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Master in BIG DATA. Read more
Master in BIG DATA : Data Analytics, Data Science, Data Architecture”, accredited by the French Ministry of Higher Education and Research, draws on the recognized excellence of our engineering school in business intelligence and has grown from the specializations in Decision Support, Business Intelligence and Business Analytics. The Master is primarily going to appeal to international students, "free movers" or those from our partner universities or for high-potential foreign engineers who are looking for an international career in the domain of Business Analytics.

This program leads to a Master degree and a Diplôma accredited by the French Ministry of Higher Education and research.


Business Intelligence and now Business Analytics have become key elements of all companies.

The objective of this Master is to train specialists in information systems and decision support, holding a large range of mathematic- and computer-based tools which would allow them to deal with real problems, analyzing their complexity and bringing efficient algorithmic and architectural solutions. Big Data is going to be the Next Big Thing over the coming 10 years.

The targeted applications concern optimization in the processing of large amounts of data (known as Big Data), logistics, industrial automation, but above all it’s the development of BI systems architecture. These applications have a role in most business domains: logistics, production, finance, marketing, client relation management.

The need for trained engineering specialists in these domains is growing constantly: recent studies show a large demand of training in these areas.

Distinctive points of this course

• The triple skill-set with architecture (BI), data mining and business resource optimization.
• This master will be run by a multidisciplinary group: statistics, data mining, operational research, architecture.
• The undertaking of interdisciplinary projects.
• The methods and techniques taught in this program come from cutting-edge domains in industry and research, such as: opinion mining, social networks and big data, optimization, resource allocation and BI systems architecture.
• The Master is closely backed up by research: several students are completing their end-of-studies project on themes from the [email protected] laboratory, followed and supported by members from the laboratory (PhD students and researcher teachers).
• The training on the tools used in industry dedicated to data mining, operational research and Business Intelligence gives the students a plus in their employability after completion.
• Industrial partnerships with companies very involved in Big Data have been developed:
• SAS via the academic program and a ‘chaire d’entreprise’ (business chair), allowing our students access to Business Intelligence modules such as Enterprise Miner (data mining) and SAS-OR (in operational research).

Practical information

The Master’s degree counts for 120 ECTS (European Credit Transfer System) in total and lasts two years. The training lasts 1252 hours (611 hours in M1 and 641 hours in M2). The semesters are divided as follows:
• M1 courses take place from September until June and count for a total of 60 ECTS
• M2 courses take place from September until mid-April and count for a total of 42ECTS
• A five-month internship (in France) from mid- April until mid- September for 9 ECTS is required and a Master thesis for 9 ECTS.

Non-French speakers will be asked to participate to a one week intensive French course that precedes the start of the program and allows students to gain the linguistic knowledge necessary for daily interactions.

[[Organization ]]
M1 modules are taught from September to June (60 ECTS, 611 h)
• Data exploration
• Inferential Statistics (3 ECTS, 30h, 1 S*)
• Data Analysis (2 ECTS, 2h, 1 S)
• Mathematics for Computer science
• Partial Differential Equations and Finite Differences (3 ECTS, 30h, 1 S)
• Operational Research: Linear Optimization (2 ECTS, 20h, 1 S)
• Combinatory Optimization (2 ECTS, 18h, 1 S)
• Complexity theory (1 ECTS, 9h, 1 S)
• Simulation and Stochastic Process (3 ECTS, 30h, 2 S**)
• Introduction to Predictive Modelling (2ECTS, 21h, 2 S)
• Deterministic and Stochastic Optimization (3 ECTS, 30h, 2 S)
• Introduction to Data Mining (2 ECTS, 21h, 2 S)
• Software and Architecture
• Object-Oriented Modelling (OOM) with UML (3 ECTS, 30h, 1 S)
• Object-Oriented Design and Programming with Java (2 ECTS, 30h, 1 S)
• Relational Database: Modelling and Design (3ECTS, 30h, 1 S)
• PLSQL (2 ECTS, 21h, 2 S)
• Architecture and Network Programming (3 ECTS, 30h, 2 S)
• Parallel Programming (3 ECTS, 30h, 2 S)
• Engineering Science
• Signal and System (3 ECTS, 21 h, 1 S)
• Signal processing (3 ECTS, 30h, 1 S)

• Research Initiation
• Scientific Paper review (1 ECTS, 9h, 1 S)
• Final research project on BIG DATA (5 ECTS, 50h, 2 S)
• Project Management
• AGIL Methods & Transverse Project (2 ECTS, 21h, 2 S)
• Languages and workshops
• French and Foreign languages (6 ECTS, 61h, 1&2 S)
• Personal and Professional Project (1 ECTS, 15, 1 S)
*1 S= 1st semester, ** 2 S= 2nd semester

M2 Program: from September to September (60 ECTS, 641h)
M2 level is a collection of modules, giving in total 60 ECTS (42 ECTS for the modules taught from September to April, plus 9 ECTS for the internship and 9 ECTS for the Master thesis).

