year program that has been designed for international and German graduate students. The curriculum is very flexible. Students can compile their individual study plans based on their background and interests. It is also a very practical program. In addition to lectures and tutorials, students will complete two seminars, one or two projects and the master thesis.

In the beginning students will choose one or two key courses. Key courses are courses which introduce the students to the research areas represented at the Department of Computer Science. The following key courses are offered:

• Algorithm Theory
• Pattern Recognition
• Databases and Information Systems
• Software Engineering
• Artificial Intelligence
• Computer Architecture

After that, students can specialize in one of the following three areas:

• Cyber-Physical Systems
• Information Systems
• Cognitive Technical Systems

Here are some examples of subjects offered in the three specialization areas:

Cyber-Physical Systems:

• Cyber-Physical Systems – Discrete Models
• Cyber-Physical Systems – Hybrid Control
• Real Time Operation Systems and Reliability
• Verification of Embedded Systems
• Test and Reliability
• Decision Procedures
• Software Design, Modeling and Analysis in UML
• Formal Methods for Java
• Concurrency: Theory and Practice
• Compiler Construction
• Distributed Systems
• Constraint Satisfaction Problems
• Modal Logic
• Peer-to-Peer Networks
• Program Analysis
• Model Driven Engineering

Information Systems:

• Information Retrieval Data Models and Query Languages
• Peer-to-Peer Networks
• Distributed Storage
• Software Design, Modeling and Analysis in UML
• Security in Large-Scale Distributed Enterprises
• Machine Learning
• Efficient Route Planning
• Bioinformatics I
• Bioinformatics II
• Game Theory
• Knowledge Representation
• Distributed Systems

Cognitive Technical Systems:

• Computer Vision I
• Computer Vision II
• Statistical Pattern Recognition
• Mobile Robotics II
• Simulation in Computer Graphics
• Advanced Computer Graphics
• AI Planning
• Game Theory
• Knowledge Representation
• Constraint Satisfaction Problems
• Modal Logic
• Reinforcement Learning
• Machine Learning
• Mobile Robotics I

We believe that it is important for computer science students to get a basic knowledge in a field in which they might work after graduation. Therefore, our students have the opportunity to complete several courses and/or a project in one of the following application areas:

• Bioinformatics
• Educational Sciences
• Geosciences
• Cognitive Sciences
• Mathematics
• Medicine
• Meteorology
• Microsystems Engineering
• Physics
• Political Sciences
• Psychology
• Sociology
• Economics

In the last semester, students work on their master’s thesis. They are expected to tackle an actual research question in close cooperation with a professor and his/her staff.

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The Masters in Data Science provides you with a thorough grounding in the analysis and use of large data sets, together with experience of conducting a development project, preparing you for responsible positions in the Big Data and IT industries. Read more
The Masters in Data Science provides you with a thorough grounding in the analysis and use of large data sets, together with experience of conducting a development project, preparing you for responsible positions in the Big Data and IT industries. As well as studying a range of taught courses reflecting the state-of-the-art and the expertise of our internationally respected academic staff, you will undertake a significant programming team project, and develop your own skills in conducting a data science project.

Why this programme

◾The School of Computing Science is consistently highly ranked achieving 2nd in Scotland and 10th in the UK (Complete University Guide 2017)
◾The School is a member of the Scottish Informatics and Computer Science Alliance: SICSA. This collaboration of Scottish universities aims to develop Scotland's place as a world leader in Informatics and Computer Science research and education.
◾We currently have 15 funded places to offer to home and EU students.
◾You will have opportunities to meet employers who come to make recruitment presentations, and often seek to recruit our graduates during the programme.
◾You will benefit from having 24-hour access to a computer laboratory equipped with state-of-the-art hardware and software.

Programme structure

Modes of delivery of the MSc in Data Science include lectures, seminars and tutorials and allow students the opportunity to take part in lab, project and team work.

Core courses

◾Big data
◾Data fundementals
◾Information retrieval
◾Machine learning
◾Research methods and techniques
◾Text as data
◾Web science
◾Masters team project.

