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Masters Degrees (Data Visualisation)

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As a Data Visualisation Designer you can contribute innovative solutions with the potential to transform societal challenges, by designing the human interface to increasingly complex problems. Read more

Why take this course?

As a Data Visualisation Designer you can contribute innovative solutions with the potential to transform societal challenges, by designing the human interface to increasingly complex problems.

On this course, you will learn how to create rich and meaningful stories with data. We will study digital content in any mode, whether it is in alphanumeric form, binary, vector, pixel, video, or others. The designer provides an important interface, that allows us to explore data and generates meaningful communication. This communication is predominantly visual, but with developments in Wearables and the Internet of Things, is also becoming increasingly physical, affective, networked and interactive. Data Visualisation Design spans traditional graphic and information design, interaction design, information architecture, computational design, design thinking and user-centred and user experience design.

What will I experience?

On this course you can:

Learn the theory and practice of data visualisation, data, interface/interaction design and user experience, and apply this to your own design
Critically question the role of data related to the social, political, economic and cultural through contextual research
Explore live data sets from real world scenarios, such as industry or charities like the digital humanitarian network
Develop independent research and project ideas to create innovative, forward thinking design solutions and experiences for a digital and data driven world

What opportunities might it lead to?

The course will prepare you to work in the design disciplines of the creative industries, with a focus on data visualisation, information design, computational design, digital content, interactivity and user experience. Data Visualisation designers are in demand in sectors including business, research, health, education, government/public service, the arts.

The skills gained on this course can also be applied to employment in UI (user interface) design, or focus on interaction as a UX (User experience) designer. The critical and contextual outlook allows you to position yourself as a strategist and operate in a consultative manner. The research aspect of the course would also suit a career in compulsory, further and higher education.

Careers include:

Data Visualisation Design
Information Design
Digital Graphic Design
UI (user interface) / UX (user experience) design
Interaction design

Module Details

The course is offered over one year (full-time) or two years (part-time).

You will study five units, one of which is shared with other MA courses in the School of Art and Design. There will be preparatory units delivering a grounding in practical skills, theoretical context and academic research (competencies and skills). You will also study units that allow more thematic engagement with interactive and data driven design in terms of theory such as critical design, affordances, experience and complexity. It will also provide a unit oriented towards employability, and incorporate live briefs and group work. These units work to catalyse your own ideas and research direction for the Major Project unit.

Core units currently comprise:

A Question of Research
Fundamentals of Data and Interaction Design
Digital Futures – Themes and Issues in Practice
Design Solutions for Enterprise, Society and Culture
Major Project

Programme Details

The teaching combines interactive lectures and group seminar discussions with support through one-to-one tutorials. You also receive feedback on your work through friendly but critical peer review in group sessions with other students, members of faculty and other experts as appropriate. One of the units includes working as a team. Your project work emphasises self-initiated learning which gives you the freedom to explore the specialist area of your interest, while being helpfully guided by your supervisor. The curriculum is very closely related to the research areas in the department, so the staff have cutting edge knowledge of the field and its potential for innovation.

Your learning is mostly assessed through the submission of practical course work, such as digital prototypes, and the documentation of the learning journey in sketchbooks, diaries, blogs or journals.

This will be documenting contextual research as well as stages in practical experimentation and annotation of reflection. There are some written elements to be submitted as well, mostly accompanying proposals/reports to contextualise your practice. The assessment also includes individual and group presentations, this mode is also used to give you formative feedback on your work throughout.

Here's how we assess your work:

Digital artefacts / prototypes
Learning journals
Proposals
Reports
Oral presentation

Student Destinations

This course is an opportunity to focus your creative design practice on the interactive, data driven, user centred and culturally contextualised. It also enhances your design career by upgrading your skills and widening your knowledge and thinking in the digital arena, allowing you to stay one step ahead of the rest. The independent research aspect of the course prepares you for further education in terms of a research degree and employment in R&D and/or education.

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This Postgraduate Certificate course in Data Visualisation and Modelling provides graduates with a comprehensive understanding of the mathematical, statistical and data visualisation techniques needed to investigate problems in a wide range of applications. Read more
This Postgraduate Certificate course in Data Visualisation and Modelling provides graduates with a comprehensive understanding of the mathematical, statistical and data visualisation techniques needed to investigate problems in a wide range of applications.

With recent developments in digital technology, society has entered the era of ‘Big Data’. However, the explosion and wealth of available data gives rise to new challenges and opportunities in all disciplines – from science and engineering to biology and business.

A major focus is on the need to take advantage of an unprecedented volume of data in order to acquire further insights and knowledge.

The flexibility of this course makes it particularly suitable for students in employment.

See the website http://www.brookes.ac.uk/courses/postgraduate/data-visualisation-and-modelling/

Why choose this course?

- A flexible approach to study enables participants to complete the Postgraduate Certificate course in between 1 and 5 years (part-time).

- Use of SPSS.

- A course designed to increase employability in a high-demand field of work.

- Develop your critical skills in the application of visualisation techniques for understanding and presenting the results of analysis.

- Join a supportive and close-knit community of teachers, support staff and learners.

This course in detail

Advanced Statistical Modelling - This module introduces a broad class of linear and non-linear statistical models and the principles of statistical inference to a variety of commonly encountered data analysis problems. The software package SPSS will be used as a tool for statistical analysis with the goal of enabling students to develop their critical thinking and analytical skills. The emphasis, however, is very much on the practical aspect of the methodology and techniques with the theoretical basis kept at a minimum level.

