Masters Degrees (Visual Analytics)

Visual analytics is a key requirement of the early 21st century. As our human activity generates rapidly increasing amounts of new data every day, there is an urgent need to make sense of it and a huge potential to elicit new knowledge and insights from it.

World events and phenomena like climate change, 9/11, global finance systems and public health are data rich and increasingly complex. Visual analytics turns large and complex, data sets into interactive visualisations that can prompt visceral comprehension and moments of insight that are compelling and offer an unparalleled richness of possibility for data analysts. In this uncharted world of boundless data, visual analytics is providing our new maps and new ways of navigating. Data analytics is recognised as a key trend that will have a major impact on the IT and Communications industry in the next 5 years

Middlesex University is the recognised centre for excellence in visual analytics the UK and leads the UK Visual Analytics Consortium (UKVAC). This group is working at the leading edge of Visual Analytics and works on a global basis. The UK partners are: Imperial College, University College London (UCL), Swansea, Bangor and Oxford Universities.

We are growing our community of practice in visual analytics and this is a chance to join our team, to work alongside some of the best UK researchers and to get involved in leading edge work. The course will build your professional network, your understanding and your portfolio of experience. Visual Analytics is an interdisciplinary, creative and technical activity. That's why we've built a masters course that is unique. You'll be working for most of the time on your own research and development project, supported by great supervisors, and connecting with new people, theories and practices through workshops that are spread throughout the year. You'll leave with unique insights, strong skills and great contacts; you'll also pick up an MSc along the way.

As a student of this course you'll receive a free electronic textbook for every module.

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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.

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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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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Centennial College's Marketing Research and Analytics program positions you at the forefront of a cutting edge job market in which organizations have oceans of data available to them but struggle to make sense of it as marketing becomes increasingly data-driven. As a result, there is a large and growing demand for trained researchers who can harness the power of big data using the latest tools and analytical techniques to uncover new insights and drive businesses forward.

This Marketing Research and Analytics program combines advanced courses in marketing research and big data analytics with training on leading commercial technologies and platforms and the opportunity to gain in-demand industry certifications.

This program equips you with knowledge, skills and training in leading business intelligence and marketing research technologies and tools used in the field. Among them are SAS Enterprise Guide and SAS Enterprise Miner, Environics Analytics Envision (used to develop comprehensive profiles of selected target markets), SPSS, Tableau (the leading data visualization software), Excel, XL Miner, Dell Factiva and NVIVO (qualitative research and text analysis software).

Upon graduation, you receive an Ontario Graduate Certificate from Centennial College, plus certificates of recognition from SAS and Environics Canada. In addition, you are put on an accelerated track to earning the Certified Marketing Research Professional (CMRP) designation, the premier credential in Canadian marketing research from the Marketing Research and Intelligence Association (MRIA).

Career Opportunities

Program Highlights
-The Marketing – Research and Analytics program combines marketing research principles and skills with cutting edge "big data" analytics techniques to equip you with the training required to deliver insights and strategies to help organizations make smarter and more impactful business decisions.
-Employed is an extensive use of learner-centered approaches such as case studies, simulations and project-based learning, with a focus on developing project management, teamwork, analytical thinking, and report writing and presentation skills.
-Hands-on learning covers areas such as questionnaire design, data manipulation, quality control, statistical output and program development.
-There is a strong focus on applying marketing research and analytics to strategic marketing decision-making.
-In the second semester, you develop and implement a capstone project that will integrate and apply your learning.
-In addition to market research technologies, you also have access to the full suite of Microsoft products, including Microsoft Excel, XL Miner, Access and PowerPoint.
-Once you graduate, you have the option to take the Comprehensive Marketing Research Exam (CMRE) on campus at Centennial College, which leads to the Certified Marketing Research Professional (CMRP) designation.

Articulation Agreements
Start with a graduate certificate, and continue to a master of business administration through our degree completion partnership. Successful graduates of this Marketing – Research and Analytics program may choose to continue with courses leading to a graduate degree.

Career Outlook
-Marketing research specialist or analyst
-Research analyst
-Marketing research and intelligence coordinator
-Market intelligence specialist or analyst
-Customer insights analyst
-Consumer research manager
-Business intelligence analyst
-Market research analytics manager
-Web marketing analyst
-Customer experience analyst
-CRM analyst
-Direct response analyst
-Digital marketing analyst
-Social media analyst
-Data and analytics specialist
-Business analytics specialist
-Loyalty program analyst
-Sales analyst
-Marketing strategy analyst

Program Outcomes
-Optimize the financial results produced by interactive marketing programs through the application of marketing analytics
-Contribute to the design of a marketing analytics team project (develop charter, business case financials, technical requirements, design, test plan, test results, approval to proceed) and the management of the resulting project
-Create, manage and mine, and apply modelling and decision making functions to a database
-Utilize data auditing techniques and quality control processes that are consistent with current marketing research codes of conduct and Canadian privacy principles to ensure the integrity of the data collection, storage, analysis and presentation processes
-Compare and contrast, evaluate and select appropriate data sources to meet specific marketing objectives
-Conduct industry, competitor and customer analyses using a wide variety of secondary research sources
-Produce reliable and analyzable data through the application of sound questionnaire design principles to marketing research projects
-Design marketing research projects and interactive marketing programs that are founded in sound sampling techniques, hypothesis testing and research design
-Solve business and marketing problems by identifying, selecting and applying effective, current and relevant techniques such as descriptive and inferential analysis
-Prepare provisional output of analyses including cross-tabulations and pivot tables that address the needs of analysts and prepare final output, including research reports, presentation sides and visual representations of data that address the needs of management
-Develop actionable recommendations based on situation analyses and research findings