Optional courses

◾Advanced networking and communications
◾Advanced operating systems
◾Algorithmics
◾Artificial intelligence
◾Big data: systems, programming and management
◾Computer architecture
◾Computer vision methods and applications
◾Cryptography and secure development
◾Cyber security forensics
◾Cyber security fundamentals
◾Distributed algorithms and systems
◾Enterprise cyber security
◾Functional programming
◾Human computer interaction
◾Human computer interaction: design and evaluation
◾Human-centred security
◾Information retrieval
◾Internet technology
◾IT architecture
◾Machine learning
◾Mobile human computer interaction
◾Modelling reactive systems
◾Safety critical systems.
◾Software project management
◾Theory of Computation

Depending on staff availability, the optional courses listed here may change.

If you wish to engage in part-time study, please be aware that dependent upon your optional taught courses, you may still be expected to be on campus on most week days.

Industry links and employability

◾The advent of Big Data tools in recent years has facilitated the large-scale mining of voluminous data, to allow actionable knowledge and understanding, known as Data Science. For instance, search engines can gain insights into how ambiguous a query is according to the querying and clicking patterns of different users. Data Science combines a thorough background in Big Data processing techniques, combined with techniques from information retrieval and machine learning, to permit coherent and principled solutions allowing real insights and predictions to be obtained from data.
◾The programme includes a thorough grounding in professional software development, together with experience of conducting a development project. The programme will prepare you for a responsible position in the IT industry.
◾The School of Computing Science has extensive contacts with industrial partners who contribute to several of their taught courses, through active teaching, curriculum development, and panel discussion. Recent contributors include representatives from IBM, J.P. Morgan, Amazon, Adobe, Red Hat and Bing.
◾During the programme students have an opportunity to develop and practice relevant professional and transferrable skills, and to meet and learn from employers about working in the IT industry.

The Data Lab

We work closely with The Data Lab, an internationally leading research and innovation centre in data science. Established with an £11.3 million grant from the Scottish Funding Council, The Data Lab will enable industry, public sector and world-class university researchers to innovate and develop new data science capabilities in a collaborative environment. Its core mission is to generate significant economic, social and scientific value from data. Our students will benefit from a wide range of learning and networking events that connect leading organisations seeking business analytics skills with students looking for exciting opportunities in this field.

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The distance learning MSc in Clinical Visual Science has been developed to foster advanced theoretical knowledge in clinical issues within the field of vision science, with an emphasis on how research informs practice and the evolution of eye care services and capabilities. Read more
The distance learning MSc in Clinical Visual Science has been developed to foster advanced theoretical knowledge in clinical issues within the field of vision science, with an emphasis on how research informs practice and the evolution of eye care services and capabilities. The MSc course is organised and structured so you will have the opportunity to choose a particular area of study that interests you, with a balance of core modules to encourage scientific thinking and the critical review of concepts. Modules have been designed with contributions from guest experts from optometry, clinicians from hospital settings and other specialists. Self-motivation, independent learning, problem solving and developing and communicating scientific arguments are encouraged through a programme which emphasizes critical thinking. A research project will be undertaken by the student in an area of their choosing with support and guidance from an academic supervisor.

Graduates of this course will be equipped to evaluate and undertake research; they will have enhanced knowledge of aspects of advanced vision science. The graduate will be equipped with skills to undertake extended clinical roles, promote the development of eye care within their region and may provide a pathway for further research endeavours including doctoral research training.

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The Department of Health has introduced a new career pathway for Healthcare Scientists. This includes clinical science training to provide an appropriately trained workforce to work in the NHS. Read more
The Department of Health has introduced a new career pathway for Healthcare Scientists. This includes clinical science training to provide an appropriately trained workforce to work in the NHS.

DBS & Fitness to Practise

Students enrolling onto certain programmes within the School of Life and Health Sciences at Aston University will be required to undertake an Enhanced Level Disclosure and Barring Service check. Also in line with national requirements for programmes leading to a health professional qualification, a number of degree programmes in the School of Life and Health Science are subject to Fitness to Practise Regulations.

Course Outline & Modules

The Department of Health has introduced a new career pathway for Healthcare Scientists. This includes clinical science training to provide an appropriately trained workforce to work in the NHS. This requires the education and training of practitioners in the division of neurosensory science. This division is made up of 3 pathways:
-Audiology
-Neurophysiology
-Ophthalmic & Vision Sciences

Students undertake common learning throughout the programme but also have specialist topics in year two and three. Students are employed within NHS Departments and released to undertake academic study.