Modelling and Data Analysis using MATLAB - This module gives depth of knowledge in advanced modelling techniques and breadth of analysis by virtue of its general application to any field of engineering and data analysis. In this module students learn to build computer models, present and analyse data using the facilities of MATLAB. Some mathematics is taught as relevant to data interpolation, optimisation and/or choosing solvers for models featuring differential equations.

Data Visualisation and Applications - This module provides a general but broad grounding in the principles of data visualisation and its applications. It covers an introduction to perception and the human visual system, design and evaluation of visualisation techniques, analysing, organising and presenting information visually, using appropriate techniques and visualisation systems.

Teaching and learning

The programme follows a supportive teaching and learning strategy based on active student engagement.

Modules offer a variety of teaching methods, and feature a selection of critical appraisal reports, the use of software applications for data analysis, presentations and case studies.

Learning methods include blended learning, formal lectures and problem solving practicals, but also guided independent learning, use of the virtual learning environment Moodle, independent research, software data analyses, and experiments.

Approach to assessment

Due to the data analysis and the interpretive nature of the course content, the high level industrial participation, and the authentic nature of the assessment, all modules are assed entirely by coursework which includes in-class tests. The assessment regime is selected according to what is appropriate for the material covered.

Attendance pattern

Students will study one twelve-week module per semester, attending campus one day per week for six weeks for each module. A typical module delivery structure is as follows.
- Face to face lectures will take place in weeks 2-5. Each face to face session is three hours, and there will be two face-to-face sessions per day.

- A two-hour class test and individual discussion of mini-projects will take place in week 6.

- An online surgery is available to support guided self-study in weeks 7-11.

- E-learning materials will be available throughout the semester as required on Moodle.

- Weekly exercises for formative feedback will be submitted into a drop box for each module.

- Mini-projects will be due at the end of week 12.

Careers

Currently, global demand for combined statistical, mathematics and computing expertise outstrips supply, with evidence-based predictions suggesting a major shortage in this area for at least the next 10 years.

For graduates in data visualisation and modelling this shortage presents opportunities to enhance career progression in one of the most crucial areas of modern science.

Free language courses for students - the Open Module

Free language courses are available to full-time undergraduate and postgraduate students on many of our courses, and can be taken as a credit on some courses.

Please note that the free language courses are not available if you are:
- studying at a Brookes partner college
- studying on any of our teacher education courses or postgraduate education courses.

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There has been a recent upsurge in commercial interest in the new role of "data scientist". A data scientist is a person who excels at manipulating and analysing data, particularly large data sets that don't fit easily into tabular structures (so-called "Big Data"). Read more
There has been a recent upsurge in commercial interest in the new role of "data scientist". A data scientist is a person who excels at manipulating and analysing data, particularly large data sets that don't fit easily into tabular structures (so-called "Big Data").

Why study Data Science at Dundee?

The School of Computing has been working on 'big data' and data analysis for at least five years; not only working with data but also developing new algorithms and techniques for data scientists. The School already runs the most successful Business Intelligence Masters course in the UK.

This course will be led by Professor Mark Whitehorn and Andy Cobley. Mark is an emeritus professor at the University of Dundee and also runs a successful consultancy company that specialises in BI, Data Sciences and analytics. Andy is the course organiser for both the existing BI course and the new Data Science course.

This course will enhance your employability by providing you with knowledge, skills and understanding of data science research and implementation. You will also acquire skills in the professional procedures necessary to ensure that data science research and implementation is both valid and actionable and engage with contemporary debate about the role, ethics and utility of data science in commercial and other settings.

What is the difference between Data Science and Business Intelligence?

There is clearly a huge overlap with Business Intelligence. A BI specialist will need to understand data and data analytics. However there is a bias towards understanding how data is stored in the current operational systems within an enterprise the design and the implementation of an analytical system such as a data warehouse. A data scientist will be less concerned with the construction of a data warehouse and more interested in the message the specific sets of data can deliver.

However, without some understanding of data warehouses the data scientist will find it difficult to interrogate the data for its secrets. For this reason there is overlap between the two courses.

If you already have a strong grounding in Business Intelligence and would like to upgrade your knowledge to include topics from the Data Science MSc, we offer the relevant Data Science modules either on a stand alone basis or as a PGCert.

What's so good about Data Science at Dundee?

Our facilities will give you 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.

A booming Postgraduate culture where 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.

Duncan Ross (Director of Data Sciences at Teradata) has said that: "The first and most important trait is curiosity. Insane curiosity. In many walks of life evolution selects against the kind of person who decides to find out what happens 'if I push that button'. Data Science selects for it."

How you will be taught

The programme will be delivered by Prof. Mark Whitehorn with input from Andy Cobley, Yasmeen Ahmad, Chris Hillman and other specialists from within the School of Computing in an innovative blend of live co-presented master-classes, video seminars and recorded materials. A series of guest speakers from industry will provide case studies across both semesters.

The programme will be provided predominantly on-campus, with two intensive study weeks in each of the semesters. Other classes may be taken off-campus using the university’s VLE, remote desktop, Adobe Connect and video conferencing systems along with telephone conferencing.