Areas of Employment
-Retail corporations
-Organizations with in-house analytical and research functions
-Marketing research firms

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Computer Science influences every aspect of modern life and is one of the fastest-moving academic disciplines. It contributes to everything from the efficiency of financial markets to film and TV graphics and has a huge impact on both economic competitiveness and human wellbeing.


Why study MSc Computer Science at Middlesex?

Our course not only offers a balance between advanced computer science theory and practical experience, but has a very strong focus on contemporary research. Practical work is an important part of every module and the School of Science and Technology has strong links with industry, including companies such as Microsoft and Siemens. The university is very active in the exploration of a number of areas, including computer graphics,mobile development, human-computer interaction, robotics, artificial intelligence, ethics, ubiquitous computing, functional programming, algorithmic biology, image and video analysis, quantum computing, computational biology and visual analytics, and this research influences the course very strongly.

Our course is aimed at students who've studied computing for their first degree, and wish to make themselves stand out further by developing an advanced mastery of the subject.

Course highlights:

The university is home to the Human Interactive Systems Laboratory, acentre of research into haptic technology, and leads the UK Visual Analytics Consortium.

Our specialist multimedia laboratories are well-equipped with industry-standard software and hardware, including both PCs and Macs.

Many of the teaching staff are the authors of widely-used textbooks and learning materials. They include:

Dr Kai Xu, a former senior research scientist with CSIRO, Australia's national science agency;
Dr Elke Duncker-Gassen, aformer systems and software engineer at GEI Gesytec;
Dr Chris Huyck, a former software engineer at Microsoft.
You'll also improve your communication, teamwork, time-management, problem-solving and critical skills.

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About the course

Data is being collected at an unprecedented speed and scale – but 'big data' is of little use without 'big insight'. The skills required to develop such insight are in short supply and the shortage of skilled workers in the data analytics market is cited as a key barrier.

The Data Science and Analytics MSc programme provides these skills, combining a strong academic programme with hands-on experience of leading commercial technology – and the chance to gain industry certification.

You will develop both your critical awareness of the state-of-the-art in data science and the practical skills that help you apply data science more effectively in the business, science and social world.

The programme is run in conjunction with SAS, a market leader in business analytics software and services, and the largest independent vendor in the business intelligence market.

Brunel's programme is unique in being the only current MSc programme that is fully integrated with SAS, providing the SAS base certification.

Aims

The Harvard Business Review calls data science the “sexiest job of the 21st century” – with demand for graduates with SAS skills rapidly rising across financial, retail and government sectors. Data science is now in vogue.

From government, social networks and ecommerce sites to sensors, smart meters and mobile networks, data is being collected at an unprecedented speed and scale – creating an expanding job market for qualified data analysts.

The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (e.g. SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). This experience is designed, in part, to develop skills in preparation for the SAS certification part of the programme.

By the end of the course you should be able to:

Comprehend the key concepts and nuances of the disciplines that need to be synthesised for effective data science.
Demonstrate a critical understanding of the challenges and issues arising from taking heterogeneous data at volume and scale, understanding what it represents and turning that understanding into insight for business, scientific or social innovation (i.e. data science).
Develop a practical understanding of the skills, tools and techniques necessary for the effective application of data science.
Apply a practical understanding of data science to problems in social, business and scientific domains.
Evaluate the effectiveness of applied data science in relation to the issues addressed.

Course Content

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 (e.g. 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.

Typical Modules:

Digital Innovation
Quantitative Data Analysis
High Performance Computational Infrastructures
Systems Project Management
Big Data Analytics
Research Methods
Data Visualisation
Learning Development Project
Dissertation

Special Features

SAS Certification
As an integral part of the programme, you will gain hands-on experience of commercial SAS tools – SAS being the market leader in business analytics software and services, and the largest independent vendor in the business intelligence market.
You will have the opportunity to obtain SAS certification (e.g. SAS Base Programming) which is a recognised industry qualification, following a two week SAS certification ‘boot camp’ preparation course.

Women in Engineering and Computing Programme

Brunel’s Women in Engineering and Computing mentoring scheme provides our female students with invaluable help and support from their industry mentors.

Teaching

Module are typically presented in a mixture of lecture and seminar/lab format. However, where appropriate other teaching methods will also be incorporated. All our learning environments are supported by the market leader in Virtual Learning Environments (VLE), the BlackboardLearn system.

Assessment

Your learning will be evaluated through a combination of in module assessments and more traditional exams, with module specific assessments – for example, presentations within the Learning Development Project.

