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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Health Data Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017). Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Health Data Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

Healthcare, with an already established strong relationship with Information & Communication Technologies (ICT), is continuously expanding the knowledge forefront as new methods of acquiring data concerning the health of human beings are developed.

Processing this data to extract valuable information about a population (epidemiological applications) or the individual (personalised healthcare applications) is the work of health data scientists. Their work has the potential to improve quality of life on a large scale.

Swansea University is the first institution in the UK to offer this taught master's programme in Health Data Science designed to develop the essential skills and knowledge required of the Health Data Scientist.

Key Features of the Health Data Science Programme

- A one year full-time taught master's programme designed to develop the essential skills and knowledge required of the Health Data Scientist.

- The Health Data Science course is also available for three years part-time study.

- An integrated programme of studies tailored to the essential skill set required for Data Scientists operating within healthcare organisations covering key topics in computation, data modeling, visualisation, machine learning and key methodologies in the analysis of linked health data.

- Hands on experiential learning from the professionals behind the Secure Anonymised Information Linkage (SAIL) Databank, a UK-exemplar project for the large scale mining of healthcare data within a secure environment.

- Strong collaboration links with colleagues from the Centre for Health Services Research of the University of Western Australia, a group of leading experts in the analysis of linked health data.

- The Health Data Science course is based within the award winning Centres for Excellence for Administrative Data and eHealth Research of Swansea University, awarded by the Economic and Social Research Council (ESRC) and Medical Research Council (MRC), enhancing the quality of the course.

Who should study MSc Health Data Science?

The Health Data Science course is suitable for those working in healthcare with roles involving the analysis of health data and also computer scientists with experience in working with data from the healthcare domain, as well as biomedical engineers and other similar professions.

Course Structure

Students must complete 6 modules of 20 credits each and produce a 60 credits dissertation on a Health Data Science project. Each module of the programme requires a short period of attendance that is augmented by preparatory and reflective material supplied via the course website before and after attendance.

Attendance Pattern

Health Data Science students are required to attend the University for 1 week (5 consecutive days) for each module in Part One. Attendance during Part Two is negotiated with the supervisor.

Modules

Modules on the Health Data Science programme typically include:

Scientific Computing and Health Care

Health Data Modelling

Introductory Analysis of Linked Health Data

Machine Learning in Healthcare

Health Data Visualisation

Advanced Analysis of Linked Health Data

Professional Development

The College of Medicine offers the modules on the Health Data Science course as standalone opportunities for prospective students to undertake continued professional development (CPD) in the area of Health Data Science.

You can enroll on the individual modules for the Health Data Science programme as either an Associate Student (who will be required to complete the module(s) assessments) or as a Non-Associate Student (who can attend all teaching sessions but will not be required to complete any assessments).

For information and advice on applying for any of the continuing education opportunities, please contact the College directly at .

Employability

Postgraduate study has many benefits, including enhanced employability, career progression, intellectual reward and the opportunity to change direction with a conversion course.

From the moment you arrive in Swansea, specialist staff in Careers and Employability will help you plan and prepare for your future. They will help you identify and develop skills that will enable you to make the most of your postgraduate degree and enhance your career options. The services they offer will ensure that you have the best possible chance of success in the job market.

The student experience at Swansea University offers a wide range of opportunities for personal and professional development through involvement in many aspects of student life.

Co-curricular opportunities to develop employability skills include national and international work experience and study abroad programmes and volunteering, together with students' union and athletic union societies, social and leisure activities.

For the MSc Health Data Science course, we are in the process of identifying opportunities for our students to complete volunteering placements with a number of our collaborative partners.



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This one year MSc Data Science degree prepares you to become a proficient data scientist, building core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to devising and applying sophisticated Big Data analytics techniques. Read more

This one year MSc Data Science degree prepares you to become a proficient data scientist, building core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to devising and applying sophisticated Big Data analytics techniques.

Introducing your degree

This MSc programme will train students to become proficient data scientists.

You will gain advanced knowledge in areas such as data mining, machine learning, and data visualization, including state of the art techniques, programming toolkit, and industrial and societal application scenarios.

Overview

This programme prepares you to become a proficient data scientist, developing your specialist knowledge in subjects that are crucial for mastering the vast and ever-so-complex information landscape that is characteristic to modern, digitally empowered organisations.

This is typically linked to a number of core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to the know-how that is required to devise and apply sophisticated Big Data analytics techniques, and the creativity involved in designing powerful visualizations.

In the first semester you start with a review of key topics in data science. The course will introduce the core theoretical and technology components required to design and use a data science application, using open-source tools and openly accessible data sets. You will also cover the most important machine learning techniques, which are at the core of any attempt to analyse and reason about data.

You will be exposed to more advanced topics in data mining in the second semester, including feature engineering, methods to manipulate text and multimedia data, topic modelling, social network analysis, and spectral analysis. A new module on data visualization will introduce the most common types of visualization techniques and state-of-the-art technology used to build graphic elements into data science applications to present analytics results.

Finally, during the summer the MSc project enables you will demonstrate your mastery of specialist techniques, relevant methods of enquiry, and your ability to design and deliver advanced application, systems and solutions to a tight deadline, including the production of a substantial dissertation.

Career Opportunities

Data scientists help organisations handle large amounts of data being produced thanks to digital technologies. Harvard Business Review described the role as 'The Sexiest Job of the 21st Century' due to the rare combination of skills that a trained data scientist possesses.

Data science has seen an unparalleled expansion as the data-driven economy grows. Increasingly organisations require skilled professionals who can handle large datasets and managers who can utilise the resulting analysis to make impactful decisions.