First year modules:
-NS1HS1 Introduction to Healthcare Science (15 credits)
-NS1PP1 Professional Practice (15 credits)
-NS1NS1 Introduction to Neurosensory Sciences (15 credits)
-NS1CS1 Clinical Science (15 credits)

Second year modules:
-NS2RM1 Research Methods (10 credits)
-NS2EB1 Evidence Based Practice (10 credits)
-NS2RP1 Research Project (20 credits)

Audiology pathway
-NS2AR1 Adult Rehabilitation (20 credits)

Vision Science pathway
-NS2VS1 Ophthalmic & Vision Science (20 credits)

Neurophysiology pathway
-NS2NE1 Evoked Potentials (20 credits)

Third year module:
Neurophysiology pathway
-Neurophysiology Practice (30 credits)

Learning, teaching & Assesment

The majority of the teaching and learning material is delivered via the virtual learning environment (Blackboard 9). Students are provided with a study guide for each module which ensures they are aware of what material needs covering when. The first year is mostly assessed via coursework so students are able to benchmark their abilities early on and to develop their skills in managing their learning.

In the second and third years of the programme students undertake a research project with an associated research methods module to develop their skills in this area.

Students are on campus for short periods each term when they have the opportunity to participate in group activities, tutorials, skills laboratories and seminars.

Professional accreditation

The programme is accredited by the Department of Health via the Modernising Scientific Careers programme.

Career opportunities

This programme sits within the Department of Health’s vision for the Healthcare Science workforce. The aim is to develop practitioners who can improve the scientific profile within healthcare and who have the requisite skills to enhance both the diagnosis and treatment of patients in the NHS. As such it is predominantly aimed at graduates employed within the Scientist Training Programme. It is also aimed at NHS practitioners within the disciplines who wish to undertake an academic qualification as part of their professional development. Individual modules can be undertaken as part of the School’s flexible credit accumulation system leading to a post-graduate certificate, diploma or masters qualification.

The ageing population means that demand for assessment and treatment services is set to rise substantially over the coming years. Our Graduates will be well placed to enter careers in hospitals, community-based practice and also related research areas. Previous graduates have become advanced practitioners or gone on to lead a section of service.

The programme is designed to formally meet the requirements of the NHS and builds on Aston’s established links and extensive experience of health education.

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Vision is the most useful sense we possess and as such accounts for about 30% of the sensing processing of the brain. Read more
Vision is the most useful sense we possess and as such accounts for about 30% of the sensing processing of the brain. The automation of visual processing (ie computer vision) has many applications in the modern world including medical imaging for better diagnosis, surveillance systems to improve security and safety, industrial and domestic robotics plus advanced interfaces for computer games, mobile phones and human-computer interfaces. The possibilities are only limited by our imagination.

Key features
-The unique combination of computer vision and embedded systems skills is highly desirable in state-of-the-art industrial applications.
-This course is accredited by BCS, The Chartered Institute for IT.
-You will have the opportunity to work on your project dissertation in the internationally recognised Digital Imaging Research Centre with groups on visual surveillance, human body motion, medical imaging and robotics and being involved in national and international projects or in collaboration with our industrial contacts.

What will you study?

The Embedded Systems (Computer Vision) pathway will equip you with the knowledge and skills required to specify and build computer vision embedded systems, choosing from different imaging devices and applying software that can process and understand images. You will study a range of option modules encompassing computing, engineering and digital media processing. It may also be possible for you to undertake a real-world project in an industrial placement or as part of high-quality research working alongside DIRC (Digital Imaging Research Centre) groups (eg visual surveillance, human body motion analysis, robotics, medical imaging).

The Embedded Systems (Computer Vision) MSc course can be combined with Management Studies enabling you to develop business and management skills so you can work effectively with business managers to develop innovative and imaginative ways to exploit computer vision and embedded systems for business advantage. This is a key skill for employability, particularly as organisations in the public, private and voluntary sectors grapple with austerity.

Assessment

Coursework and/or exams, research project/dissertation.

Work placement scheme

Kingston University has set up a scheme that allows postgraduate students in the Faculty of Science, Engineering and Computing to include a work placement element in their course starting from September 2017. The placement scheme is available for both international and home/EU students.