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Organisations of all sizes must analyse complex business data to remain competitive. Business analytics helps to predict market trends and improve business processes. It empowers managers to make strategic decisions to improve performance in areas such as product development, operations, marketing, sales and supply chain management.

Our MSc Business Analytics develops your analytical skills so you can solve complex business problems. You’re trained to organise, integrate and interpret data so you can make insightful forecasts into all aspects of business operation and implement appropriate actions.

You cover topics such as:
-Statistics and forecasting
-Data mining, visual and analytical techniques
-Global supply networks
-Economic theory
-Business management

Our range of optional modules gives you the opportunity to specialise in a variety of complementary business, management and marketing subjects.

Essex Business School, where this course is taught, is home to the ESRC Business and Local Government Data Research Centre, which helps local authorities and businesses across the UK to harness data more effectively. Not only does the centre have expert data analytics facilities, you’re taught by academics who are actively involved in researching big data and collaborating with businesses to solve real-world issues.

Our School is home to an international community of students and staff. Across our two campuses, our current Masters students join us from more than 40 different nationalities. The University of Essex also offers a number of scholarship and discounts for Masters study, including tailored awards for international applicants.

MSc Business Analytics can be studied on a full-time, part-time or modular basis (ideal if you’d like to gain a qualification whilst in employment).

Postgraduate loans for Masters courses are now available from the Student Loans Company, worth up to £10,000, for students from the UK and EU.

Our expert staff

Our expert academics are at the forefront of the big data debate and reflect this thinking in their teaching.

Essex Business School is in the top 25 in the UK for research excellence (REF 2014) and is recognised for being at the cutting edge of research in: business ethics and corporate social responsibility; organisation studies; leadership and strategy; finance and banking; risk management; and international management.

Specialist facilities

MSc Business Analytics is based at our Southend Campus, with its excellent study and social facilities.

You benefit from being located close to London in the Thames Gateway, one of the UK’s priority areas for economic growth – offering fantastic internship and networking opportunities.

Southend is a seaside town with award-winning beaches, a vibrant night life and excellent transport links. You have access to The Forum, a state-of-the-art building with 24-hour computer suites and study pods. Unlike our Colchester Campus, Essex Business School in Southend is a town centre campus, so you’re amongst the buzz of the High Street, with its excellent bars, shops and restaurants and employment opportunities.

The University of Essex provides initiatives, including those for international students, to help you perform to your best academic potential, such as free academic English classes.

Your future

Data is the driving force behind critical business decisions, so data scientists and analysts are in great demand in both start-ups and well-established companies. Becoming an expert in data analytics means you can help businesses gain competitive advantage by becoming better at making decisions and predictions through organising, analysing, integrating and interpreting data.

This course will equip you with essential numerical, analytical and problem solving skills for a thriving career as a business analyst, manager, or consultant in private and public enterprises.

We have a dedicated employability team within our department, in addition to the University of Essex Employability and Careers Centre, so you are well-placed to find work opportunities both during your course and after you graduate.

In 2015, 78% of our postgraduate taught students were in work or further study (DLHE). Read our graduate profiles to find out the types of organisations our Masters students go on to work for.

Example structure

Postgraduate study is the chance to take your education to the next level. The combination of compulsory and optional modules means our courses help you develop extensive knowledge in your chosen discipline, whilst providing plenty of freedom to pursue your own interests. Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore to ensure your course is as relevant and up-to-date as possible your core module structure may be subject to change.

Business Analytics - MSc
-Managerial Economics
-Global Supply Chain and Operations Management
-Business Analytics for Managers and Entrepreneurs
-Applied Statistics and Forecasting
-Research Methods
-Dissertation
-International Business and Strategy (optional)
-The International Business Environment (optional)
-Creating and Managing the New and Entrepreneurial Organisation (optional)

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Our MSc in Data Science and Analytics aims to provide you with a comprehensive set of skills needed to handle, collect, store and analyse large and complex sets of data. You will be taught by subject experts from both the School of Mathematics and the School of Computer Science and Informatics, which will allow you to see the topic from different perspectives and provides access to a wide range of modules across both Schools.

Throughout the course you will develop data handling and extraction skills, programming skills, machine learning and informatics skills, and problem solving and modelling skills. You will undertake case studies and project work which will give you the opportunity to put your skills into practice and provides valuable experience of working in the field. The dissertation project, typically undertaken with an industrial partner, will allow you to work with complex data in a creative manner and a problem-solving environment, as well as to communicate your ideas and findings effectively.

This programme is available on a one year full-time basis or a three-year part-time basis.

Distinctive features:

• A three-stage degree with exit points at PG Certificate, PG Diploma and Master’s level, allowing you to go into as much depth as you like.

• Acquire transferable data science and analytics skills that are highly sought after in a broad range of sectors.

• Learn from experts across the Schools of Mathematics and Computer Science and Informatics, and related University research groups specialising in various applications of data science and analytics, for example the Data Innovation Research Institute, Social Data Science Lab, and Health Modelling Centre Cymru.

• Gain valuable work experience; we have some placement opportunities available with industrial partners in the UK and abroad.

Structure

There are three stages to this programme. During the first stage, you will study a number of core modules covering fundamental subjects such as statistics, pattern recognition, data mining and optimisation. You may choose to exit after this first stage, at which point you may be able to obtain a PG Certificate qualification.