There is a range of potential jobs available; demand for big data staff is predicted to rise 92% over 5 years from Jan 2013. The programme provides an excellent opportunity for entry into data sciences or similar fields. Plus, big data positions offer a median salary of £55,000 – 24% higher than for IT staff in general (UK). There are also academic possibilities for doctoral study, as there are for entrepreneurial careers.

ECS runs a dedicated careers hub with is affiliated with more than 100 renowned companies such as IBM, Arm, Microsoft, Samsung, and Google. Visit our Careers Hub for more information.

Graduates from our MSc program can seek employment worldwide in:

  • established companies looking to spot trends in sales, marketing or operational data;
  • start-ups based around new opportunities in the booming data-driven economy;
  • government departments looking to utilise linked open data to gain insights to affect policy at the highest levels;
  • research/consultancy companies analysing data and feeding back to the wider community, with training and specialist services to clients.

Through an extensive blend of networks, mentors, societies and our on-campus startup incubator, we also support aspiring entrepreneurs looking to build their professional enterprise skills. Discover more about enterprise and entrepreneurship opportunities.



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Summary. Data Science is a rapidly developing field of study within both academia and industry. Its interdisciplinary nature ensures its wide application domain. Read more

Summary

Data Science is a rapidly developing field of study within both academia and industry. Its interdisciplinary nature ensures its wide application domain. This MSc Data Science aims to prepare students for a successful career as a data scientist or business analyst working in any profession where large amounts of data is collected, hence there is a need for skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation. These type of skills are typically in high demand in IT business, security and health sectors, intelligent transport, energy efficiency and the creative industries.

More generally data and analytics capabilities have developed rapidly in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. Most companies, however, are not capturing the full potential value from data and analytics because they do not have the required expertise. Consequently, the MSc Data Science aims to address these challenges by providing a firm grounding in the core disciplines of data analytics and information processing, partnered with a broad appreciation of aspects of other disciplines where data science can form natural synergistic relationships.

Modules

  • Business intelligence
  • Machine learning and data modelling
  • Data science foundations
  • Big data and infrastructure
  • Statistical modelling and data mining
  • Masters project (research)

Academic profile

Ulster University academics are actively involved in both research and teaching and this ensures that the developments accrued through research can feed into the teaching of students. A high percentage of staff are members of the Higher Education Academy, and all staff are expected to have a Postgraduate Certificate in University Teaching or equivalent. All Computing courses are subject to periodic Faculty Review and University Revalidation.

Career options

The key message from employability and work-related learning initiatives is that enhancing opportunities to develop work-related learning and employability enhances the learning of the subject being studied. We understand the importance of including real industrial and commercial contexts to our student's experience, so this MSc Data Science will pursue opportunities for industrially linked teaching material and student project work. In this regard, we will utilise our business and industry links to facilitate an industrially relevant student project. Such projects create valuable experiences for the student, and additionally, they can also help to build new and ongoing collaborations with departments and companies, with the potential to tap into funding streams designed for industry-academic research and development.

A recent statement from Ulster University’s Careers Office indicates that Data analysts are in high demand across all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education. Data analysts can work in large companies such as the ‘big four’ consultancies or financial services firms, or consumer retail firms, small and medium sized businesses such as marketing agencies’ or the public sector.



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The UCL programme in Data Science for Research in Health and Biomedicine covers computational and statistical methods as applied to problems in data-intensive medical research. Read more

The UCL programme in Data Science for Research in Health and Biomedicine covers computational and statistical methods as applied to problems in data-intensive medical research. Students learn techniques that are transforming medical research and creating exciting new commercial opportunities. Our recent graduates, many of whom begin paid internships while completing the MSc, have moved on to roles in industry and academia.

About this degree

Students learn how to link and analyse large complex datasets. They design and carry out complex and innovative clinical research studies that take advantage of the increasing amount of available data about the health, behaviour and genetic make-up of small and large populations. The content is drawn from epidemiology, computer science, statistics and other fields, including genetics.

Students undertake modules to the value of 180 credits.

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

A Postgraduate Diploma (120 credits) is offered.

A Postgraduate Certificate (60 credits) is offered.

Core modules

  • Principles of Epidemiology Applied to Electronic Health Records Research
  • Data Management for Health Research
  • Statistics for Epidemiology and Public Health
  • Statistical Methods in Epidemiology
  • Topics in Health Data Science

Optional modules

  • Advanced Statistics for Records Research
  • Database Systems
  • Information Retrieval and Data Mining
  • Principles of Health Informatics
  • Machine Learning in Healthcare and Biomedicine
  • Statistics for Interpreting Genetic Data
  • Electronic Health Records
  • Clinical Decision Support Systems

Dissertation/report

All students undertake an independent research project which culminates in a dissertation. Project Proposal 20% (2,000 words); Journal Article 80% (6,000 words).

Teaching and learning

The programme is delivered by clinicians, statisticians and computer scientists from UCL, including leading figures in data science. We use a combination of lectures, practical classes and seminars. A mixture of assessment methods is used including examinations and coursework.

Further information on modules and degree structure is available on the department website: Data Science for Research in Health and Biomedicine MSc

Careers

Students on this programme will be passionate about research and know that, in the 21st century, some of the most exciting, stimulating and productive research is carried out using large collections of data acquired in big collaborative endeavours or major public or private initiatives. We hope that graduates will build on that passion and, together with the experience gained on the programme, will go one to develop careers as entrepreneurs, scientists and managers, working in industry, academia and healthcare.

Employability

The programme is designed to meet a need, identified by the funders of health research and by a number of industrial organisations and healthcare agencies, for training in the creation, management and analysis of large datasets. This programme is practical, cross-disciplinary and closely linked to cutting-edge research and practice at UCL and UCL’s partner organisations. Data science is arguably the most rapidly growing field of employment at the moment and employers recruiting in health data science include government agencies, technology companies, consulting and research firms as well as scientific organisations. A number of employers are supporting the programme in different ways, including providing paid internships to selected students.