Computer technologies
• Web Services (3 ECTS, 24h, 1 S)
• NOSQL (2 ECTS, 20h, 1 S)
• Java EE (3 ECTS, 24, 1S)
Data exploration
• Semantic web and Ontology (2 ECTS, 20h, 1 S)
• Data mining: application (2 ECTS, 20h, 1S)
• Social Network Analysis (2ECTS, 18h, 1S)
• Collective intelligence: Web Mining and Multimedia indexation (2 ECTS, 20h, 2 S)
• Enterprise Miner SAS (2 ECTS, 20h, 2 S)
• Text Mining and natural language (2 ECTS, 20h, 2 S)
Operations Research
• Thorough operational research: modelling and business application (2 ECTS, 21h, 1 S)
• Game theory (1 ECTS, 10h, 1 S)
• Forecasting models (2 ECTS, 20h, 1 S)
• Constraint programming (2 ECTS, 20h, 2 S)
• Multi-objective and multi-criteria optimisation (2 ECTS, 20h, 2 S)
• SAS OR (2 ECTS, 20h, 2 S)
Research Initiation Initiative
• Scientific Paper review (1 ECTS, 10h, 1 S)
• Final research project on BIG DATA (2 ECTS, 39, 2 S)
BI Architecture
• BI Theory (2 ECTS, 20h, 2 S)
• BI Practice (2 ECTS, 20h, 2 S)
Languages and workshops (4 ECTS, 105h, 1&2 S)
• French as a Foreign language
• CV workshop
• Personal and Professional Project
• Internship (9 ECTS, 22 weeks minimum)
• Master thesis (9 ECTS, 150h)


Fourteen external teachers (lecturers from universities, teacher-researchers, professors etc.), supported by a piloting committee, will bring together the training given in Cergy.

All the classes will be taught in English, with the exception of:
• The class of FLE (French as a foreign language), where the objective is to teach the students how to understand and express themselves in French.
• Cultural Openness, where the objective is to enrich the students’ knowledge of French culture.
The EISTI offers an e-learning site to all its students, which complements everything the students will learn through their presence and participation in class:
• class documents, practical work and tutorials online
• questions and discussions between teachers and students, and among students
• a possibility of handing work in online

All Master’s students are equipped with a laptop for the duration of the program that remains the property of the EISTI.

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

Who is it for?

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

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


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.


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.


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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In this day and age, data is very easy to gather and store; it’s knowing what to do with it that presents an obstacle. Read more

Secure a big future in big data

In this day and age, data is very easy to gather and store; it’s knowing what to do with it that presents an obstacle. Studies have shown that one in three business leaders do not know how to transform their data into meaningful intelligence, according to IBM ‘The Four V's of Big Data’ (http://www.ibmbigdatahub.com/tag/587). The online Master of Business Administration (MBA) with Data Analytics, is designed to help you unlock that ability, without having to set foot on campus.

This course is aimed at accomplished middle managers seeking more senior, strategic roles, helping to give them an edge over other business managers by providing contemporary data analytics expertise.

Flexible and engaging online learning

NTU is committed to offering highly relevant courses, tailored to fit around your lifestyle and career. The online MBA course provides a flexible and engaging way to learn.

Through the online modules, you will cover a breadth of management subjects, with exposure to lecturers, guest speakers and students from a host of industry sectors. You will also participate regularly in business simulations and case study projects, helping you understand the challenges of managing an organisation.

Find out more here: http://landing.online.ntu.ac.uk/mba-data-analytics?utm_source=I-%20FindAUniversity&utm_medium=Listing&utm_campaign=Basic

Course curriculum

Nottingham Business School’s online MBA is a modular course, completed over three years. The MBA course consists of 10 core modules worth ten credits each, a project module worth 40 credits, plus four additional modules in Data Analytics (total 40 credits).

Course modules:
• Responsible Leadership
• The Values-Led Organisation
• Global Marketing Management
• Operations Management
• Organisations and People Management
• Financial Management
• Business Information and Decision Making
• Strategic Change Management
• Business Research Project
• Professional and Leadership Development

Data Analytics modules:
• Statistical Approaches to Data Analysis
• Fundamentals of Big Data and its Infrastructure
• Practical Machine Learning Methods for Data Mining
• Deriving Business Value from Data Science

Career outlook

Throughout our online MBA with Data Analytics, you’ll gain an understanding of the entire life cycle of big data: capturing, organising, analysing, drawing conclusions and taking action to gain leverage or competitive advantage, giving you with a highly coveted set of skills that will be extremely attractive to prospective employers.

Graduates from our data analytics courses have found employment as senior managers in a variety of national and international organisations, along with more specific roles as data analysts, data engineers, data scientists, data architects and business intelligence analysts.