-The work placement, up to 12 months; is optional.
-The work placement takes place after postgraduate students have successfully completed the taught portion of their degree.
-The responsibility for finding the placement is with the student. We cannot guarantee the placement, just the opportunity to undertake it.
-As the work placement is an assessed part of the course for international students, this is covered by a student's tier 4 visa.

Details on how to apply will be confirmed shortly.

Course structure

The full MSc course consists of an induction programme, four taught modules, and project dissertation. Please note that this is an indicative list of modules and is not intended as a definitive list.

Embedded Systems (Computer Vision) MSc modules
-Digital Signal Processing
-Real-time Programming
-Artificial Vision Systems
-Project Dissertation
-One option module

Embedded Systems (Computer Vision) with Management Studies MSc modules
-Digital Signal Processing
-Real-time Programming
-Artificial Vision Systems
-Business in Practice
-Project Dissertation

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Demand is growing for high value data specialists across the sciences, medicine, arts and humanities. The aim of this unique, modular, online distance learning programme is to enhance existing career paths with an additional dimension in data science. Read more

Programme description

Demand is growing for high value data specialists across the sciences, medicine, arts and humanities. The aim of this unique, modular, online distance learning programme is to enhance existing career paths with an additional dimension in data science.

The programme is designed to fully equip tomorrow’s data professionals, offering different entry points into the world of data science – across the sciences, medicine, arts and humanities.

Students will develop a strong knowledge foundation of specific disciplines as well as direction in technology, concentrating on the practical application of data research in the real world.

You can study to an MSc, Postgraduate Diploma, Postgraduate Certificate or Postgraduate Professional Development level.

Online learning

Our online learning technology is fully interactive, award-winning and enables you to communicate with our highly qualified teaching staff from the comfort of your own home or workplace.

Our online students not only have access to the University of Edinburgh’s excellent resources, but also become part of a supportive online community, bringing together students and tutors from around the world.

Programme structure

You can study to an MSc, Postgraduate Diploma, Postgraduate Certificate or Postgraduate Professional Development level.

For the MSc programme, students must successfully complete a total of 180 credits: Practical Introduction to Data Science (20 credits), the Dissertation Project (60 credits) plus 100 credits from the list of courses below.

For the MSc with specialism in Medical Informatics, students must successfully complete a total of 180 credits: Medical Informatics (10 credits), Research and Evaluation in eHealth (10 credits), the Dissertation Project (60 credits) plus 100 credits from the list of courses below. Students wishing to study the MSc with specialism in Medical Informatics should apply for the standard MSc in Data Science, Technology and Innovation and contact the Programme Administrator to discuss the specialism.

For the Postgraduate Diploma (PG Dip), students must successfully complete a total of 120 credits: Practical Introduction to Data Science (20 credits) plus 100 credits from the list of courses below.

For the Postgraduate Certificate (PgCert), students must successfully complete a total of 60 credits: Practical Introduction to Data Science (20 credits) plus 40 credits from the list of courses below.

For the Postgraduate Professional Development (PPD), students may take a maximum of 50 credits from the list of courses below. These credits will be recognised in their own right for postgraduate level credits or may be put towards gaining a higher award such as a PgCert.

Option courses

Some option courses may be compulsory for a specific programme; please refer to the information above.

Advanced Vision (10 credits)
Engaging with Digital Research (10 credits)
Ethics and Governance of eHealth (10 credits)
Introduction to Clinical Trials (10 credits)
Introduction to Health Informatics 1 (10 credits)
Introduction to Health Informatics 2 (10 credits)
Introduction to Vision and Robotics (10 credits)
Machine Learning (10 credits)
Managing Digital Influence (10 credits)
Medical Informatics (10 credits)
Neuroimaging: Common Image Processing Techniques 1 (20 credits)
Neuroimaging: Common Image Processing Techniques 2 (10 credits)
Practical Introduction to Data Science (20 credits)
Practical Introduction to High Performance Computing (20 credits)
Public Health Informatics (10 credits)
Research and Evaluation in eHealth (10 credits) (restricted to the MSc and MSc with Medical Informatics programmes)
Social Shaping of Digital Research (10 credits)
Technologies of Civic Participation (10 credits)
Telemedicine and Telehealth (10 credits)
The Use and Evolution of Digital Data Analysis and Collection Tools (10 credits)
Understanding Data Visualisation (10 credits)
User Centred Design in eHealth (10 credits)
Dissertation project – all Masters

(We recommend you take Introduction to Vision and Robotics before or simultaneously taking Advanced Vision, or have some previous experience with image processing.)