Why study this degree at UCL?

Data science is an exciting area with a dynamic job market, including in healthcare. Our graduates have gone on to work for a range of companies, including large research organisations and small start-ups, while others are working in health care or pursuing their interests in universities.

The lecturers on this programme are international experts in health data science and students will learn about cutting-edge research projects. The collaboration is part of the Farr Institute, a network of centres of excellence created to enhance the UK’s strength in data-intensive research. This MSc will draw on that collaboration, giving students access to the most advanced research in the field.

We work closely with a range of employing organisations to ensure that our graduates have the best possible preparation for a career in data science. This includes offering industry-sponsored dissertations for selected students.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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Are you looking for a distance learning course that gives you the flexibility to combine your existing job, or other commitments, with a Masters-level qualification in the field of data analytics? This course combines core modules in information science with specialised modules in Database Modelling as well as Statistics and Business Intelligence. Read more
Are you looking for a distance learning course that gives you the flexibility to combine your existing job, or other commitments, with a Masters-level qualification in the field of data analytics? This course combines core modules in information science with specialised modules in Database Modelling as well as Statistics and Business Intelligence.

Compared to the full-time on-campus version of this course, this Masters is taught via a flexible distance learning mode and it has a slightly extended duration of 16 months. This makes it very suitable for those who are already employed as information professionals, in addition to those looking to break into the sector for the first time.

All of Northumbria’s information science postgraduate courses are accredited by the Chartered Institute of Library and Information Professionals. This accreditation makes our courses stand out and enhances their credibility and currency among employers, and is also crucial for progressing to Chartership status once qualified.

Learn From The Best

Our teaching staff include cutting-edge researchers whose specialisms overlap with the content of this course, helping ensure that teaching is right up-to-date. Specialisms include big data, data mining, decision-making, digital literacy, information behaviour, information retrieval systems, recommender systems, and the link between information science and cognitive psychology.

Our eminent academics have written books that regularly appear on reading lists for information science courses at universities all over the world. They also work as external examiners and reviewers of courses at other UK and non-UK universities.

Our course is delivered through the Northumbria iSchool, which is one of only six iSchools in the UK. A hallmark of an iSchool is an understanding that expertise in all forms of information is required for progress in science, business, education and culture. This expertise must cover the uses and users of information, the nature of information itself, as well as information technologies and their applications.

Information Science at Northumbria was established over 70 years ago and has developed in close collaboration with the profession. That dynamic working relationship has allowed us to not only reflect professional requirements, but also to be instrumental in understanding and shaping those requirements.

Teaching And Assessment

Our teaching is linked to what you want to learn and also to what you need to learn in order to achieve greater success in information science. Our long established relationship with employers ensures that you receive the most relevant and up-to-date knowledge to bring innovation, relevance, ethical sensitivity and currency to all you do. There is an emphasis on learning by doing; coursework will include projects, portfolios of work, reports and presentations as well as essays. All this helps you to make sense of the subject, getting a clear understanding of important concepts and theories.

While some assessments contribute to your final grade, there are other assessments that are provided purely to guide your progress and reinforce your learning. You can expect both your tutors and your peers to provide useful comments and feedback throughout the course.

Learning Environment

As a distance learner you will have full access to our eLearning Portal, ‘Blackboard Learn’, which includes lecture materials, web conferencing, study notes, discussion boards, virtual classrooms and communities. Blackboard Learn brings together all aspects of course management as well as assessment and feedback. Simpler technology is also effective and there’s still the option to reach tutors through a quick telephone call!

You will also have online access to Northumbria’s library, which has half a million electronic books that you can read whenever or wherever you need them. Our library was ranked #2 in the Times Higher Education Student Experience Survey for 2015 and, since 2010, it has been accredited by the UK Government for Customer Service Excellence.

The University has advanced search software and database tools, including NORA Power Search that allows you to use a single search box to get fast results from across a wide and reliable range of academic resources. The use of such software and tools is an important aspect of our information science courses.

Research-Rich Learning

In fast-moving fields like information science it’s particularly important for teaching to take account of the latest research. Northumbria is helping to push out the frontier of knowledge in a range of areas including:
-Digital consumers, behaviours and literacy
-Digital socio-technical design
-Digital libraries, archives and records

As a student, you will be heavily engaged in analysing recent insights from the field of information science. You will undertake a major individual study that will require you to evaluate relevant literature as well as to develop your ideas within the context of existing research. Your study will be tailored to your particular interests but the underlying theme will be the relationships between information, people and technology. Many of our students publish their own research and present at professional and academic conferences, before or soon after graduating.

Give Your Career An Edge

This course is accredited by the Chartered Institute of Library and Information Professionals. This reflects the relevance of the curriculum, which is informed by contact with employers and close professional links.

The topics and activities in the course have a strong emphasis on employability. For example you will develop skills in how to analyse, monitor and evaluate user behaviour. You will also learn how to evaluate and use a range of appropriate technologies for solving problems and supporting decision-making in organisations. Your knowledge and practical skills will help you take a lead on research-informed approaches that give organisations and professionals a valuable advantage.

Your Future

Data analytics is firmly in the spotlight due to transformations in information science and the emergence of big data. As we look to the future, which will be marked by ever greater capabilities for data processing, and a rising expectation that major decisions should be based on data-driven insights, data analytics will become increasingly valued and rewarded.