The second stage consists of a range of optional modules where you can explore subjects of interest to you and relevant your potential career path, for example web and social computing, time series and forecasting, supply chain modelling and visual communication and information design. You may choose to exit after the second stage, at which point you may be able to obtain a PG Diploma qualification.

The third and final stage consists of a three-month dissertation project, which will typically involve working with a company on a real problem of importance. Following successful completion of all modules and the dissertation, you may be able to obtain a Master’s qualification.

As a full-time student, you will complete all modules and your dissertation project in year one.

Part-time students will typically only need to be in the University for lectures and workshops for the equivalent of one day per week over 24 weeks for years 1 and 2. The dissertation project is undertaken during year 3.

Core modules:

Pattern Recognition and Data Mining
Statistical Methods
Optimisation Methods
Dissertation

Optional modules:

Information Processing in Python
Computer Science Topic 1: Web and Social Computing
Web Application Development
Distributed and Cloud Computing
Informatics
Visual Communication and Information Design
Time Series and Forecasting
Supply Chain Modelling
Statistics and Operational Research in Government
Credit Risk Scoring

Teaching

The methods of teaching we employ will vary from module to module, as appropriate depending on the subject matter and the method of assessment. We teach using a mixture of lectures, seminars, computer workshops and tutorials.

Programming skills and the use of relevant software packages will be taught in our dedicated computer suites. We often invite industry experts to give presentations, which our students are welcome to attend.

We will allocate three supervisors to you for your dissertation project. Usually your supervisors will be two members of academic staff with an interest or specialism in your field of research and a sponsor supervisor from the organisation you will work with during your project. You should meet regularly with your supervisor throughout your project.

Support

All of our students are allocated a personal tutor when they enrol on the course. A personal tutor is there to support you during your studies, and can advise you on academic and personal matters that may be affecting you. You should have regular meetings with your personal tutor to ensure that you are fully supported.

You will have access to the Trevithick Library, which holds our collection of mathematical and computer science-related resources, as well as to the other Cardiff University Libraries.

We will provide you with a copy of the Student Handbook, which contains details of each School’s policies and procedures. We also support students through the University’s virtual learning environment, Learning Central, where you can ask questions in a forum or find course-related documents.

Cardiff University also offers a wide range of support services which are open to our students, such as the Graduate Centre, counselling and wellbeing, financial and careers advisors, the international office and the Student Union.

Feedback:

We offer written and oral feedback, depending on the coursework or assessment you have undertaken. You will usually receive your feedback from the module leader. If you have questions regarding your feedback, module leaders are usually happy to give advice and guidance on your progress. We aim to provide you with feedback in a timely manner after you have submitted an assessment.

Assessment

We will assess your progress throughout the course. These assessments may take the form of written exam papers, in-module assignments, and the project dissertation, where knowledge and technical competence will be appraised. We may also use group work, oral presentations and poster displays to test communication, critical thinking and problem solving skills.

Career prospects

Data is increasingly cheap and ubiquitous, and is being collected on a massive scale. There is a significant and growing demand for professionals who can work efficiently and effectively with handling such complex and sizeable data and to extract insights to help inform decision-making. The skills you gain during the programme will equip you for graduate roles in this field. This new MSc programme enhances the already well-established related postgraduate taught programmes that the School of Mathematics offers, and is expected to be as successful in the recruiting of our graduates. Previous postgraduates have gone on to work with a variety of companies and Government organisations including the Office for National Statistics, Lloyds Banking Group, Nationwide, British Airways, Network Rail, UK Government, The Financial Times, Virgin Media, Welsh Water and Admiral Insurance.

If you prefer to continue on a more academic career pathway, you may choose to continue your studies with a PhD.

Placement

You will undertake a three-month placement for your dissertation project, based with one of our industrial partners in the UK or abroad.

We employ a dedicated Knowledge Exchange Officer who will work with you to obtain a placement and support you throughout your project.

Past placements achieved by our students have been with companies such as Admiral, British Airways, Lloyds Banking Group, Welsh Water, Office for National Statistics, Sainsbury’s, Virgin Media, Transport for London, and Deloitte.

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Our modern world is witnessing a growth of online data in a variety of forms, including web documents, blogs, social networks, digital libraries and medical records. Much of this data contains valuable information, such as emerging opinions in social networks, search trends from search engines, consumer purchase behaviour, and patterns that emerge from these huge data sources.

The sheer volume of this information means that traditional stand-alone applications are no longer suitable to process and analyse this data. Our course equips you with the knowledge to contribute to this rapidly emerging area.

We give you hands-on experience with various types of large-scale data and information handling, and start by providing you with a solid understanding of the underlying technologies, in particular cloud computing and high-performance computing. You explore areas including:
-Mobile and social application programming
-Human-computer interaction
-Computer vision
-Computer networking
-Computer security

You also obtain practical knowledge of processing textual data on a large scale in order to turn this data into meaningful information, and have the chance to work on projects that are derived from actual industry needs proposed by our industrial partners.

We are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of our research rated ‘world-leading’ or ‘internationally excellent (REF 2014).