For more information visit http://landing.online.ntu.ac.uk/mba-data-analytics?utm_source=I-%20FindAUniversity&utm_medium=Listing&utm_campaign=Basic

Start your journey with NTU today

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We offer a suite of Masters programmes at Stirling. This is a one year, full time taught MSc. designed to lead to a job in data science or analytics. Read more


We offer a suite of Masters programmes at Stirling.
This is a one year, full time taught MSc. designed to lead to a job in data science or analytics.
Big Data skills are in high demand and they attract high salaries. The MSc Big Data at the University of Stirling is a taught advanced Master's degree covering the technology of Big Data and the science of data analytics.
The course is taught in the beautiful Stirling campus in the heart of Scotland with support from companies who recruit data scientists.
The course covers Big Data technology, advanced analytics and industrial and scientific applications. The syllabus includes:
- Mathematics for Big Data
- Python scripting
- Big Data theory and computing foundations
- Big databases and NoSQL
- Analytics, machine learning and data visualisation
- Optimisation and heuristics for big problems
- Hadoop and MapReduce
- Scientific and commercial applications
- Student projects

Key information

- Degree type: MSc
- Duration: One year
- Start date: September
- Course Director: Kevin Swingler

Course objectives

- An understanding of the issues of scalability of databases, data analysis, search and optimisation
- The ability to choose the right solution for a commercial task involving big data, including databases, architectures and cloud services
- An understanding of the analysis of big data including methods to visualise and automatically learn from vast quantities of data
- An appreciation of the size of search spaces in large problems and the ability to choose an appropriate heuristic to find a near optimal solution
- The programming skills to build simple solutions using big data technologies such as MapReduce and scripting for NoSQL, and the ability to write parallel algorithms for multi processor execution.

English language requirements

If English is not your first language you must have one of the following qualifications as evidence of your English language skills:
- IELTS: 6.0 with 5.5 minimum in each skill
- Cambridge Certificate of Proficiency in English (CPE): Grade C
- Cambridge Certificate of Advanced English (CAE): Grade C
- Pearson Test of English (Academic): 54 with 51 in each component
- IBT TOEFL: 80 with no subtest less than 17

For more information go to English language requirements https://www.stir.ac.uk/study-in-the-uk/entry-requirements/english/

If you don’t meet the required score you may be able to register for one of our pre-sessional English courses. To register you must hold a conditional offer for your course and have an IELTS score 0.5 or 1.0 below the required standard. View the range of pre-sessional courses http://www.intohigher.com/uk/en-gb/our-centres/into-university-of-stirling/studying/our-courses/course-list/pre-sessional-english.aspx .

Structure and content

Our Big Data MSc is a mix of practical technology such as Hadoop, NoSQL, and Map-Reduce, important maths and computing theory, and advanced computational techniques. The course will teach you what you need to know to collect, manage and analyse big, fast moving data for science or commerce


In REF2014 Stirling was placed 6th in Scotland and 45th in the UK with almost three quarters of research activity rated either world-leading or internationally excellent.


Stirling is a member of The Data Lab, which is an Innovation Centre with the aim of developing the data science talent and skills required by industry in Scotland. The data lab with facilitate industry involvement and collaboration and provide funding and resources for students.
The Stirling MSc in Big Data has been developed in partnership with global and local companies who employ data scientists. HSBC have a development centre in Stirling and have provided some very interesting Big Data projects to our students. Amazon’s development centre in Scotland is close by in Edinburgh. The course features a long summer project, generally in partnership with a company or technology provider, that provides students with a showcase of their skills to take to employers or launch online.
We also have a programme of invited speakers from industry who give the students a chance to ask questions of people who are doing data science every day. Recent companies have included MongoDB, SkyScanner and HSBC.

Career opportunities

Demand for people with big data skills is projected to grow rapidly in the coming years. Average salaries are higher in Big Data jobs than the IT average and the skills shortage will make that gap bigger.
The Stirling Big Data MSc is run in partnership with industry and is designed to produce graduates with the skills that companies need.
e-Skills UK estimate that:
- The number of Big Data jobs in the UK rose by 41% from 2012 - 2013
- By 2020 there will be 56,000 Big Data jobs in the UK alone
- Big Data professionals earn on average 31% more than other IT professionals
- 77% of companies say it is difficult to recruit people with the Big Data skill they need

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The opportunity to exploit Big Data is recognised world-wide and some countries include it in their economic strategies. The UK Government identified Big Data as one of the 8 great technologies which will have a strong impact on growth and the Scottish Government highlights it as an emerging opportunity for Scotland. Read more
The opportunity to exploit Big Data is recognised world-wide and some countries include it in their economic strategies. The UK Government identified Big Data as one of the 8 great technologies which will have a strong impact on growth and the Scottish Government highlights it as an emerging opportunity for Scotland.