On graduation, you will be well placed to take advantage of this trend. Employers are looking for information professionals who can develop new insights through mastery of their subject and critical scholarship. With your Masters qualification, you will be equipped to make a difference, advance your practice and make well-balanced judgements. You could work for a wide range of employers in the field of data analytics or you could progress in a career that you have already started. Your Masters qualification can also form the basis for further postgraduate studies at a higher level.

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This programme aims to develop a high level understanding of quantitative and computational geographical methods. This includes skills in GIS software and statistical programming languages, such as R or Python. Read more
This programme aims to develop a high level understanding of quantitative and computational geographical methods. This includes skills in GIS software and statistical programming languages, such as R or Python.

Within an applied setting, emphasis is placed on developing skills in the visualisation, modelling and statistical analysis of spatial data using both web-based and traditional techniques.

Human activity are increasingly associated with the generation of large volumes of data. For example, transactional data are collated by retailers for marketing and store location purposes, administrative data are assembled to help with the efficient running of public services, data shadows are created through social media use, and an increased prevalence of smart-card linked transport systems record our travel behaviours.

Many grand human challenges concern problems of a geographical nature; be this how we can mitigate the human impact of climate change; ensure global food and water security; design energy systems that are resilient within the context of future population dynamics; or, how to design future cities where spatial inequities in health and wellbeing might be eradicated? The growing volumes of big data about the form, function and dynamics of human activities and their contexts are providing new opportunities to advance such debates within a framework of Geographic Data Science.

Why Geography?

We’ve exceptional academic staff with expertise in a range of areas:

Geographies of Population and the Lifecourse
Globalisation, Development and Place
Advanced Environmental Analytical Techniques
The study of Environmental and Climate Change.

Career prospects

Our degrees provide pathways into rewarding careers and our graduates have found employment in a wide range of industries and organisations, both in the UK and abroad. Graduates of the Environment and Climate Change MSc have gone on to continue their studies towards a PhD, or are employed in a wide range of positions, including environmental, energy and engineering consultancies, multinational companies (energy), local government, environmental bodies, research positions and teaching.

PhD graduates are now working in academic life as lecturers in Geography, Environmental Science, Economic History, Development Studies and Statistics at universities in the UK and overseas. Others are employed in applied fields, working in Europe, Africa and across the world, for example as professional statisticians (one is now Director of Statistics in Zambia, another working in the Health Service in the UK), development professionals (including a member of staff on the WHO malaria programme in East Africa), and scientists at climate and environmental research centres around the world.

Students will be well placed to undertake a career in social science research at the end of their studies, both in an academic and a non-academic environment.

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Are you looking for a Masters-level qualification that will open doors to jobs and promotions in the field of data analytics? This 2-year Masters course combines core modules in information science with specialised modules in Database Modelling as well as Statistics and Business Intelligence. Read more
Are you looking for a Masters-level qualification that will open doors to jobs and promotions in the field of data analytics? This 2-year Masters course combines core modules in information science with specialised modules in Database Modelling as well as Statistics and Business Intelligence.

As part of the focus on data analytics, you will cover topics such as how to design and manipulate databases with relational algebra and SQL. You will also analyse (big) data in order to improve organisational decision-making, using techniques such as regression analysis, clustering, distance measures, probability and distributions.

In the second year, for one semester, you’ll undertake an internship, study in another country or join a research group. This valuable experience will enhance your employability and further develop your theoretical and practical skills.

Internship
This option offers the opportunity to spend three months working full-time in one of the many companies/industries with which we have close links. You may be able to extend this over more than one semester in cases where it is adjacent to a vacation period. We will endeavour to help those who prefer this option to find and secure a suitable position but ultimately we are in the hands of the employers who are free to decide who they take into their organisation.

Research
If you take this option, you will be assigned to our Computer Science and Informatics Research Group. There is every possibility that you may contribute to published research and therefore you may be named as part of the research team, which would be a great start to a research career.

Study Abroad
We have exchange agreements with universities all over the world, including partners in Europe, Asia, the Americas and Oceania. If you take the Study Abroad option, you will spend a semester at one of these partners, continuing your studies in English but in a new cultural and learning environment. Please note that this option may require you to obtain a visa for study in the other country.

All of Northumbria’s information science postgraduate courses are accredited by the Chartered Institute of Library and Information Professionals. This accreditation makes our courses stand out and enhances their credibility and currency among employers, and is also crucial for progressing to Chartership status once qualified.

Learn From The Best

Our teaching staff include cutting-edge researchers whose specialisms overlap with the content of this course, helping ensure that teaching is right up-to-date. Specialisms include big data, data mining, decision-making, digital literacy, information behaviour, information retrieval systems, recommender systems, and the link between information science and cognitive psychology.

Our eminent academics have written books that regularly appear on reading lists for information science courses at universities all over the world. They also work as external examiners and reviewers of courses at other UK and non-UK universities.

Our course is delivered through the Northumbria iSchool, which is one of only six iSchools in the UK. A hallmark of an iSchool is an understanding that expertise in all forms of information is required for progress in science, business, education and culture. This expertise must cover the uses and users of information, the nature of information itself, as well as information technologies and their applications.

Information Science at Northumbria was established over 70 years ago and has developed in close collaboration with the profession. That dynamic working relationship has allowed us to not only reflect professional requirements, but also to be instrumental in understanding and shaping those requirements.

Teaching And Assessment

Our teaching is linked to what you want to learn and also to what you need to learn in order to achieve greater success in information science. Our long established relationship with employers ensures that you receive the most relevant and up-to-date knowledge to bring innovation, relevance, ethical sensitivity and currency to all you do. There is an emphasis on learning by doing; coursework will include projects, portfolios of work, reports and presentations as well as essays. All this helps you to make sense of the subject, getting a clear understanding of important concepts and theories.

The Advanced Practice semester will be assessed via a report and presentation about your internship, study abroad or research group activities.