This degree is accredited by the Institution of Engineering and Technology (IET).This accreditation is increasingly sought by employers, and provides the first stage towards eventual professional registration as a Chartered Engineer (CEng).

Our expert staff

Today’s computer scientists are creative people who are focused and committed, yet restless and experimental. We are home to many of the world’s top scientists, and our staff are driven by creativity and imagination as well as technical excellence. We are conducting world-leading research in areas such as evolutionary computation, brain-computer interfacing, intelligent inhabited environments and financial forecasting.

Specialist staff working on data analytics include:
-Dr Luca Citi – machine learning, learning from biological signals and data (EEG, etc)
-Dr Adrian Clark – automatic construction of vision systems using machine learning and evaluation of algorithms, data visualisation and augmented reality
-Professor Maria Fasli – analysis of structured/unstructured data, machine learning, adaptation, semantic information extraction, ontologies, data exploration, recommendation technologies
-Professor John Gan – machine learning for data modelling and analysis, dimensionality reduction and feature selection in high-dimensional data space
-Dr Udo Kruschwitz – natural language processing, analysis textual/unstructured data, information retrieval
-Professor Massimo Poesio – cognitive science of language, text mining, computational linguistics
-Professor Edward Tsang – applied AI, constraint satisfaction, computational finance and economics, agent-based simulations

Specialist facilities

We are one of the largest and best resourced computer science and electronic engineering schools in the UK. Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.
-We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
-All computers run either Windows 7 or are dual boot with Linux
-Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
-Students have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
-We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors

Your future

Demand for skilled graduates in the areas of big data and data science is growing rapidly in both the public and private sector, and there is a predicted shortage of data scientists with the skills to understand and make commercial decisions based on the analysis of big data.

Our recent graduates have progressed to a variety of senior positions in industry and academia. Some of the companies and organisations where our former graduates are now employed include:
-Electronic Data Systems
-Pfizer Pharmaceuticals
-Bank of Mexico
-Visa International
-Hyperknowledge (Cambridge)
-Hellenic Air Force
-ICSS (Beijing)
-United Microelectronic Corporation (Taiwan)

We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

Postgraduate study is the chance to take your education to the next level. The combination of compulsory and optional modules means our courses help you develop extensive knowledge in your chosen discipline, whilst providing plenty of freedom to pursue your own interests. Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore to ensure your course is as relevant and up-to-date as possible your core module structure may be subject to change.

Big Data and Text Analytics - MSc
-MSc Project and Dissertation
-Information Retrieval
-Cloud Technologies and Systems (optional)
-Group Project
-High Performance Computing
-Machine Learning and Data Mining
-Natural Language Engineering
-Professional Practice and Research Methodology
-Text Analytics
-Advanced Web Technologies (optional)
-Data Science and Decision Making (optional)
-Big-Data for Computational Finance (optional)
-Computer Security (optional)
-Computer Vision (optional)
-Creating and Growing a New Business Venture (optional)
-Mobile & Social Application Programming (optional)

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Surrey Business School’s Business Analytics programme is dedicated to producing creative and knowledgeable Business Analysts with the ability to convert Big Data to actionable insight in business.

Whether it’s using Artificial Intelligence to improve a chess programme, or understanding the power of visualisation from a simple graph.

With input from industry experts in class and on-site, you will engage with real-world business problems.

PROGRAMME OVERVIEW

Artificial Intelligence and Machine Learning, Big Data. New technologies and ways of working are changing the way we make decisions.

This programme will take your career to the next level and develop your ability to confidently make high impact businesses decisions that are driven by data.

The programme focuses on two key areas: analysing business data, and solving business challenges analytically. Optional modules allow you to further specialise in areas such as the economic, managerial or finance or aspects of the subject.

Furthermore, you will benefit from hands-on experience of a wide range of analytics software such as simulators and mathematical tools.

PROGRAMME STRUCTURE

The programme is studied full-time over one academic year. It consists of eight taught modules and a dissertation. 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.
-Data Analytics
-Supply Chain Analytics
-Econometrics I
-Machine Learning and Visualisations
-Principles of Accounting
-Foundations of Finance: Finance and Investments
-Supply Chain and Logistics Management
-Information for Decision Making
-Managing Decisions Implementation
-Introduction to Marketing Analytics
-Econometrics II
-Business Process Management
-Innovation Management
-Investment Analysis
-Dissertation

CAREER PROSPECTS

Business analytics students often pursue careers as consultants, researchers, managers, and analysts.

SOFTWARE

You will get hands-on experience using a wide range of tools in the course. An indicative list of the software tools is as follows:
-Excel (using the Solver and Data Analysis Add-Ins) and Tableau for decision making and visual analytics
-COGNOS and SQL Server for Business Intelligence for analytical processing
-Apache Hadoop (Map Reduce) with Amazon’s Elastic Cloud or IBM’s Smart Cloud for distributed Big Data analytics
-SAP for Enterprise Resource Planning
-R, SPSS and EViews for coding, statistics and forecasting
-ILOG’s Optimisation Studio (Cplex) for optimisations
-Matlab for algorithms and programming and Simulink (SimEvents) for simulations
-Arena (or Simul8) for Discrete Event Simulations

EDUCATIONAL AIMS OF THE PROGRAMME

The programme’s aim is to provide a high quality education that is both intellectually rigorous and at the forefront of management science research, relevant for problem solving and decision making by managers.