Our MSc in Data Science aims to produce specialist data scientists with training in industry relevant data acquisition, storage, warehousing, analytics and visualisation tools and techniques and a good understanding of the needs of industry. The course will prepare graduates in technical disciplines for a career in the design and implementation use of computer-analytics and visualisation solutions for industry.

Visit the website: http://www.rgu.ac.uk/computing/study-options/postgraduate/masters-in-data-science

Course detail

The course will focus on satisfying industry’s demand for data scientists who have the ability to:

• Apply appropriate data science tools and techniques to industry’s data in order to uncover important, previously unknown information only implicit in the data.
• Relate a company’s key performance indicators to a data science problem area in order to focus a data science task.
• Handle large amounts of real-time, non-persistent, data.
• Contribute to business decision-making by effectively communicating (potentially large volumes of) key data visually.
• Understand, clean up, summarise, interpret and manage data.
• Grasp key knowledge about new problem areas in order to communicate with end-users; understand key business needs and processes and identify added value through data analytics.
• Provide user-centred data analytics at an appropriate level.
• Protect and share data as appropriate.

The course will emphasise Big Data, covering not only traditional data management systems but also systems where data and/or its storage is unstructured.


Throughout the course, content is complemented by practical work, allowing you to support your theoretical knowledge with practical experience in data storage, mining, warehousing, visualisation and analysis as well as transferrable skills. You will be taught through a mixture of lectures, tutorials, labs. You will be invited to attend talks presented by highly-experienced researchers, speakers from industry, and members of the BCS (British Computer Society) on a wide range of industry-related topics. You will also be supported through our online virtual learning environment where you can access a wide variety of resources and other support materials.

The individual project provides an opportunity for applying specialist knowledge together with analytic, problem-solving, managerial and communication skills to a particular area of interest within data science. Working with the full support and guidance of an allocated project supervisor, you will be given the opportunity to propose, plan, specify, develop, evaluate, and present a substantial project.

Placements and accreditation

Students who perform particularly well during their first semester of studies will be invited to apply for a 45-week internship.


The course prepares you for a career in Data Science. Job openings include: Data Scientist, Data Analyst, Data Visualisation Specialist, Data Manager, Database Designer/Manager, Data Mining Expert and Big Data Scientist.

Aberdeen is home to many multinational oil and gas companies and associated suppliers such as mainstream software houses, IT providers to major oil-related companies, specialist software consultancies, and venture capital start-ups.

The university is involved in a number of commercial collaborations on a local, national and international scale with organisations such as BP, British Geological Survey, Wood Group PSN, Accenture, WIPRO and many Aberdeen-based software development companies.

The course also prepares students for research careers by providing the skills necessary of an effective researcher. Suitable MSc graduates may continue to PhD programmes within the school.

How to apply

To find out how to apply, use the following link: http://www.rgu.ac.uk/applyonline


For information on funding, including loans, scholarships and Disabled Students Allowance (DSA) please click the following link: http://www.rgu.ac.uk/future-students/finance-and-scholarships/financial-support/uk-students/postgraduate-students/postgraduate-students/

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Typically information governance/security and law have been taught as distinct subjects in different discipline areas. Read more
Typically information governance/security and law have been taught as distinct subjects in different discipline areas. In recognition of the relationship that exists between information governance/information security and the protection of personal data this programme brings together these subjects in one multi-disciplinary qualification.

The Postgraduate Certificate in Data Protection Law and Information Governance is a distance learning course that has been specifically designed to meet the needs of professionals already working in data protection and/or information governance. You will study three modules. The first of these, the legal research module, will develop your ability to undertake legal research and to present your research findings appropriately. In the second module you will develop your understanding of information governance and security principles that underpin the management of an organisation’s information assets. The third module will focus upon data protection law and practice.

The programme will not only provide you with valuable knowledge of current law and proposed developments to the law and to the principles of information governance, it will also enhance your ability to advise upon both information governance and data protection. The research, writing and presentation skills you will develop will also be of use to you in your working environment. Unlike typical CPD type learning this programme will challenge you to undertake critical evaluation of the law and to consider the application of information governance and security to your own/a chosen organisation.

Learn From The Best

This programme is delivered jointly by academics within Northumbria Law School and the iSchool, in the Faculty of Engineering and Environment. Northumbria Law School is actively involved in research and consultancy in the field of data protection, information sharing, freedom of information and privacy law. The iSchool, which delivers the information governance and security module, is widely recognised for its innovative distance and work-based learning programmes in information and records management and for its related research.

This course is delivered by a team of solicitors and academics with extensive experience in data protection and information governance, who are actively researching the area. In addition our team also boast memberships to key professional bodies, in addition to editing industry publications such as the Records Management Journal.