While some assessments contribute to your final grade, there are other assessments that are provided purely to guide your progress and reinforce your learning. You can expect both your tutors and your peers to provide useful comments and feedback throughout the course.

Module Overview

Year One
KC7013 - Database Modelling (Core, 20 Credits)
KC7020 - Information Organisation and Access (Core, 20 Credits)
KC7021 - Statistics and Business Intelligence (Core, 20 Credits)
KC7022 - Information Systems and Technologies (Core, 20 Credits)
KC7023 - Research Methods and Professional Practice (Core, 20 Credits)
KC7024 - User Behaviour and Interaction Design (Core, 20 Credits)

Year Two
KC7026 - Masters Dissertation (Core, 60 Credits)
KF7005 - Engineering and Environment Advanced Practice (Core, 60 Credits)

Learning Environment

Northumbria uses a range of technologies to enhance your learning, with tools including web-based self-guided exercises, online tests with feedback, videos and tutorials. These tools support and extend the material that is delivered during lectures, and are available anywhere anytime. Group work and peer interaction feature prominently in our learning and teaching, this reflects the practices you’re likely to encounter within the working environment.

You will have 24/7 term-time access to Northumbria’s library, which has over half a million print books as well as half a million electronic books available online. Our library was ranked #2 in the Times Higher Education Student Experience Survey for 2015 and, since 2010, it has been accredited by the UK Government for Customer Service Excellence.

The University has advanced search software and database tools, including NORA Power Search that allows you to use a single search box to get fast results from across a wide and reliable range of academic resources. The use of such software and tools is an important aspect of our information science courses.

Give Your Career An Edge

This course is accredited by the Chartered Institute of Library and Information Professionals. This reflects the relevance of the curriculum, which is informed by contact with employers and close professional links.

The topics and activities in the course have a strong emphasis on employability. For example you will develop skills in how to analyse, monitor and evaluate user behaviour. You will also learn how to evaluate and use a range of appropriate technologies for solving problems and supporting decision-making in organisations. Your knowledge and practical skills will help you take a lead on research-informed approaches that give organisations and professionals a valuable advantage.

The Advanced Practice semester will help you develop a track record of achievement that will help you stand out from other job applicants.

A two-year master’s course, like this one, will carry particular weight with employers. They’ll understand that you’ll have a deeper understanding of topics as well as more hands-on practical experience.

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The GIS (Geographical Information Science) MSc provides an education in the theoretical, scientific and practical aspects of GIS. Read more
The GIS (Geographical Information Science) MSc provides an education in the theoretical, scientific and practical aspects of GIS. It prepares students for technical and analytical GIS roles and is in high demand; we have very close links with industry and the majority of our students find employment prior to contemplating their degree.

Degree information

Students gain a solid grounding in the scientific principles underpinning the computational and analytical foundations of GISc. Our staff are world-leading experts in the areas of programming location-enabled Apps, spatial and 3D databases, big spatio-temporal analytics, citizen science and and human computer interaction, and the MSc therefore is able to offer a wide range of options and specialisations.

Students undertake modules to the value of 180 credits. The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research project (60 credits). A Postgraduate Diploma, four core modules (60 credits), four optional modules (60 credits), full-time nine months is offered.

Core modules - core modules introduce the theory underpinning GIS, along with programming skills (python) and the basics of spatial analysis and statistcs. You'll learn to critically engage with GIS rather than just pushing buttons - how does the way data is captured and modelled influence the results of your analysis? Do you get the same results from two different GIS packages? Knowing what is inside the 'black box' means you understand analytical results and their limitations.
-GIS Principles and Technology
-Principles of Spatial Analysis
-Mapping Science
-Representations, Structures and Algorithms

Optional modules - term two is where you start to specialise, chosing modules that fit your interests, intended career choice and/or prepare you for your dissertation. At this point you can chose a heavilty technical route (e.g. databases, programming, human computer interaction) a more analytical route (spatio-temporal data mining, network and locational analysis, databases) or a mixture of the two routes. You will need to chose four modules in total. At least 30 credits of optional modules selected from :
-Geographical Information System Design
-Spatio-Temporal Analysis and Data Mining
-Web and Mobile GIS – Apps and Programming
-Spatial Databases and Data Management

Plus no more than 30 credits of optional modules (all term two) selected from :
-Airborne Data Acquisition
-Applied Building Information Modelling
-Network and Locational Analysis
-Image Understanding
-Ocean and Coastal Zone Management
-Positioning
-Research Methods
-Terrestrial Data Acquisition

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of 10,000–15,000 words. Where appropriate, this may be undertaken in conjunction with one of our many industrial partners, including Arup, Joint Research Centre, British Red Cross, Transport for London.

Teaching and learning
The programme is delivered through lectures, practical classes, demonstrations and tutorials, and is supported by a series of external speakers from industry and visits to industrial who give weekly seminars describing how GIS is used in their field as well as what they are looking for when recruiting graduate GIS students. Assessment is through unseen examinations, group and individual coursework, formal and oral presentations, and the dissertation.

Careers

There are excellent employment prospects for our graduates, with starting salaries of around £25,000. Recent GIS graduates have found openings with large engineering design firms (such as Arup or WSP), specialist consultancy firms such as Deloitte or Informed Solutions, in leading professional software companies (such as ESRI or Google), with local authorities, for organisations such as Shell, Tesco, the Environment Agency, Transport for London, NHS and the Ordnance Survey.

Employability
Students will develop specific skills including a fundamental understanding of GIS and its application to real-world problems, through theoretical lectures covering the foundations of the science – how data is captured, map creation, generalisation, spatial data management, spatial analysis, data quality and error, and spatial algorithms. Students will develop strong technical (python, R, Java, HTML, Javascript, SQL) and analytical skills (data mining, human computer interaction and usability), and in order to fully understand the principles behind GIS will make use of multiple GIS packages, both proprietary and free/open source (ArcGIS, QGIS).