It will respond to the emergent needs of corporations and academia for professionals who are able to work with analytical tools to generate value from available Information depots and take advantage of the vast amounts of data now provided by the modern ICT and ERP systems, which underlie the operations of modern corporations.

The program will implant understanding of the theoretical base around knowledge management and knowledge work, practical skills and experience in using analytical software tools.

It will allow future professional managers and consultants to cope with an increasingly complex and global operational environment of the modern corporation.

Completion of the programme will provide a sound foundation for those considering continuing their academic development towards a PhD degree in the management disciplines.

The programme is structured in a way that would provide students with a choice between a more quantitative intensive track of modules or a qualitative analytic (business development track) which would reflect students’ personal strengths and preferences and match future career aspirations.

The compulsory modules provide a sound foundation which builds an analytical skillset using relevant statistical and management theories, and supports the development of practical hands-on experience applying the theoretical aspects using real-world data to address corporate challenges and find solutions to actual problems.

The readings in the module will build a sound basis which would allow students to access and understand the academic literature and undertake empirical investigations in the areas of decision modelling and business development.

PROGRAMME LEARNING OUTCOMES

The programme provides opportunities for students to develop and demonstrate knowledge and understanding, skills, qualities and other attributes in the following areas:

Knowledge and understanding
-A systematic, in-depth understanding of the development; issues and influences relevant to discipline of Management Decision Making, Management Science, and Data Science.
-Deep and thorough understanding of quantitative analytical methodologies and hands-on experience with decision-making software and data management tools.
-Knowledge about issues, application and analysis of Big Data
-An understanding of the academic research process.

Intellectual / cognitive skills
-Demonstrate deep learning, understanding of the material and ability to apply the knowledge and demonstrate skills in problem solving in the topic space of the modules studied
-Carry out assessments of data in a repository, select the appropriate analysis tools, design and execute an analytical methodology (not required for PG Certificate), apply adequate visualization methodologies to present the results and interpret the findings and finally to communicate the results effectively to a select audience

Professional practical skills
-Demonstrate the ability to independently evaluate critical approaches and techniques relevant to Business Analytics
-Know and apply a range of techniques and tools to analyse data related to business operations
-Capability of selecting the right methodology and software to solve management and operational business issues
-Relate existing knowledge structures and methodologies to analytical business challenges

Key / transferable skills
-Conduct critical literature review; to select, define and focus upon an issue at an appropriate level
-Develop and apply relevant and sound methodology
-Apply the methodology to analyse the issue
-Develop logical conclusions and recommendations
-Be aware of the limitations of the research
-Identify modifications to existing knowledge structures and theoretical frameworks and therefore to prose new areas for investigation, new problems, new or alternative applications or methodological applications

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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Data science is positioned to be the major new arena of scientific discovery in the 21st century. As Eric Schimdt, CEO of Google, has pointed out, we now generate as much information every two days as we did from the dawn of human civilisation up until the year 2003.

To cope with this vast amount of data, there is an urgent requirement to derive meaningful insights from very large and diverse data sources: the so-called 'big data challenge'. Secure identification of individuals, accurate financial prediction and reliable cancer diagnosis are all examples of area in which the technologies underpinning data science are marketing revolutionary contributions, enriching our lives, and making our future healthier, more efficient and more secure.

The MSc by Research (Data Science) provides an opportunity for students who wish undertake an individual real-world big-data research project; you can choose a project area from a wide variety of interdisciplinary domains, including business information systems, e-health, social media, cloud computing, smart homes, intelligent vehicles and ambient assisted living.

We currently have supervisors available in:

Machine learning
Visual analytics
Mathematics and statistics
Smart sensors (for ambient assisted living and smart homes)
Numerical algorithms (especially computational geometry)
Artificial intelligence
Ethics of technology
Psychology
Business information systems and business informatics
Robust software/system development
Networking
Ethics
Marketing
Operations management
Enterprise and economic development
Education
Design
As a student of this course you'll receive a free electronic textbook for every module.

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This professional degree is for people who are passionate about drawing meaningful knowledge from data to drive business decision-making or research output.
Data is a vital asset to an organisation. It can provide valuable insights into areas such as customer behaviour, market intelligence and operational performance. Data scientists build intelligent systems to manage, interpret, understand and derive key knowledge from this data.
For those with strong mathematical or quantitative backgrounds, this degree will develop your analytical and technical skills to use data science to guide strategic decisions in your area of expertise. It offers the flexibility to tailor your learning to your professional and personal interests.
Leveraging the University’s research strengths, you will explore the latest in data mining, machine learning and data visualisation, while developing the skills to effectively communicate data insights to key stakeholders.
For those with qualifications in other areas such as health and education, a Graduate Certificate in Data Science can provide you with data science capability to complement your existing skills and provide a pathway to the master’s program.
Course structureThe course comprises core units, elective units and a capstone project where you will apply your skills to a real-world data science problem. You can tailor your degree by selecting elective units and a project that complement your particular interests, background and qualifications.
Core units for the Master of Data Science include Principles of Data Science, Computational Statistical Models, Visual Analytics, and Knowledge Discovery and Data Mining.
For the Graduate Certificate in Data Science, core units include Principles of Data Science, Algorithms, Database Management Systems and Introduction to Statistics.
You can select elective units from the following data science subjects, or from other disciplines relevant to your background and qualifications.
Data science electives include:
Advanced Data Models
Cloud Computing
Multimedia Retrieval
Data Analytics and Business Intelligence
Information Security Management
Statistical Learning and Data Mining
Statistical Natural Language Processing
Predictive Analytics.We also offer a pathway for eligible candidates planning to pursue a research degree.