What you will study

Semester 1
Big Data - 20 Credits
Business Intelligent Systems - 20 Credits
Data Analysis and Visualisation - 20 Credits

Semester 2
Analytical Database Models and Design - 20 Credits
Advanced statistics and data mining - 20 credits
MDX - 20 Credits

Semester 3
Data Science Mini Project - 20 credits (for Certificate)
Data Science Research Project - 60 credits

PGCert:
The PGCert is intended for students who have a strong grounding in Business Intelligence and would like to upgrade their knowledge to include topics from the Data Science MSc. The modules are available stand alone for those who want to take their time studying the material and perhaps build up to a PGCert.

The three modules that make up the PGCert are:
Big Data
Advanced Anlaysis
Mini Project

For more information about the content of the course, please visit the course webpage on the School of Computing website.

How you will be assessed

Assessment will be by examination, practical coursework and research project.

Careers

Various job sites now report an increase in jobs carrying the title of data scientist. Other career opportunities are in intelligence analysis, data management/database maintenance, data processing manager, database development and research, business intelligence consultant and more.

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What's the "sexiest job of the 21st century"? According to Harvard Business Review, it's data scientist. A job devoted to giving structure to large quantities of formless data. Read more
What's the "sexiest job of the 21st century"? According to Harvard Business Review, it's data scientist. A job devoted to giving structure to large quantities of formless data. Ever-changing, ever-challenging big data.

The Master of Data Science (MDS) teaches you how to explore data and discover its potential – how to find innovative solutions to real problems in science, business and government, from technology start-ups to global organisations.With a degree in science, engineering, arts or computing, you can pursue a Master of Data Science, gaining skills in data management, data analytics and data processing – skills needed in this fast-growing field.

The MDS expands your knowledge of the analytical, organisational and computational aspects of data. You learn to manage data and gain an understanding of its impact on society.

The MDS caters to students from a variety of backgrounds by including foundation units in programming, databases and maths or statistics. However, if you have this background from previous studies or work experience, you may accelerate your study with an exemption from these units, or choose to take more data science electives.

The core coursework covers data science objectives, data analysis and data management. You then select data science electives such as applied data analysis, visualisation, data pre-processing, big data handling and data in society. You can also choose to take the Advanced Data Analytics stream where you build deeper skills in data analytics and machine learning.

Our highly regarded faculty takes great pride in developing the most up-to-date material while maintaining a solid core of established theory and platforms, including Python and R (two of the most popular open-source programming languages for data analysis), Hadoop and Spark (for distributed processing). You also gain hands-on experience with state-of-the-art tools and get exposure to key industry players.

In your final semester, you may take part in an Industry Experience team project, working with industry mentors to develop data-driven IT solutions. Or you may undertake a minor-thesis research project, investigating cutting-edge problems under the supervision of internationally recognised researchers.

Visit the website http://www.study.monash/courses/find-a-course/2016/data-science-c6004?domestic=true

Course Structure

The course is structured in three parts, A, B and C. All students complete Part B (core studies). Depending upon prior qualifications, you may receive credit for Part A (foundation studies) or Part C (advanced studies) or a combination of the two.

Note that if you are eligible for credit for prior studies you may elect not to receive the credit.

PART A. Foundations for advanced data science studies
These studies will provide an orientation to the field of data science at graduate level. They are intended for students whose previous qualification is not in a cognate field.

PART B. Core Master's study
These studies draw on best practices within the broad realm of data science practice and research. You will gain a critical understanding of theoretical and practical issues relating to data science. Your study will focus on your choice either of data science or advanced data analytics.

PART C. Advanced practice
The focus of these studies is professional or scholarly work that can contribute to a portfolio of professional development. You have two options.

The first option is a program of coursework involving advanced study and an Industry experience studio project.

The second option is a research pathway including a thesis. Students wishing to use this Masters course as a pathway to a higher degree by research should take this second option.

Students admitted to the course, who have a recognised honours degree in a discipline cognate to data science, will receive credit for Part C, however, should they wish to complete a 24 point research project as part of the course they should consult with the course coordinator.

For more information visit the faculty website - http://www.study.monash/media/links/faculty-websites/information-technology

Find out how to apply here - http://www.study.monash/courses/find-a-course/2016/data-science-c6004?domestic=true#making-the-application

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The world is awash with data and much more is on the way, creating a tidal wave of Big Data. Data Engineers develop the infrastructure to store, manage, analyse this wave of data, to bridge the gap between Data and Computer Science. Read more
The world is awash with data and much more is on the way, creating a tidal wave of Big Data. Data Engineers develop the infrastructure to store, manage, analyse this wave of data, to bridge the gap between Data and Computer Science. This unique course will give you the skills you’ll need to succeed as a Data Engineer.

Why study Data Engineering at Dundee?

The role of “Data Scientist” has been described as the “sexiest job of the 21st Century. However, there is a emerging a new role, that of Data Engineer as more companies are realising they need employees with specific skills to handle the amount of data that is being generated and the coming tidal wave from the Internet of Things.

This MSc has been created with industry input to prepare its students with the skills to handle this wave of data and to be at the forefront of its exploitation. Students on the sister programmes (“Data Science” and “Business Intelligence”) have gone on to work for some of the biggest companies in the industry and we are confident that graduates from this MSc will have the same success.

The School of Computing at the University of Dundee has been successfully offering related MSc programmes such as Business Intelligence and Data Science since 2010. These innovative programmes attract around 40 students per year, drawn from across Europe and Overseas.

What's so good about Data Engineering at Dundee?

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.