Why study this degree at UCL?

This highly regarded MSc has been running for nearly 30 years and is taught by internationally recognised academics. Our specialist GIS laboratory offers the latest open source and proprietary software and our unique dual focus on the computer science and analytical aspects of GIS means that you will be able to develop your skills in multiple directions.

Our close links with industry (a strong alumni group and weekly industrial seminars) mean that you will be able to directly link your classroom learning with your future career as a GIS professional; you can also undertake your dissertation with an industrial partner.

As well as weekly industrial seminars, you will have the option to do an industry-linked project, and you will be able to attend our annual GIS careers event, which is co-organized with the UK Assocation of Geographic Infrormation.

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New to LJMU, this cutting edge programme aims to train scientists, engineers and mathematicians in the new field of Data Science. Read more
New to LJMU, this cutting edge programme aims to train scientists, engineers and mathematicians in the new field of Data Science. It combines lectures, hands-on practical experience and an extended research project, delivered by experts from two university departments.

•Study a ground-breaking curriculum linked to industry needs
•Use industry standard tools and methodologies
•Undertake an extended project using real world data science problems
•Learn from academics who are world-leading researchers
•Use the programme as a basis for PhD study


SUBJECT TO VALIDATION

The programme comprises six core modules covering statistical and computing techniques plus an extended research project.

​This MSc is designed to give you the knowledge to move into the world of work as a qualified Data Scientist or to carry out further research through a PhD or equivalent. It is delivered via a combination of lectures, tutorials and and hands-on computer laboratory sessions.

A major component of the MSc programme is the research project module, which will give you the opportunity to work on a high-level original research topic, with guidance from an experienced researcher and supervisor.

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IN BRIEF. MSc by project accredited by the British Computer Society. Gain hands-on experience of the design and implementation of databases and web applications. Read more

IN BRIEF:

  • MSc by project accredited by the British Computer Society
  • Gain hands-on experience of the design and implementation of databases and web applications
  • Learn data-mining techniques using popular tools in different application domains
  • Part-time study option
  • International students can apply

COURSE SUMMARY

This course is your opportunity to specialise in the development of web-based software systems that use databases. During your time with us, you will gain a critical awareness of the methodologies, tools and techniques used for the development of web-based computer systems and an advanced understanding of the techniques used for the development, evaluation and testing of databases.

The course also develops an awareness of the latest developments in the field of advanced databases, data mining and data warehousing. You will also gain substantial knowledge and skills in the deployment of SAS business intelligence software leading towards SAS data miner accreditation, and learn what the Semantic Web and Linked Data are, together with what these technologies enable.

COURSE DETAILS

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 often lead to ideas that you can develop for your MSc Project.

TEACHING

Teaching on this course takes the form of lectures, individual and group class work, topical class discussions and critical case study evaluation.

You will gain hands-on lab experience of using and setting up databases and web-based systems. What’s more, tutorials will give you practice in solving the theoretical and design problems associated with these systems.

ASSESSMENT

  • Coursework 60%
  • Examinations 40%

EMPLOYABILITY

With this qualification, you’ll be equipped as web/database designer and programmer, data analytics and miner among other roles. Your experience will be in high demand across all industrial and commercial sectors.

Previous students have gone on to work with companies including British Airways, Google, Hewlett-Packard, Oracle and other IT firms.

LINKS WITH INDUSTRY

Our links with industry include large companies (BT, Oracle, Microsoft) and local companies.

These companies engage with the University by giving guest seminars and often our students will work with them on their MSc Project.

FURTHER STUDY

Many of our graduates will go on to further study in our Computer Networks and Telecommunications Research Centre (CNTR)

The CNTR undertakes both pure and applied research in the general field of telecommunications and computer networking including computer networking technologies, wireless systems, networked multimedia applications, quality of service, mobile networking, intelligent buildings, context driven information systems and communication protocols. Much of this work is funded through research grants and supported by industry. In addition, members of the group are actively involved in a range of public engagement courses which aim to raise the awareness of these subjects for the general public and in schools.

Research themes in this Centre include:

  • Wireless technologies and sensor networks
  • Context and location based information systems
  • Intelligent buildings and energy monitoring
  • Communication protocols, traffic routing and quality of service
  • Network planning, traffic modelling and optimisation
  • Ubiquitous and ambient technology
  • Information security and computer forensics
  • Public Awareness


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The MPH in Palliative Care Research is designed for students wishing to pursue a service or academic career in palliative care. It will provide you with an excellent understanding of both research and public health issues, thus increasing your career opportunities. Read more
The MPH in Palliative Care Research is designed for students wishing to pursue a service or academic career in palliative care. It will provide you with an excellent understanding of both research and public health issues, thus increasing your career opportunities.

Why study Palliative Care Research at Dundee?

Dundee is ideally placed to deliver the MPH in Palliative Care. The Division of Population Health Sciences has several internationally recognised research programmes, with associated academic and research staff, and the Division also houses the renowned Health Informatics Centre (HIC) which provides researchers access to anonymised record-linked data. This includes routinely collected NHS patient datasets for the whole population.

The MPH degree has been run successfully in Dundee for over 25 years and our past students now contribute to the global public health workforce. Building on this success, we are ideally placed to offer a new exit - in palliative care research - from the core MPH. The MPH Palliative Care Research presents an opportunity to integrate public health with quality palliative care research and the clinical palliative care services. This provides a rich learning environment for prospective students.