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About the course

BIM is a growing technology used in the building industry worldwide. It uses digital techniques to create and use intelligent 3D models to communicate building project decisions. GIS is a technical system to implement the collection, storage, management, calculation, analysis, display and description of geospatial information data. The results help to understand what is happening in a geographical space which can increase efficiency in building planning and design. The current ‘lack of BIM innovation’ and ‘lack of BIM talent’ could delay the progress of Chinese “smart” cities, which aim to reduce resource consumption and cost and use digital technologies to benefit their citizens.

This programme will be run entirely at the University of Nottingham Ningbo China (UNNC) with internship opportunities in leading BIM companies in China. It is a collaboration between Department of Architecture and Built Environment and Civil Engineering. In particular, research and teaching support will be provided by three leading research laboratories including Geospatial BIM lab, Digital City Infrastructure and technology Innovation Laboratory (D-CiTi Lab), and Big Data and Visual Analytics Lab. These laboratories are working closely with leading AEC consultants (Arup, WSP BP), international professional institutions (RICS, ICES, CIBSE) and leading BIM software vendors (Autodesk, Bentley, Leica, Tekla, Trimble).

Students are able to learn how to use and operate a very wide variety of state-of-the-art software, as well as surveying equipment including servo driven total stations, laser scanners, GNSS, digital and analogue photogrammetry. With extensive project and consultancy experience on the Geospatial Engineering, BIM in the AEC sector in the UK and China, the team is planning to promote the Smart City with multi-dimension BIM applications across China.

Advantages of studying this programme at the University of Nottingham Ningbo:

- Be familiar with BIM related software and surveying device
- The ability to apply their skills directly within the surveying and AEC industry
- React quickly to new technologies and innovations
- Communicate ideas effectively in written reports, verbally and presentations to groups
- Exercise original thought, as well as gain interpersonal, communication and professional skills
- Participate real project work for experience accumulation
- Plan and undertake individual projects

This programme will help:

- Fain a complete understanding of theory, practice and issues of BIM and Geospatial technologies
- Acquire opportunities to use what you have learnt in real project work
- Explore new research methodology to promote development in this field
- Acquire technical skills of software operation, data analysis and design optimization
- Improve team-work ability and communication skill
- Foster individual ability to conduct academic researches

Course structure

The course is studied over 12 months. 60 credits of modules are studied in the autumn and 60 credits of modules are studied in the Spring Semesters. A research project is undertaken in the summer semester, also worth 60 credits.

Compulsory modules:

Geospatial Engineering and BIM Research Project
Introduction to Building Information Modelling and Management
Research Project Literature Review
Fundamentals of Satellite Positioning
Geodetic Reference Systems
Analytical Methods
Photogrammetry and Remote Measurement Techniques
Global Smart City with Integrated BIM
3D Modelling for BIM
Research Project Organization and Planning
Engineering Surveying
BIM and Knowledge Management
BIM+ and its Future

Career options for this degree

In China, BIM has attracted an increasing amount of concern and lots of AEC companies or institutes desire to recruit talented people with relevant BIM skills and experience. Graduates from this program would be expected to find decent jobs in local and international AEC companies. During the academic period, students have opportunities to visit and communicate with large AEC companies, and even enroll as interns after graduation. For those who wish to pursue their academic careers, this postgraduate programme prepares them well for higher level research and to continue onto PhD level.

Our Careers Development Service will work with you to explore your options. They will invite you to attend recruitment events featuring potential employers, and will suggest further opportunities, such as relevant work experience placements and skills workshops.

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Take advantage of one of our 100 Master’s Scholarships to study Computer Science: Informatique 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.

The MSc in Computer Science: Informatique is a Dual Degree scheme between Swansea University and Université Grenoble Alpes for computer science.

The MSc in Computer Science: Informatique Grenoble dual degree scheme is a two year programme that provides students with an opportunity to study in both Swansea, UK and Grenoble, France. One year of the Computer Science: Informatique programme students study at Swansea University and the second year of the programme students study at Université Grenoble Alpes. Upon successful completion of the programme, students will receive an M.Sc. in Advanced Computer Science from Swansea University and a Master from Université Grenoble Alpes.