Special features

The University of Dundee has close ties with the Big Data industry, including Teradata, Datastax and Microsoft. We have worked with SAS, Outplay, Tag, GFI Max, BrightSolid and BIPB, and our students have enjoyed guest lectures from Big Data users such as O2, Sainsbury’s, M&S and IBM.

You will be able to work with a range of leading researchers and tutors, including top vision and imaging researchers and BI experts. Our honorary staff include legal experts, entrepreneurs and renowned industry experts such as John Richards of the newly formed IBM Watson Group.

How you will be taught

The course will be taught by staff of the School of Computing. Depending on the modules you take this will include Andy Cobley, Professor Mark Whitehorn, and Professor Stephen McKenna.

What you will study

The course will be taught in 20 credit modules with a 60 credit dissertation. Students will require to complete 180 credits for the award of the MSc (including 60 credits for the dissertation). Students completing 120 credits (without the dissertation) will be eligible for a Postgraduate Diploma.

Course content

Each module on the course is designed to give the student the skills and understanding they need to succeed in the Data Engineering/ Science field. Content on the course includes (but is not limited to):

CAP theorem
Lamda Architecture
Cassandra, Neo4j and other nosql databases
The Storm distributed real time computation system
Hadoop, HDFS, MapReduce, and other Hadoop/SQL technologies
Spark and Shark frameworks
Data Engineering languages such as Python, erlang, R, Matlab
Vision systems, which are becoming increasingly important in data engineering for extracting features from large quantities of images such as from traffic, medical and industrial
RDBMS systems which will continue to play an important role in data handing and storage. You will be expected to research the history of RDMBS and delve in to the internals of modern systems
OLAP cubes and Business Intelligence systems, which can be the best and quickest way to extract information from data stores
Goals of machine learning and data mining
Clustering: K-means, mixture models, hierarchical
Dimensionality reduction and visualisation
Inference: Bayes, MCMC
Perceptrons, logistic regression, neural networks
Max-margin methods (SVMs)
Mining association rules
Bayesian networks

How you will be assessed

The course is assessed through a combination of examinations, coursework, presentations and interviews. Each module is different: for instance the Big data module has 40% coursework, consisting of Erlang programming and a presentation on nosql databases, along with an examination worth 60%.

Careers

Our experience suggests that graduates of this course will have most impact in the following areas:

Cloud and web based industries that handle large volumes of fast moving data that need to be stored, analysed and maintained. Examples include the publishing industry (paper, TV and internet), messaging services, data aggregators and advertising services

Internet of Things. A large amount of data is being generated by devices (robotic assembly lines, home power management, sensors etc.) all of which needs to be stored and analysed.

Health. The NHS (and others) are starting to store and analyse patient data on an unprecedented scale. The healthcare industry is also combining data sources from a large number of databases to improve patient well-being and health outcomes

Games industry. The games industry records an extraordinary amount of data about its customers' play activities, all of which needs to be stored and analysed. This course will equip students with the knowledge and skill to engage with the industry.

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Have you ever wanted to ‘Mung’ data? Apply Machine Learning techniques? Search for hidden patterns? Be part of Big Data?. This course is your opportunity to specialize as a Data Scientist, one of the most in demand roles across all sectors including health, retail, and energy. Read more
Have you ever wanted to ‘Mung’ data? Apply Machine Learning techniques? Search for hidden patterns? Be part of Big Data?

This course is your opportunity to specialize as a Data Scientist, one of the most in demand roles across all sectors including health, retail, and energy. Companies such as Google and Microsoft, and also public organisations such as the NHS are struggling to fill their vacancies in this field due to a lack of suitably qualified people. This course is unique in the UK in that it has been developed as a MSc conversion course – if you have a good honours degree in any discipline with a demonstrable mathematical aptitude, an enquiring mind, a practical and analytical approach to problem solving, and an ambition for a career in data science; then this course is for you.

Key benefits

• We welcome applications from students who may not have formal/traditional entry criteria but who have relevant experience or the ability to pursue the course successfully.

• The Accreditation of Prior Learning (APL) process could help you to make your work and life experience count. The APL process can be used for entry onto courses or to give you exemptions from parts of your course.

• Two forms of APL may be used for entry: the Accreditation of Prior Certificated Learning (APCL) or the Accreditation of Prior Experiential Learning (APEL).

Visit the website: http://www.salford.ac.uk/pgt-courses/msc-data-science

Course detail

During your time with us, you will develop an awareness of the latest developments in the fields of Data Science and Big Data including advanced databases, data mining and big data tools such as Hadoop. You will also gain substantial knowledge and skills with the SAS business intelligence software suite due to the partnership of the University with the SAS Student Academy.

This course covers a very comprehensive range of topics split in to four large modules worth 30 credits each plus the MSc Project. External speakers from blue-chip and local companies will give seminars to complement your learning, that will be real-world case studies related to the subjects you are studying in your modules. These are designed to improve the breadth of your learning and could lead to ideas that you can develop for your MSc Project.

Suitable For

Students who want to become trained professionals in:

• Data Science and Analysis Consultancy
• Implementing and designing Big Data platforms ie Data Warehouses, Hadoop, NoSQL databases
• Modelling and Visualisation of data

Format

The course is focused around the underpinning knowledge and practical skills needed for employment within the data sciences industry. There will be 22 hours of lectures; 11 hours of tutorials and 22 hours workshops; 2 hours of examination-based assessment; and 245 hours of independent study, assessed coursework and preparation for examination. This makes a total of 300 hours total learning experience.