Research led supervision

The Co-Director of the course, Dr Deans Buchanan, was recently appointed Consultant in Palliative Medicine. The Tayside Palliative Care Service is well placed to support excellence in research and in research training. The varied clinical settings throughout Tayside provide an excellent basis for research projects. Clinicians from within the palliative care service will supervise dissertations and include: Dr Rosie Conway, Dr Claire Douglas, Dr Fiona McFatter, Dr Martin Leiper and Dr Alison Morrison. In addition, Dr Bridget Johnston, Reader in Palliative Care (School of Nursing and Midwifery) will also contribute to the teaching and supervision of students.

Aims of the Programme

This course will provide you with:

* The necessary skills and expertise to enable you to undertake well designed research and interpret research data.
* The requisite communication skills and understanding of the importance of such communication.

Teaching & Assessment

This course is based in the School of Medicine. The MPH in Palliative Care Research degree course starts in September each year and lasts for 12 months on a full time basis, or 24 months on a part time basis.

How you will be taught

A variety of teaching methods will be used including traditional lectures; tutorials; discussion sessions; self directed learning including the use of internet based resources; and supervised research.

The MPH programme of studies provides teaching within a supportive environment and students are encouraged to contact lecturers to raise specific questions.

What you will study

Semester 1:
Epidemiology (15 SCQF credits)
Introduction to Clinical Statistics (15 SCQF credits)
Palliative care: Foundations and research part 1 (7 SCQF credits)

Semester 2, part 1
Research methods (15 SCQF credits)
Applied Statistics with Routine Health Datasets (15 SCQF credits)
Palliative care: Foundations and research part 2 (3 SCQF credits)

Semester 2, part 2
Spatial Epidemiology (5 SCQF credits) OR Data Visualization (5 SCQF credits)
Systematic reviews (5 SCQF credits)

Dissertation
The purpose of the dissertation is to enable students to write a dissertation which utilises all of the knowledge and expertise that they have acquired during the taught component of the course.

How you will be assessed

Performance is monitored by formal examinations and continuous assessment. Formative assessment is delivered through group and individual feedback during the tutorials, discussion sessions and on coursework. Summative assessment is based on assignments and examinations. Examinations are marked by two independent members of the School who are blinded to student identity. Guidelines for markers are provided. The dissertations are also double marked.

Careers

An MPH (Palliative Care Research) will enhance the employability of professionals interested in palliative care research.

For Specialty Trainees in Palliative Medicine this will add distinct skills sets and an essential understanding of both research and public health issues. Such qualifications will open job opportunities in academic medicine and at policy development levels. This course is compatible with the Palliative Medicine Curriculum and dissertation projects could be undertaken in candidates own localities.

Non-medical staff (including nursing staff) will benefit from the same skill set and enhanced ability to enter academic palliative care. Multi-professional learning is encouraged and dissertation projects can be tailored to specific backgrounds.

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Digital Humanities is a field of study, research, and invention at the intersection of humanities, computing, and information management. Read more

What is Digital Humanities?

Digital Humanities is a field of study, research, and invention at the intersection of humanities, computing, and information management. It is methodological by nature and multidisciplinary in scope involving the investigation, analysis, synthesis, and presentation of information in electronic form.

Digital humanists do not only create digital artefacts, but study how these media affect and are transforming the disciplines in which they are used. The computational tools and methods used in Digital Humanities cut across disciplinary practice to provide shared focal points, such as the preservation and curation of digital data, the aesthetics of the digital (from individual objects to entire worlds), as well as the creation of the born-digital.

Why Take this Course?

This M.Phil. provides a platform for a technically innovative research path within the humanities giving students the opportunity to engage with a new and dynamic area of research. It provides them with the technologies, methodologies, and theories for digitally-mediated humanities providing a framework for new and bold research questions to be asked that would have been all but inconceivable a generation ago.

Course Outcomes

Those who complete this course will have highly specialised IT skills combined with an advanced understanding of how these skills can be applied to a wide variety of digital objects (text, image, audio, and video). It will also provide students with the theories and perspectives central to the field, including the aesthetics implicit in digital creation and migration, best practice in terms of the standards used for a number of data formats, as well as the growing concerns of digital curation and preservation. Through the internship programme students will get real world experience working with cultural heritage partners or digital humanities projects. Moreover, several modules will integrate content from these partners in their learning outcomes, providing opportunities for students to engage with cutting-edge issues and technologies.

What's on the course?

This MPhil consists of three core modules and three optional modules. There is also a dissertation module in which a research topic is chosen in agreement with your supervisor.

Core modules

Theory and Practice of Digital Humanities
Web Technologies
Internship at cultural heritage institution, library, or project
Optional modules (for the 2012-13 academic year):
Cyberculture/Popular Culture
Computational Theories of Grammar and Meaning
Corpus Linguistics
From Metadata to Linked Data
Programming for Digital Media (Full year module)
Contextual Media (Full year module)
Visualising the Past
Heritage Visualisation in Action
NB: Some optional modules require prerequisites

How is it taught and examined?

The taught component of the course begins in September and ends in April. Contact hours depend on the modules you take. Theory-based modules meet for two hours a week (such as 'Theory and Practice of Digital Humanities' and 'Cyberculture/Popular Culture'); practice based modules (such as 'Web Technologies' and 'Digital Scholarly Editing') typically meet for three hours a week to include lab time. Modules are assessed through a combination of essays, in-class presentations, assignments, and projects (either individual or group), depending on the module. There are no examinations. The supervised dissertation of 15,000-20,000 words is submitted by 31 August.