Learning outcomes

The modular course structure offers broad engagement at different career stages. Individual courses provide an understanding of modern data-intensive approaches while the programme provides the knowledge base to develop a career that majors in data science in an applied domain.

Career opportunities

This programme is intended for professionals wishing to develop an awareness of applications and implications of data intensive systems. Our aim is to enhance existing career paths with an additional dimension in data science, through new technological skills and/or better ability to engage with data in target domains of application.

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The Masters in Computing Science provides you with a thorough grounding in advanced computing science, together with experience of conducting a development project, preparing you for responsible positions in the IT industry. Read more
The Masters in Computing Science provides you with a thorough grounding in advanced computing science, together with experience of conducting a development project, preparing you for responsible positions in the IT industry.

Why this programme

◾The School of Computing Science is consistently highly ranked achieving 2nd in Scotland and 10th in the UK (Complete University Guide 2017)
◾The School is a member of the Scottish Informatics and Computer Science Alliance: SICSA. This collaboration of Scottish universities aims to develop Scotland's place as a world leader in Informatics and Computer Science research and education.
◾You will have opportunities to meet employers who come to make recruitment presentations, and often seek to recruit our graduates during the programme.
◾You will benefit from having 24-hour access to a computer laboratory equipped with state-of-the-art hardware and software.
◾With a 92% overall student satisfaction in the National Student Survey 2015, computing at Glasgow continues to meet student expectations combining both teaching excellence and a supportive learning environment.

Programme structure

Modes of delivery of the MSc in Computing Science include lectures, seminars and tutorials and allow students the opportunity to take part in lab, project and team work.

Core courses
◾Research methods and techniques
◾Masters team project

Optional courses
◾Advanced networking and communications
◾Advanced operating systems
◾Algorithmics
◾Artificial intelligence
◾Big data: systems, programming and management
◾Computer architecture
◾Computer vision methods and applications
◾Cryptography and secure development
◾Cyber security forensics
◾Cyber security fundamentals
◾Distributed algorithms and systems
◾Enterprise cyber security
◾Functional programming
◾Human computer interaction
◾Human computer interaction: design and evaluation
◾Human-centred security
◾Information retrieval
◾Internet technology
◾IT architecture
◾Machine learning
◾Mobile human computer interaction
◾Modelling reactive systems
◾Safety critical systems.
◾Software project management
◾Theory of Computation
◾Web Science

Depending on staff availability, the optional courses listed here may change.

If you wish to engage in part-time study, please be aware that dependent upon your optional taught courses, you may still be expected to be on campus on most week days.

Accreditation

MSc Computing Science is accredited by the British Computer Society (BCS) and the Institution of Engineering & Technology (IET)

Our specialist MSc graduates in Computing Science, Software Engineering and Information Security are recognised by the British Computer Society (BCS), The Chartered Institute for IT, for the purposes of fully meeting the further learning academic requirement for registration as a Chartered IT Professional (CITP Further Learning) and partially meeting the academic requirement for registration as a Chartered Scientist (CSci). These programmes have also been awarded the Euro-Info Master Label.

[[Industry links and employability ]]

◾The School of Computing Science has extensive contacts with industrial partners who contribute to several of their taught courses, through active teaching, curriculum development, and panel discussion. Recent contributors include representatives from IBM, J.P. Morgan, Amazon, Adobe and Red Hat.
◾Employers are interested in graduates who have a combination of good technical skills and well-developed personal skills, and in this respect graduates of the MSc in Computing Science from the University of Glasgow are particularly well placed.
◾During the programme students have an opportunity to develop and practice relevant professional and transferrable skills, and to meet and learn from employers about working in the IT industry.

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Computer vision and imaging is the exciting science and technology of machines that see, concerned with building artificial systems that obtain information from images that are derived from a range of sources. Read more
Computer vision and imaging is the exciting science and technology of machines that see, concerned with building artificial systems that obtain information from images that are derived from a range of sources. This MSc in Computing with Vision and Imaging teaches you the skills necessary to undertake work in this ever-evolving field.

Why study at Dundee?