Teaching And Assessment

This course is primarily delivered online to provide flexibility and the ability for you to study at times convenient to you. We believe, however, that opportunities to engage with your tutors and with fellow students are an important part of your learning experience. On two of the modules you will be offered the opportunity to meet your tutors and attend lectures or workshops at the University at an optional study day. All of the content will be available online should you not be able to attend. On the third module you will be encouraged to engage with your tutor and with fellow students via the module discussion board.

Module Overview
KC7046 - Information Governance and Security (Core, 20 Credits)
LW7002 - Data Protection (Core, 20 Credits)
LW7003 - Legal Research (Core, 20 Credits)

Each taught module is assessed via written assignment. On the legal research module you will work in a group with other postgraduate students to undertake the research, writing and review of that assignment. On the data protection module and the information governance module you will submit an individual written assignment at the end of each module. As part of the assessment process you will be expected to undertake a critical evaluation of the law, and to consider information governance and security in your own or another chosen organisation.

Learning Environment

Your course will be delivered online using the latest innovative software. Learning materials such as module handbooks, assessment information, lecture presentation slides, recorded lectures and electronic reading lists will be available via our highly accessible e-learning platform, Blackboard. You can also access student support and other key University systems through your personal account.

Research-Rich Learning

Research Rich learning (RRL) is embedded across the programme, reflecting the pervasive research culture of the law school. Your student journey commences with the Legal Research module. This module will help you to gain a clear awareness and understanding of appropriate legal research methods and legal sources and how to cite those sources. In your subsequent modules your tutors will expose you to a range of academic literature covering substantive data protection law and relevant information governance and data security frameworks and principles. You will also develop your legal research skills further as your tutors encourage you to discuss, evaluate and critically examine relevant principles and frameworks and as you undertake your own research in order to complete your module assignments.

Give Your Career An Edge

It is envisaged that most students who study this programme will already be employed within the data protection/information governance fields. It recognises that the introduction of a new data protection regulation will result in significant challenges for professionals working in the data protection field, and seeks to help you to develop the skills and knowledge which you will need to do your job professionally notwithstanding the changing legislation framework.

Your Future

This course provides academic recognition of your knowledge of data protection and information governance law and your ability to apply that knowledge to practice. It also provides a strong foundation for further study. Should you decide upon completion of the programme that you wish to further develop your knowledge of information rights law or information governance/security then Northumbria Law School and the Faculty of Engineering and Environment both offer masters programmes in these fields. This programme provides you with a stepping stone towards study a Masters in Law (an LLM). Successful completion of this programme exempts you from study of the first three modules on the Pg Dip/LLM in Information Rights Law and Practice.

What Does Britain Leaving The EU Mean For This Course?

We can confirm that we will not be changing the course in light of the Brexit decision. The focus in this course will be the current legal framework, and any likely reforms including the GDPR. There are several reasons why the course will not be changed at this particular point. Firstly there are no changes to the current legal framework on data protection or environmental information. This is well explained in a statement by the information commissioner's office https://ico.org.uk/about-the-ico/news-and-events/news-and-blogs/2016/06/referendum-result-response/ and was reiterated by Baroness Neville-Rolfe, the Government Minister responsible for Data Protection, on 4 July. These statements also acknowledge that there is a need for reform in data protection and that would have to be seen in the context of European data protection laws. Although it is not clear what the exact relationship of the UK and EU will be in the future there is a recognition that there will be a need for equivalency of data protection law in the UK with other countries. The need for equivalency of the law is likely to be necessary whether the UK is part of the single market, or if it exits the European economic area, in order for EU countries to send data to us as part of the 8th principle (See Schedule 1 of the Data Protection Act 1998). As such the GDPR still has relevance in our understanding of what would be required to achieve equivalent protection and what likely reforms on data protection may be considered in the UK. From an educational perspective the examination of reforms such as the GDPR provide a useful mechanism to critique current data protection laws, allowing for the discussion of strengths and weaknesses, even if all those reforms are not ultimately adopted. We will of course keep the position under review, as we do with all our teaching areas in order to ensure that learning material reflects both the current law and likely changes to that law.

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

About the Course

1. Big Challenges being addressed by this programme – motivation

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

2. Programme objectives & purpose

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

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

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

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

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

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

5. Programme Content – module names

Sample Foundational Modules:

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

Sample Advanced Modules:

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

6. Testimonials

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

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

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

For further details

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

How to Apply:

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

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

Scholarships :

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

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

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The developments of the internet have given database journalism a new definition, according to which it defines a process where the database becomes the center of the journalistic work (as opposed to the story in traditional journalism). Read more
The developments of the internet have given database journalism a new definition, according to which it defines a process where the database becomes the center of the journalistic work (as opposed to the story in traditional journalism). It slowly evolved into data journalism; a journalistic process based on analyzing and filtering large data sets for the purpose of creating a new story.