• Lectures will be used to introduce ideas, and to stimulate group discussions.
• Tutorials will be used to develop problem solving strategies and to provide practice and feedback with scenarios to help with exam preparation.
• Workshops will be used to develop expertise in SAS tools, by analysing example datasets of increasing complexity.

Modules

• Principles of Data Science
• Advanced Databases
• Applied Statistics and Data Mining
• Big Data Tools and Techniques

Assessment

• 50% of the assessment will comprise a practical project where students will be given some data, will devise and carry out an analysis strategy and will present their interpretations and explain their strategy.
• 50% will comprise an examination, which will assess more theoretical aspects of the course and will explore students’ immediate response to unseen scenarios or data.

Career Prospects

A recent report by e-Skills and SAS (Big Data Analytics: An assessment of the demand for labour and skills, 2012-1017) indicates the demand forecast for staff with big data skills is predicted to ”rise by 92% between 2012 and 2017, and by 2017 there will be at least 28,000 job openings for big data staff in the UK each year…”

With this qualification, you’ll be equipped with the skill set and technical knowledge relevant for the data science and big data job market.

How to apply: http://www.salford.ac.uk/study/postgraduate/applying

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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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This course is suitable for numerate graduates across many disciplines. Non-computing graduates are eligible. You will learn how to use state-of-the-art computer science methods to process a range of data, including (but not limited to) big data. Read more

Description

This course is suitable for numerate graduates across many disciplines. Non-computing graduates are eligible.

You will learn how to use state-of-the-art computer science methods to process a range of data, including (but not limited to) big data. You will develop technical, statistical, analytical and data mining skills. The course incorporates methods of statistical analysis and data mining to extract understanding from data, formulate high-quality data models and interpret them to ‘tell a story’. You will learn how to communicate these effectively to stakeholders, bearing in mind ethical, legal and societal implications.

You will be introduced to data science concepts, techniques and algorithms for processing and visualising datasets so as to infer useful, actionable knowledge.

Core units

- Computational Statistics and Visualisation
- Data Management and Machine Learning
- Data Science Project
- High Performance Computing and Big Data
- Introduction to Data Science

Career prospects

There has been a 35% increase in UK demand for Data Scientists between 2012 and 2015. Britain is expected to create an average of 56,000 new big data jobs per year until 2020 and McKinsey and Company reports that by 2018, there will be 140,000-190,000 job postings by Companies that they are unable to fill due to the lack of expertise. The range of job roles envisaged for graduates from this degree include, but are not limited to:

- Data Scientist
- Data warehouse analyst
- Data architect / modeller
- Database developer
- Data Governor
- Big Data Analyst
- Decision Sciences Analyst

Careers support is available from the moment you join us, throughout your time here, and for up to three years after the completion of your course. We have a range of services available through the School of Computing, Mathematics and Digital Technology and the University Careers Service including dedicated careers and employability advisors.
Professional Accreditation

The School is an educational affiliate of the British Computing Society – the Chartered Institute for IT in the UK (BCS), a member of the Oracle Academy and an Academy for the Computer Technology Industry Association (CompTIA). Many of the School’s degree programmes are accredited by BCS.

The School is also an academic partner of the Institute of Information Security Professionals who recognise our expertise in the field of information and cyber security. Mathematics degree courses are approved by the Institute of Mathematics and its Applications.

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This course is suitable for numerate graduates across many disciplines. Non-computing graduates are eligible. This course provides students with the ability to solve business problems and obtain actionable business insight using analytics. Read more

Description

This course is suitable for numerate graduates across many disciplines. Non-computing graduates are eligible.

This course provides students with the ability to solve business problems and obtain actionable business insight using analytics. The focus of data analytics is on the movement, analysis and interpretation of data and how derived advanced information can inform business strategy. The programme will firstly, prepare students to work with a variety of complex, structured and unstructured data in the business environment, using appropriate statistical and computational skills and technologies. Secondly, it will enable them to articulate insights confidently when presenting reports and visualizations.

Driven by market demands Data Analytics focuses on the movement and interpretation of data, typically with a focus on the past and present in the business context, Data analytics graduates will develop skills to apply qualitative and quantitative techniques and processes used to enhance productivity and business gain.

Core units

- Business Intelligence (with SAS)
- Computational Statistics and Visualisation
- Data Analytics Project

Option units

- Business Analytics
- Data Management and Machine Learning
- Emerging Technologies for the Enterprise
- Strategic Information Systems and Technology

Career prospects

There has been a UK increase in demand of 28% for Data Analytic themed jobs since 2013 to 2015 and Britain is expected to create an average of 56,000 new big data jobs a year until 2020. There is currently a skills shortage in this field which is forecast to increase significantly up to 2020. McKinsey and Company reports that by 2018, there will be 140,000-190,000 job postings by Companies that they are unable to fill due to the lack of expertise.

The range of roles envisaged for graduates from this degree include, but are not limited to:

- Data analyst
- SQL data analyst
- Data quality analyst
- Insight data analyst
- Business intelligence analyst
- Data applications management
- Statistical data Analyst

Careers support is available from the moment you join us, throughout your time here, and for up to three years after the completion of your course. We have a range of services available through the School of Computing, Mathematics and Digital Technology and the University Careers Service including dedicated careers and employability advisors.

Professional Accreditation

The School is an educational affiliate of the British Computing Society – the Chartered Institute for IT in the UK (BCS), a member of the Oracle Academy and an Academy for the Computer Technology Industry Association (CompTIA). Many of the School’s degree programmes are accredited by BCS.