Computer vision and imaging is a rapidly expanding field with plenty of real-life applications and opportunities. Here at Dundee, we encourage a professional, inter-disciplinary and user-centred approach to computer systems design and production.

Application areas include:
controlling processes - e.g. an industrial robot or an autonomous vehicle
detecting events - e.g. for visual surveillance or people counting
organising information - e.g. for indexing databases of images and image sequences
modelling objects or environments - e.g. for industrial inspection
medical image analysis
topographical modelling

You will acquire skills in computer vision, inference, algorithmic underpinnings of computer vision systems, how images and signals are formed, filter, compressed and analysed, and how multiple images can be combined.

Throughout this course, you will also develop the necessary skills to undertake independent research and participate in proposal development and innovation - an excellent grounding for many future careers.

What's Great about studying at Dundee?

Research-led teaching:
Teaching at Dundee is research-led, meaning that the MSc programme benefits from association with cutting-edge research of international standard and its commercial applications.

We also have an active Computer Vision and Image Processing research group. Our Vision and Imaging students are involved in a number of http://www.computing.dundee.ac.uk/projects/vision/projects.php, and have been involved with a number of completed research projects like ACTIVE, a project concerning adaptive interfaces for the operation of secondary controls in motor vehicles using pointing gestures and virtual dashboards.

Links with industry

The School of Computing collaborates with, and has links to, companies such as IBM, NCR and Oracle.

Our facilities

You will have 24-hour access to our award winning and purpose-built Queen Mother Building. It has an unusual mixture of lab space and breakout areas, with a range of conventional and special equipment for you to use. It's also easy to work on your own laptop as there is wireless access throughout the building. Our close ties to industry allows us access to facilities such as Windows Azure and Teradata, and university and industry standard software such as Tableau for you to evaluate and use.

Postgraduate culture

The School of Computing maintains a friendly, intimate and supportive atmosphere, and we take pride in the fact that we know all of our students - you're far more than just a matriculation number to us. We have a thriving postgraduate department with regular seminars and guest speakers.

What you will study

You select seven taught modules, three per semester, during the period September-April. You will make module selections with your advisor.

Semester 1 (Sept-Dec):
Probabilistic Inference and Learning
Signals and Images

Plus two from:
Technology Innovation Management
Computer Graphics
Logical Inference & Symbolic Reasoning
Information Theory

Semester 2 (Jan-Mar):
Vision and Perception
Research Methods

Plus one from:
Computing Research Frontiers
Multi-agent Systems & Grid Computing

Subject to examination performance, you then progress to the MSc project which runs from May to September, or to a Diploma project lasting 9 weeks.

Please note that some of the modules in the programme are shared with other masters programmes and some of the teaching and resources may be shared with our BSc programme. These joint classes offer a valuable opportunity to learn from, and discuss the material with, other groups of students with different backgrounds and perspectives.

How you will be assessed

The taught modules are assessed by continuous assessment plus end of semester examinations in December and March/April. The project is assessed by dissertation.

Computing coursework is often very practical, e.g. writing computer programs, designing interfaces, writing reports, constructing web sites, testing software, implementing databases, analysing problems or presenting solutions to clients.

Careers

The knowledge, skills and understanding that you will gain in the areas of computer vision, inference and learning will enable you to work effectively in the application of video and image-based computing - whether you choose industry, commerce or research.

Computing at the University of Dundee is ranked 21st in the UK according to most recent Times Good University Guide and 12th in the UK according to the Guardian University League Table 2009. The University of Dundee has powered its way to a position as one of Scotland's leading universities with an international reputation for excellence across a range of activities. With over 18,000 students, it is growing fast in both size and reputation. It has performed extremely well in both teaching and research assessment exercises, has spawned a range of spin-out companies to exploit its research and has a model wider-access programme.

Dundee has been described as the largest village in Scotland which gives an indication of how friendly and compact it is. With a population of 150,000 it is not too large but has virtually all the cultural and leisure activities you would expect in a much larger city. It is situated beside a broad estuary of the river Tay, surrounded by hills and farmland, and for lovers of the great outdoors it is hard to imagine another UK location that offers so much all year round on land and water. The University is situated in the centre of Dundee, and everything needed is on the one-stop campus: study facilities, help, advice, leisure activities... yet the attractions of the city centre and the cultural quarter are just a stroll away.

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