This new international Master's program explores the opportunities of data journalism from four angles: data retrieval, data storytelling, data visualization and data publishing. It combines the scientific methods of data treatment with the core values of journalism: select, arrange, digest and reflect. The Data Journalism Master's track puts a strong focus on online and social media based journalism.

The master track Data Journalism (DJ) has a natural connection with Human Aspects of Information Technology (HAIT) and Communication Design (CD). Embedded in the strong Communication and Information Sciences program, Tilburg University believes it has launched a unique Master's program in which students learn how to transfer information and how new means of communication can be used.

Career Prospects Data Journalism

After completing the MSc specialization Data Journalism, a broad range of career paths in business, research and education will be open to the student. A graduate will be able to work and consult on data journalism and work in a broad range of media (related) companies or institutes. With a master's degree, a student can also start a career as a scientific researcher in this field. These opportunities are worldwide because the master is internationally-oriented.

Core competences:
•Ability to select data from a broad range of data sources
•Ability to analyze and abstract data from a scientific perspective
•Ability to explore and detect abnormity in data
•Familiarity with various data standards & the ability to convert
•Ability to visualize data from a journalistic perspective in graphics and text
•Ability to transform data in a journalistic storyline
•Ability to plan and organize innovative data projects

International careers:
•Data journalist
•Research journalist
•Data consultant
•Data researcher
•Interaction designer
•Multimedia storyteller
•Innovation officer
•Project manager new media
•Data scientist

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

Programme description

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

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

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

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

Online learning

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

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

Programme structure

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

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

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

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

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

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

Option courses

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

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

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

Learning outcomes

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

Career opportunities

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

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This innovative new course developed in collaboration with CDA (Common Data Access Ltd) provides flexible entry to an education in the field of petroleum data management. Read more
This innovative new course developed in collaboration with CDA (Common Data Access Ltd) provides flexible entry to an education in the field of petroleum data management. The course has been designed as an access route for those with relevant work experience in the energy sector who do not currently have the necessary qualifications in this area. The course facilitates access to any of our Masters Degree courses available from the Department of Information Management and enhances professional career development.

You will be able to draw upon your current and previous work experience within the modules. This approach will allow you to analyse both the organisation and its approach to petroleum data management, and to utilise the course content to improve strategic organisational effectiveness. In addition, it encourages you to consider any practical problems that may arise in the execution of any activities, and to reflect critically on the value of your own organisational input.

Visit the website: http://www.rgu.ac.uk/information-communication-and-media/study-options/distance-and-flexible-learning/petroleum-data-management

Course detail

The Petroleum Data Management Graduate Certificate aims to promote the understanding of subsurface exploration and production data and evaluate its importance to upstream oil and gas businesses. The course focuses on managing subsurface exploration and production data throughout its life cycle from capture to realisation until it becomes obsolete.

You will study four modules over the academic year, each assessed through coursework assignments:

• Managing Subsurface Exploration and Production Data
• The Data Management Life Cycle
• Providing Data Management Services
• Data Quality and Governance


The course will be offered online. Our supported distance learning mode of delivery allows you to study online from any location and is designed to fit in around any existing work commitments.

Our virtual learning environment, CampusMoodle offers students flexibility of where and when they can study, offering full and open access to tutors and other class members. Students have the benefit of being part of a group of learners with the invaluable opportunity to participate in active, group-related learning within a supportive online community setting. The online campus provides students with lectures and course materials and it also includes:

• Virtual tutorials
• Live chat
• Discussion forums - student and tutor led
• Up-to-date web technology for delivery methods
• User friendly material
• Access to our online library

As online learners, students are part of a 'virtual cohort' and the communication and interaction amongst members of the cohort is a significant aspect of the learning process.


There is a growing recognition of the need for more effective data management in the energy sector. In addition to gaining a recognised industry-focussed qualification, this course will also provide access to our CILIP accredited courses allowing you to develop your knowledge and career further, and to undertake a diverse range of roles in the energy sector including:
• Data analyst
• Information scientist
• Records manager


The award of Graduate Certificate Petroleum Data Management given on completion of this course has been promoted by CDA as a means of promoting the professionalisation of data managers within the energy sector.

How to apply

To find out how to apply, use the following link: http://www.rgu.ac.uk/applyonline


For information on funding, including loans, scholarships and Disabled Students Allowance (DSA) please click the following link: http://www.rgu.ac.uk/future-students/finance-and-scholarships/financial-support/uk-students/postgraduate-students/postgraduate-students/

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

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

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

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

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

Why choose this course?

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

- Use of SPSS.