The School is also an academic partner of the Institute of Information Security Professionals who recognise our expertise in the field of information and cyber security. Mathematics degree courses are approved by the Institute of Mathematics and its Applications.

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This Masters degree provides you with a strong conceptual and theoretical understanding of big data analytics. You will gain the essential skills and confidence required to apply and produce knowledge and understanding of issues surrounding big data analytics in a range of contexts. Read more
This Masters degree provides you with a strong conceptual and theoretical understanding of big data analytics. You will gain the essential skills and confidence required to apply and produce knowledge and understanding of issues surrounding big data analytics in a range of contexts. This will enable you to evaluate, adapt, create and utilise appropriate models, methods, practices, theories and computational techniques in the face of changing and evolving technology. There is the opportunity to develop a critical understanding of visualisation concepts, modelling and algorithmic foundations, as well as to develop and evaluate new or advanced bespoke solutions for processing, analysing and making sense of big and/or complex data. The programme enables you concentrate on a specific practical area within computer science and is suitable whether you are a recent graduate or already working in the IT industry and looking to change career paths.

What will I study?

Gaining an in-depth and systematic knowledge of big data management theories, concepts, methodologies and professional practice, you will develop a systematic and critical understanding of algorithms and programming techniques for processing, storing, analysing, visualising and interpreting data.

You will learn the practical skills of mathematics that underpin the processing of data, the programming applications required to manage big data, and the visualisation techniques necessary to make sense of large data sets. There will also be the opportunity to work with emerging technologies derived from industry.

How will I study?

The course is delivered through a combination of lectures, seminars and tutorials with a mixture of daytime and evening classes. Sessions will frequently be highly interactive with a focus on the practical application of concepts and the use of case studies drawn from real life. An emphasis on small group sizes ensures that you will have plenty of opportunities for individual discussions with your tutors. Typically, you will study for approximately nine hours a week if you are studying on a full-time basis.

How will I be assessed?

Your vocational capability, academic critical thinking and intellectual development will be assessed throughout the course. This is achieved through a combination of coursework, case studies, problem-solving exercises and examinations. You may be assessed individually or as part of a group.

Who will be teaching me?

You will be taught by highly qualified, experienced and enthusiastic academic staff who are research-active and fully engaged with the wider business and academic community. The programme team specialise in a variety of subjects so you will benefit from a wide range of expertise. There will also be occasional input from external IT professionals who will be invited to teach particular sessions.

What are my career prospects?

As organisations become ever more dependent on data, there are increasing opportunities in specialist positions related to obtaining, processing and visualising data.

The MSc Big Data Analytics provides you with the skills and knowledge to develop your interests for a career in data science. You will be ideally placed to progress into roles where you will work as a data scientist or data analyst.

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Animation is a fantastically diverse medium, and its possibilities are expanding continually. Animators are dealing with new platforms for delivery, new technologies for production and new audiences as the theories and contexts of animation are being developed and understood. Read more

Programme description

Animation is a fantastically diverse medium, and its possibilities are expanding continually. Animators are dealing with new platforms for delivery, new technologies for production and new audiences as the theories and contexts of animation are being developed and understood.

Animation has become an integral element of most feature production through VFX pipelines, documentary production through the use of data visualisation and improved compositing techniques, and a vital part of any interactive production.

In order to address the wide range of potential interests within the discipline of animation, our courses are non-prescriptive in terms of methodology and output and take advantage of extensive classical and digital technical resources.

A large part of your research work on the course will relate to both your chosen way of working and how to position yourself in the wider milieu of animation. You will develop an awareness of how to affect dynamic transformation and movement, whether it’s upon a product, an environment, a data set or a film narrative. You will be required to be resourceful, critical, and above all independent.

Programme structure

The main focus of your programme will be the production of a short animated film. Although there is no set limit, most students produce a piece of between five and 12 minutes in length. This will be part of a substantive body of practical and written work that will also be submitted for assessment.

While the MA can be completed as a standalone degree in one year, continuation to the MFA is possible. Both programmes include a combination of practical studio work, theory, written studies, professional practice preparation, and a lecture/seminar series, which explores the wider context of your discipline.

Career opportunities

Our graduates find work in four main arenas: animation for cinema, broadcast and web platforms; interactive animation; compositing and visual effects; and data visualisation. Many of our graduates have gone on to careers as award winning independent filmmakers or have followed the studio route and worked with companies such as the BBC, Channel 4, Rushes, Aardman, Laika, Passion Pictures, KoLik, and Nexus Productions, or with directors such as Tim Burton and Sylvain Chomet.

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Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). Read more
Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). That experience is designed, in part, to develop skills for the SAS certification that partners the programme.

Digital Innovation

The aim of this module is to develop knowledge and skills necessary for the implementation of digital business models and technologies intended to realign an organization with the changing demands of its business environment (or to capitalise on business opportunities). Example topics of study include: understanding and justifying change, change management, digital business models, managing technology risks, ethical issues in change.

Quantitative Data Analysis

The aim of the module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. You will develop a practical understanding of core methods in data science application and research (eg bi-variate and multi-variate methods, regression etc). You will also learn to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.

High Performance Computational Infrastructures

The aim of the module is to develop knowledge and skills necessary for working effectively with the large-scale data storage and processing infrastructures that underpin data science. Again, you will develop both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content here covers, highly-scalable data-storage paradigms (eg NoSQL data stores) alongside cloud computing tools (eg Amazon EC2) and in-memory approaches.