Applicants should have a good honours degree (at least an upper second, GPA of at least 3.3) in any of the disciplines of the humanities. The admissions process will be carried out in two stages. In stage I candidates will apply online and have the opportunity of submitting a sample of their own critical writing (3,000-5,000 words) and a cover letter. Those candidates passing this initial assessment will go onto to stage II that will take the form of interviews (either in person, telephone, video, or skype) which will be arranged by a member of the admissions subcommittee. Taken together, these stages will allow the admissions committee to assess the candidates' general suitability for postgraduate work as well as clarifying my query re on line application]

Applications are also welcome from professionals in the library and cultural heritage sectors. Those already in employment may opt to take the degree over two years: the first year all coursework is taken and the second year the dissertation is written.

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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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Research profile. At the Centre for Intelligent Systems and their Applications (CISA) we enable computer systems to reproduce or complement human abilities, work with people, and support collaboration between humans. Read more

Research profile

At the Centre for Intelligent Systems and their Applications (CISA) we enable computer systems to reproduce or complement human abilities, work with people, and support collaboration between humans. We conduct world-leading research in the foundations of Artificial Intelligence (knowledge representation and reasoning, emergence of meaning, theory and ontology change, creativity, mathematical proof) and in intelligent collaborative systems (multiagent systems, social computation, scientific collaboration platforms, web semantics and linked data).

Our research methods are inspired by developing formal models of knowledge, reasoning, and interaction that can be used to understand and automate aspects of human intelligence, but are also understandable and usable to the human designers and users of AI systems.

To achieve this, we combine theoretical research into computational models, architectures, and algorithms with a strong element of applied research. This has led to a strong track record in using our methods to address real-world problems in healthcare, scientific collaboration, social computing, emergency systems, transportation, engineering, aerospace and others.

You'll find a wide range of research areas within CISA conducted in the four research groups the Institute currently hosts:

  • Agents and Multiagent Systems
  • Mathematical Reasoning
  • Planning & Activity Management
  • Data-Intensive Research

CISA includes one of the most innovative collaborations between research and business - our Artificial Intelligence Applications Institute (AIAI). Through its resources and the engagement of CISA staff and students in consultancy, training and joint projects, we help companies and government agencies to apply newly researched techniques.

Training and support

You will carry out research work within a research group under the guidance of a supervisor. You may also attend taught courses that are relevant to your research topic, as prescribed by your supervisor. You will be expected to attend seminars and meetings of relevant research groups. Periodic reviews of progress are conducted to assist with research planning.

A programme of transferable skills courses facilitates broader professional development in a wide range of topics, from writing and presentation skills to entrepreneurship and career strategies.

The School of Informatics holds a Silver Athena SWAN award, in recognition of our commitment to advance the representation of women in science, mathematics, engineering and technology. The School is deploying a range of strategies to help female staff and students of all stages in their careers and we seek regular feedback from our research community on our performance.

Facilities

The award-winning Informatics Forum is an international research facility for computing and related areas. It houses more than 400 research staff and students, providing office, meeting and social spaces.

It also contains two robotics labs, an instrumented multimedia room, eye-tracking and motion capture systems, and a full recording studio amongst other research facilities. Its spectacular atrium plays host to many events, from industry showcases and student hackathons to major research conferences.

Nearby teaching facilities include computer and teaching labs with more than 250 machines, 24-hour access to IT facilities for students, and comprehensive support provided by dedicated computing staff.

Among our entrepreneurial initiatives is Informatics Ventures, set up in 2008 to support globally ambitious software companies in Scotland and nurture a technology cluster to rival Boston, Pittsburgh, Kyoto and Silicon Valley.

Career opportunities

While your research studies are a perfect route to a career in academia, your degree could also take you into the commercial world of applied AI and collaborative systems.

Software developers using AI technologies are among those who rely on the insights of our research. NASA and animation company Pixar are just two of the organisations that have recently employed our graduates.



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This programme is recognised by the ESRC as a research training programme designed to provide participants with a sound background on overall research design and the most up-to-date training in methods and data collection and analysis. Read more
This programme is recognised by the ESRC as a research training programme designed to provide participants with a sound background on overall research design and the most up-to-date training in methods and data collection and analysis.

The core elements of this programme are delivered by staff from across the College of Social Sciences, many of them engaged in cutting-edge research in their own fields.

The MA programme includes assessed core modules and short courses (120 credits) and the completion of a 14,000 word dissertation (60 credits), while the Postgraduate Diploma includes the assessed courses only (120 credits).

Modules

Introduction to Social Research
This module aims to provide a general introduction to studying and research methods and prepares you for your dissertation, emphasising key skills such as searching literature, finding datasets and presenting and criticising arguments. It also covers ethics of research, the role of theory and philosophical bases for understanding the social world.

Research Design
This module links the introductory module and data collection module through consideration of research design, questions, warranting practices and sampling methods. All the elements of research design are linked into an over-arching theme of the full cycle of research activity.

Social Research Methods I
This module introduces the principles and practices of data collection and explores rationales of the various methods. It will focus on the different stages of data collection, including various methods used to gather textual and numerical data.

Social Research Methods II
This module introduces students to a range of approaches for analysing and handling data. It will include covering statistical methods for quantitative data and methodological approaches for qualitative data. It emphasises that the method of analysis is not determined by the method of collection.

British Social Policy - Beyond Welfare?
This module provides students with an understanding of recent trends in social policy development and of the current social and economic context of policy making in the UK. The question underpinning the module is 'Where is British Social Policy heading?'

Researching Social Policy
This module is concerned with the politics of social research, rather than research methods and methodology. It addresses issues such as: how are certain topics identified as subjects for research, how is research commissioned and funded, and what are the relationships between research and the policy process. It draws on real-life experiences of doing research and being researched to explore these issues.

The modules on Social Research Methods I and Social Research Methods II cover a wide range of approaches, including the 'qualitative' traditions, plus mixed methods.

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