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

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

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

This course in detail

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

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

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

Teaching and learning

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

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

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

Approach to assessment

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

Attendance pattern

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

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

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

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

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

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


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

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

Free language courses for students - the Open Module

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

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

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Our MBA (Data Analytics) will cover everything you need to know to run a successful business. Topics include finance, communications and management, while the specialised data analytics modules introduce you to the art and sciences of using raw data for business analysis and intelligence. Read more
Our MBA (Data Analytics) will cover everything you need to know to run a successful business. Topics include finance, communications and management, while the specialised data analytics modules introduce you to the art and sciences of using raw data for business analysis and intelligence. You’ll study these modules alongside professionals and students from related disciplines, providing you with a collaborative and supportive environment for networking.

More about this course

London Met’s MBA (Data Analytics) course includes the fundamentals of business administration, an exploration of data analysis and the chance to conduct your own specialised research.

So you can grow into a successful business leader, the core modules teach you principles of business administration such as accounting and finance, leadership, management, marketing and communications.

The specialised data analytics modules are Data Mining for Business Intelligence and Data Modelling and Online Analytical Processing (OLAP) techniques. Studying these analytical processing methods will further enhance your ability to make informed and strategic decisions within companies.

Our teaching staff and visiting academics, who you’ll meet both informally and formally through lectures and social events, are experts in areas including strategy, management, coaching and data analytics.

Throughout the MBA, you’ll collaborate with students from a variety of professions and disciplines, including specialist postgraduate data analytics students. Your specialised training will be supplemented by regular informal learning activities including the weekly student-led Business Breakfast; a monthly dinner; networking events; meetings with business leaders; entrepreneurs and consultants and lively charity fundraising events in the City.

We’ll provide regular coaching sessions to help improve your career potential, while you can also make use of our Careers and Employability Unit to help you find new roles in preparation for life after the MBA.

You’ll be assessed through individual and group work. This is likely to come in a variety of forms including reports, portfolios, presentations, videos, conferences and competitions, enabling you to develop the skills to master a multitude of situations in the world of data analytics.

Modular structure

Core modules:
-Accounting and Finance for Managers
-Leadership and Strategic Management
-People and Organisations: Principles and Practices in Global Contexts
-Marketing, Marketing Communications and Operations

Data Analytics modules:
-Data Mining for Business Intelligence
-Data Modelling and OLAP Techniques for Data Analytics

Research-focused modules:
-Management Learning and Research
-Business Research Project

After the course

Graduates of the MBA may continue in their existing careers or choose to explore new opportunities. Recent graduates of our business related degrees are employed by companies including Oxademy, ALDI, Schwab Versand Hanau, Sapa, UBM plc, Carillion, Hanson Hispania SA, Triometric and BNP Paribas. They work in management roles in the fields of international sales, area management, business development, clients services and customer service.

Roles particularly relevant to the field of data analytics management include data science manager, business intelligence manager and CRM database manager.

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The current model of healthcare delivery in the UK is subject to unprecedented challenges. An ageing population, the impact of lifestyle factors and increasing costs mean that the existing approaches may become unsustainable. Read more
The current model of healthcare delivery in the UK is subject to unprecedented challenges. An ageing population, the impact of lifestyle factors and increasing costs mean that the existing approaches may become unsustainable.

This, coupled with a drive towards personalised medicine, presents an opportunity for a step change in healthcare delivery.

To do this, we need to make best use of the health data we collect and create a better understanding of the relationship between treatments, outcomes, patients and costs.

This requires individuals who understand:
-The healthcare sector and medicine
-How data is collected and analysed
-How this can be communicated to influence various stakeholders

Our MSc in Health Data Science aims to create a new breed of scientist able to work across all three sectors - health data scientists who will be at the forefront of this step change in healthcare delivery.

The course promotes the need for translational thinking to provide the knowledge, skills and understanding that will be applied across new challenges within healthcare delivery.

Students from a variety of professional backgrounds will benefit from the course, as the structure of the MSc ensures that you will share this knowledge with each other and learn to work in multidisciplinary teams, rather than in specialist silos.

The course has nine taught units covering key skills for health data science. Seven units are core and there is one optional unit depending on training needs and background. For those studying for an MSc, there is also a 60 credit research project.


This course will allow you to:
-Gain key background knowledge and an understanding of the healthcare system, from the treatment of individuals to the wider population;
-Gain an understanding of the governance structures and frameworks used when working with health data and in the healthcare sector;
-Experience key technical skills and software for working with and manipulating health data;
-Understand the breadth and depth of application methods and the potential uses of health data;
-Comprehend key concepts and distinctions of the disciplines that need to be synthesised for effective health data science;
-Appreciate the role of the health data scientist and how they fit into the wider healthcare landscape;
-Understand the importance of patient-focused delivery and outcomes;
-Develop the in-depth knowledge, understanding and analytical skills needed to work with health data effectively to improve healthcare delivery;
-Develop a systematic and critical understanding of relevant knowledge, theoretical frameworks and analytical skills to demonstrate a critical understanding of the challenges and issues arising from heterogeneous data at volume and scale, and turn them into insight for healthcare delivery, research and innovation;
-Apply practical understanding and skills to problems in healthcare;
-Work in a multi-disciplinary community and communicate specialist knowledge of how to use health data to a diverse community;
evaluate the effectiveness of techniques and methods in relation to health challenges and the issues addressed;
-Extend your knowledge, understanding and ability to contribute to the advancement of healthcare delivery knowledge, research or practice through the systematic, in-depth exploration of a specific area of practice and/or research.