Systems Project Management

This module examines the challenges in information systems project management. Example topics of study include traditional project management techniques and approaches, the relationship between projects and business strategy, the role and assumptions underpinning traditional approaches and the ways in which the state-of-the-art can be improved.

Big Data Analytics

The aim of the module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques (eg clustering, regression, support vector machines, boosting, decision trees and neural networks).

Data Management and Business Intelligence

The aim of the module is to develop knowledge and skills to support the development of business intelligence solutions in modern organisational environments. Example topics of study include issues in data/information/knowledge management, approaches to information integration and business analytics. Practical aspects of the subject are examined in the context of the data warehousing environment, with a focus on emerging in-memory approaches.

Data Visualisation

The aim of the module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets (eg to decision-makers). Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design and augmented reality. An emphasis is also placed on visual storytelling and narrative development.

Learning Development Project

The aim of the module is to develop a team-based integrative solution to a problem/challenge drawn from the business, scientific and/or social domain (as appropriate). Working as part of a small team you will: Refine a coherent set of stakeholder requirements from an open-ended (business, scientific or social) problem/challenge; develop a solution addressing those requirements that coherently draws upon the knowledge and skills of other modules within the programme; effectively evaluate the solution (with stakeholders where appropriate).

Dissertation (including Research Methods)

Your dissertation is an opportunity to showcase your project management and subject specific skills to potential employers, and also serves as valuable experience and a solid building block if you wish to pursue a PhD on completion of the MSc. You will be encouraged to critically examine the academic and industrial contexts of your research, identify problems and think originally when proposing potential solutions that serve to demonstrate and reflect your ideas.

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Digital Innovation. The aim of this module is to develop knowledge and skills necessary for the implementation of digital business models and technologies intended to realign an organization with the changing demands of its business environment (or to capitalise on business opportunities). Read more
Digital Innovation

The aim of this module is to develop knowledge and skills necessary for the implementation of digital business models and technologies intended to realign an organization with the changing demands of its business environment (or to capitalise on business opportunities). Example topics of study include: understanding and justifying change, change management, digital business models, managing technology risks, ethical issues in change.

Systems Project Management

This module examines the challenges in information systems project management. Example topics of study include traditional project management techniques and approaches, the relationship between projects and business strategy, the role and assumptions underpinning traditional approaches and the ways in which the state-of-the-art can be improved.

ICTs and Strategic Change

The aim of this module is to develop a critical awareness of the central issues and challenges in introducing information and communication technologies (ICTs) as part of a programme of strategic change in contemporary organisations.

Data Visualisation

The aim of the module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets (eg to decision-makers). Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design, augmented reality. An emphasis is also placed on visual storytelling and narrative development.

Digital Design Methodologies

The aim of this module is to develop an in-depth understanding of digital methodologies and approaches to allow you to select the most appropriate method for any given project or challenge. An emphasis is also placed on developing the core design and communication skills, and professional practices needed to work effectively in modern multidisciplinary digital teams. As part of this module there will be a three week work placement with a digital design company in term one.

Digital Service Applications

The aim of this module is to provide students with the opportunity to apply the methods and further develop skills learned on the Digital Design Methodologies module in term one within the context of a variety of real-world projects. This will allow students to develop their experience of applying the appropriate techniques in real-world scenarios. This module will develop students’ understanding of the type of role they would like to pursue in the digital service design industry. As part of this module there will be a three week work placement with a digital design company in term two.

Dissertation (including Research Methods)

Your dissertation is an opportunity to showcase your project management and subject specific skills to potential employers, and also serves as valuable experience and a solid building block if you wish to pursue a PhD on completion of the MSc. You will be encouraged to critically examine the academic and industrial contexts of your research, identify problems and think originally when proposing potential solutions that serve to demonstrate and reflect your ideas. In addition, there may be an opportunity to undertake an industry-based dissertation project

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1. Big Challenges being addressed by this programme – motivation. Globally, there is a reported shortage of data analytics talent, particularly of individuals with the required deep technical and analytical skills. Read more

About the Course

1. Big Challenges being addressed by this programme – motivation

• Globally, there is a reported shortage of data analytics talent, particularly of individuals with the required deep technical and analytical skills.
• Accenture, Gartner and McKinsey have all identified Data Analytics as one of the fastest growing employment areas in computing and one most likely to make an impact in the future.
• The Irish Government’s policy is for Ireland to become a leading country in Europe for big data and analytics, which would result in 21,000 potential new employment opportunities in Ireland alone.
• CNN has listed jobs in this area in their Top 10 best new jobs in America.

2. Programme objectives & purpose

This is an advanced programme that provides Computing graduates with advanced knowledge and skills in the emerging growth area of Data Analytics. It includes advanced topics such as Large-Scale Data Analytics, Information Retrieval, Advanced Topics in Machine Learning and Data Mining, Natural Language Processing, Data Visualisation and Web-Mining. It also includes foundational modules in topics such as Statistics, Regression Analysis and Programming for Data Analytics. Students on the programme further deepen their knowledge of Data Analytics by working on a project either in conjunction with a research group or with an industry partner.