Key Features of Computer Science: Informatique MSc

- We are top in the UK for career prospects [Guardian University Guide 2018]
- 5th in the UK overall [Guardian University Guide 2018]7th in the UK for student satisfaction with 98% [National Student Survey 2016]
- We are in the UK Top 10 for teaching quality [Times & Sunday Times University Guide 2017]
- 12th in the UK overall and Top in Wales [Times & Sunday Times University Guide 2017]
- 92% in graduate employment or further study six months after leaving University [HESA data 2014/15]
- UK TOP 20 for Research Excellence [Research Excellence Framework 2014]
- Our Project Fair allows students to present their work to local industry
- Strong links with industry
- £31m Computational Foundry for computer and mathematical sciences will provide the most up-to-date and high quality teaching facilities featuring world-leading experimental set-ups, devices and prototypes to accelerate innovation and ensure students will be ready for exciting and successful careers. (From September 2018)
- Top University in Wales [Times & Sunday Times University Guide 2017]

Modules of Computer Science: Informatique MSc

Modules on the MSc in Computer Science: Informatique may include:

Critical Systems; IT-Security: Theory and Practice; Visual Analytics; Data Science Research Methods and Seminars; Big Data and Data Mining; Data Visualization; Human Computer Interaction; Big Data and Machine Learning; Web Application Development; High Performance Computing in C/C++; Software Testing; Graphics Processor Programming; Embedded System Design; Mathematical Skills for Data Scientists; Logic in Computer Science; Computer Vision and Pattern Recognition; High-Performance Computing in C/C++; Hardware and Devices; 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, 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 our expansion, we are building the Computational Foundry on our Bay Campus for computer and mathematical sciences. This development is exciting news for Swansea Mathematics who are part of the vibrant and growing community of world-class research leaders drawn from computer and mathematical sciences.

Careers

All Computer Science courses will provide you the transferable skills and knowledge to help you take advantage of the excellent employment and career development prospects in an ever growing and changing computing and ICT industry.

94% of our Postgraduate Taught Computer Science Graduates were in professional level work or study [DLHE 14/15].

Some example job titles include:

Software Engineer: Motorola Solutions
Change Coordinator: Logica
Software Developer/Engineer: NS Technology
Workflow Developer: Irwin Mitchell
IT Developer: Crimsan Consultants
Consultant: Crimsan Consultants
Programmer: Evil Twin Artworks
Web Developer & Web Support: VSI Thinking
Software Developer: Wireless Innovations
Associate Business Application Analyst: CDC Software
Software Developer: OpenBet Technologies
Technical Support Consultant: Alterian
Programming: Rock It
Software Developer: BMJ Group

Research

The results of the Research Excellence Framework (REF) 2014 show that Swansea Computer Science ranked 11th in the UK for percentage of world-leading research, and 1st in Wales for research excellence. 40% of our submitted research assessed as world-leading quality (4*).

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Programme Description

Please note this programme will be undergoing some changes for the 2017/18 entry and courses may be subject to change between now and the commencement of the programme in September 2017.

This programme offers you the chance to develop a detailed understanding of the application of geographical information science (GIS) and related technologies within the field of archaeology.

The programme retains a distinctive Scottish flavour, and students will benefit from the guidance of internationally recognised staff.

The programme combines the pedigree of Edinburgh’s GIS expertise with a long-established reputation in archaeological teaching and research.

You will gain a broad understanding of the use of GIS in archaeological surveying, recording and research and will be equipped with the analytical and communication skills necessary to work in this vibrant area.

Demand for the application of GIS within archaeology is growing at an unprecedented rate, including searching for new archaeological sites, determining the societal context of existing sites and examining the interplay between successive occupations of a site.

The proven ability of our GIS graduates in employment means our programme is held in high regard by a wide range of employers.

Applicants who applied after 12 December 2016 receiving an offer of admission, either unconditional or conditional, may be required to pay a tuition fee deposit. Please see the fees and costs section for more information.

Programme Structure

The programme is organised into two semesters of taught courses, delivered through lectures and seminars, after which you will work towards your individual dissertation.

Compulsory courses typically will be:

GIS & Spatial Analysis for Archaeologists

Spatial Modelling and Analysis

Research Practice & Project Planning

Dissertation

Option courses:

In consultation with the Programme Director, you will choose from a range of option courses. We particularly recommend:

Exploring the Past with Data Science

Quantitative Methods & Reasoning in Archaeology

Technological Infrastructures for GIS

Visual Analytics

Principles and Practice of Remote Sensing

Active Remote Sensing: Radar and LiDAR

Passive Earth Observation: new platforms, sensors and analytical methods

Business Geographics

Space , Place and Time: the archaeology of built environments

The Scottish Lowlands: Archaeology and Landscape before the Normans

Courses are offered subject to timetabling and availability and are subject to change. Field trip

There is a field trip focusing on techniques for capturing geospatial information. This field trip has historically taken place at the Kindrogan Field Centre, Perthshire.

Career Opportunities

The expertise gained on this programme will allow you to continue to study or to pursue a career in surveying, illustration and 3D visualisation, digital archiving, heritage management, terrain modelling, database management, geomatics or consultancy.

Our GIS graduates have gained work in both public and private sector organisations, including Historic Scotland, English Heritage, the Royal Commission on the Ancient and Historical Monuments of Scotland, thinkWhere (formerly Forth Valley GIS) and CFA Archaeology.

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