Special features

MSc students will have an opportunity to conduct their research project in other settings such as the NHS and the biopharmaceutical industry, as well as academia.

Teaching and learning

The course covers four main areas that bring together technical, modelling and contextual skills to apply these to real world problems when harnessing the potential of health data.

In each of the units that deliver the key skills, both the importance of the patient and the governance surrounding working in the healthcare environment (especially structures around information governance) is embedded throughout.

Each unit will use case studies provided by existing work and research at the Health eResearch Centre (HeRC). The course will focus on large and complex health datasets (often routinely collected) in environments that safeguard patient confidentiality.

The course will encourage intellectual curiosity, creativity, and critical thinking, providing transferable skills for lifelong learning and research and cultivation of reflective practice.

Through the development of these innovation, critical, evaluative, analytical, technical, problem solving and professional skills, you will be able to conduct impactful work and advance healthcare delivery.

We see learning and teaching as collaborative knowledge construction, which recognises the contribution of all stakeholders (academic staff, service users and carers and students). This is demonstrated in the course through contributions made by these stakeholders through case studies, examples, invited seminars and participation in group work.

A variety of teaching methods will be used within the constraints of the method of delivery. The course will be student centred and will be delivered from the outset using a combination of face-to-face, distance learning and blended learning units.

Coursework and assessment

A range of assessments are used within each course unit and across the course as a whole.

All assessments require you to integrate knowledge and understanding, and to apply this to case studies and the outcomes of each unit.

Assessment will occur in a variety of forms including (but not exclusively) essays, case studies, assessed seminar/tutorial presentations and literature reviews.

Written assignments and presentations have a formative role in providing feedback (particularly in the early stages of course units) as well as contributing to summative assessment.

Online quizzes provide a useful method of regular testing, ensuring that you actively engage with the taught material.
The assessment of tutorials contains an element of self and peer evaluation, so you can learn the skills associated with the effective management of and participation in collaborative activity.

The course also places an emphasis on group work, as this a vital skill for professionals operating in a multidisciplinary area such as health data science, and this is shown in the teaching methods and assignments.

Each unit has a different emphasis on the group work assessment based on the nature of the material being covered, how they are to apply the knowledge and the work they are to complete.

The dissertation for the MSc requires you to undertake an extended written piece of work (10,000 to 15,000 words) that focuses on a specific aspect of health data science.

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The Online MSc in Data Analytics covers 18 5-credit modules, two per semester over 9 semesters or 3 years, of which the Online Professional Diploma in Data Analytics covers the first 4. Read more
The Online MSc in Data Analytics covers 18 5-credit modules, two per semester over 9 semesters or 3 years, of which the Online Professional Diploma in Data Analytics covers the first 4. This first year is designed to introduce you to statistical and mathematical concepts in Data Analytics and Data Mining, and to get you started on programming with data. The second year is split between understanding the theory behind statistical and mathematical models for data via predictive analytics, and dealing with data sets at scale using Python and multivariate techniques. The final year covers some advanced methods: Monte Carlo, Bayesian Analysis, Time Series Data, and Complex Stochastic models. A provisional list of topics is as follows:

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MSc Media and Communication (Data and Society) offers an intensive, year-long exploration of the significance of data and information within contemporary societies and communications. Read more

About the MSc programme

MSc Media and Communication (Data and Society) offers an intensive, year-long exploration of the significance of data and information within contemporary societies and communications. At a time when intensive data-gathering about online activity is central to both business models and to governments’ strategies for understanding their citizens, the programme’s critical perspective on the “move towards data” is highly relevant, allowing you to understand, evaluate and respond to the social and political contexts of data production and analytics. You will also consider the cultural aspects of data’s role within everyday life.

The programme provides you with the resources to understand the wider implications of a social shift towards data (as highlighted in recent debates about the data-gathering of the NSA and social media platforms). The programme will also teach you skills in understanding how data processes can be constructed, managed and renewed to fulfil social and civic ends, identifying the ethical questions raised by data’s growing role in communication and social processes and what approaches might resolve them, and understanding the significance of data-collection processes.

Graduate destinations

This programme will provide students with an understanding of how data shapes social life specifically through communication processes. This is useful for future careers in media and communication fields that are increasingly bound up with information systems and data development, such as: advertising and marketing, data analytics, legal and political consulting, information management, and editorial.

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