Graduates will be excellently qualified to pursue careers in national and multinational industries in a wide range of areas. Our graduates currently work for companies as diverse as IBM, SAP, Cisco, Avaya, Google, Fujitsu and Merck Pharmaceuticals as well as many specialised companies and startups. Opportunities will be found in:
• Multinational companies, in Ireland and elsewhere, that provide services and solutions for analytics and big data or whose business depend on analytics and big data technologies;
• Innovative small to medium-sized companies and leading-edge start-ups who provide analytics solutions, services and products or use data analytics to develop competitive advantage
• Companies looking to extend their research and development units with highly trained data analytic specialists
• PhD-level research in NUI Galway, elsewhere in Ireland, or abroad

3. What’s special about CoEI/NUIG in this area:

• The MSc in Computer Science (Data Analytics) is being delivered by the Discipline of Information Technology in collaboration with the Insight Centre for Data Analytics (http://insight-centre.org) and with input from the School of Mathematics, Statistics and Applied Mathematics in NUI Galway
• The Discipline of Information Technology at NUI Galway has 25-year track record of education, academic research, and industry collaboration in the field of Computer Science
• The Insight centre at NUI Galway is Europe’s largest research centre for Data Analytics

4. Programme Structure – ECTS weights and split over semester; core/elective, etc.:

• 90ECTS programme
• one full year in duration, beginning September and finishing August
• comprises:
- Foundational taught modules (20 ECTS)
- Advanced taught modules (40 ECTS)
- Research/Industry Project (30 ECTS).

5. Programme Content – module names

Sample Foundational Modules:

• Tools and Techniques for Large Scale Data Analytics
• Programming for Data Analytics
• Machine Learning and Data Mining
• Modern Information Management
• Probability and Statistics
• Discrete Mathematics
• Applied Regression Models
• Digital Signal Processing

Sample Advanced Modules:

• Advanced Topics in Machine Learning and Information Retrieval
• Web Mining and Analytics
• Systems Modelling and Simulation
• Natural Language Processing
• Data Visualisation
• Linked Data Analytics
• Case Studies in Data Analytics
• Embedded Signal Analysis and Processing

6. Testimonials

Ms. Gofran Shukair, MSc, Research Engineer at ZenDesk, Ireland

After graduating with an MSc at NUI Galway, Gofran worked with Fujitsu’s Irish Research Lab as a research engineer before moving to a software engineering position at Zendesk, Ireland.

“The mix of technical and soft skills I gained through my Masters studies at NUI Galway is invaluable. I had the chance to work with great people and to apply my work on real world problems. With the data management and analysis skills I gained, I am currently pursuing my research in an international research project with one of the leading IT companies. I will be always thankful for studying at NUI Galway, a great historic place based in a culturally-rich vibrant city with an international mix of young and ambitious students that made me eager to learn and contribute back the moment I graduated.”

For further details

visit http://www.nuigalway.ie/courses/taught-postgraduate-courses/msc-in-computer-science-data-analytics.html

How to Apply:

Applications are made online via the Postgraduate Applications Centre (PAC) https://www.pac.ie
Please use the following PAC application code for your programme:

M.Sc. Computer Science – Data Analytics - PAC code GYE06

Scholarships :

Please visit our website for more information on scholarships: http://www.nuigalway.ie/engineering-informatics/internationalpostgraduatestudents/feesandscholarships/

Visit the M.Sc. Computer Science – Data Analytics page on the National University of Ireland, Galway web site for more details!

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The field of computer science has produced some of the most significant advances in modern technology over the last three decades and radically transformed business and industry practices on a global scale. Read more
The field of computer science has produced some of the most significant advances in modern technology over the last three decades and radically transformed business and industry practices on a global scale. There is a profound need for postgraduate-level practitioners in this discipline.

Why Study Advanced Computer Science with us?

Taught at our Thornton Science Park near Chester, this course focuses on the Department of Computer Science’s strengths, providing a cutting-edge curriculum in medical graphics, data visualisation, cybersecurity, discrete optimisation and image processing, in addition to core computer science topics such as algorithm design.

There is also a monthly seminar programme in which leading experts present recent findings and introduce contemporary developments in the above and other areas.

The Department’s commitment to part-time, evening delivery (as an additional alternative to full-time delivery) is particularly attractive to those in employment wishing to gain a postgraduate qualification. This provision is rare among competing institutions and one that has been successfully delivered at Chester for many years.

What will I learn?

The course has a core theme addressing advanced issues in software and algorithmic development, which will equip you to deal with complex problems using a wide range of contemporary techniques. Additionally, the development of a rigorous approach to research and original enquiry will be fostered in our Research Methods and Research Dissertation modules.

Optional modules cover a range of applied topics where the Department has expertise, including data visualisation, virtual reality, computer vision and cybersecurity.

How will I be taught?

The course will be delivered at our modern facilities at Thornton Science Park, which include a VR laboratory, high performance computing facility and cybersecurity laboratories.

You will be taught using a mixture of lectures, workshops, seminars and case studies.
There are 7½ contact hours per week, and you will be expected to undertake 30 hours of private study per week.

How will I be assessed?

Assessment takes place using roughly 30% exams and 70% coursework, although the precise ratio depends on module choices.

Postgraduate Visit Opportunities

If you are interested in this courses we have a number of opportunities to visit us and our campuses. To find out more about these options and to book a visit, please go to: https://www1.chester.ac.uk/study/postgraduate/postgraduate-visit-opportunities

Request a Prospectus

If you would like to know more about the University please request a prospectus at: http://prospectus.chester.ac.uk/form.php

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