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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

Introduction

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

REF2014

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.

Strengths

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 Big Data in Business pathway will provide you with the knowledge and skills to understand and direct the strategic use of the vast amounts of information being generated by businesses today. Read more

The Big Data in Business pathway will provide you with the knowledge and skills to understand and direct the strategic use of the vast amounts of information being generated by businesses today.

Commercial focus

Our students learn to develop a strategic approach to managing Big Data in business, through the analysis of business problems as well as understanding different approaches to business intelligence. Through this, they are able to create usable business intelligence to create competitive advantage for their organisation.

After you’ve graduated

Our graduates have will leave us with the knowledge and skills necessary to analyse and manage Big Data to benefit business in a variety of sectors.

Not sure which pathway to choose from 3 choices? Apply for the one that you feel fits you better and you will be able to change the pathway within the first few weeks from your arrival to the university.

Why Henley?

  • Consistently maintain highest standards: Henley is in top 1% of business schools worldwide to hold accreditation from all three bodies in the UK, Europe and US
  • Excellent networking potential : 72,000 Henley alumni members in 150 countries
  • High calibre students: always oversubscribed, 1,000 ambitious new Masters students join Henley each year
  • Award winning campus: beautiful, green, 134 hectares, with state of the art facilities
  • World-leading faculty: widely published, frequently asked for expert comment by media and to speak at events
  • Henley is proud to be part of the University of Reading. The University is ranked within the top 200 universities worldwide (Times Higher Education World University Rankings 2016/17 and QS World University Rankings 2018) and 98% of the research is rated as being of international standard.

Course content

Compulsory modules

Optional modules

In addition students must choose two optional module from the list below.

Please note there is no guarantee that in any one year all modules will be available. 

How we teach you

A holistic approach

Effective leadership requires more than first-class business acumen. It also requires a degree of self-awareness and sensitivity. Henley is renowned for its well-researched, professional approach to this aspect of business education and all our postgraduate programmes examine this aspect of leadership - helping to create emotionally intelligent graduates who can be fully effective in their chosen careers.

How you will learn

Henley Business School enjoys a strong reputation for the practical application of business ideas and concepts, underpinned by academic excellence and the strength of our research. We offer high-quality technical skills training as well as a deep understanding of the importance of personal development for leaders, a thread that runs through all of our Masters programmes.

Our postgraduate masters programmes feature a mix of core and optional modules, allowing you to tailor your degree towards your individual personal development needs and career ambitions. You will complete up to 10 taught modules during your programme, totalling 180 credits. One module usually equates to 20 credits or 10 hours of work per week. Your week will include lectures, tutorials, workshops and personal study, with each accounting for 25% of your time on average. This stimulating mix of lectures and interactive tutorials provides you with the opportunity to discuss and explore the subject material in depth with your lecturers and fellow students. You will be introduced to the latest thinking and research findings and be able to challenge some of those that have created it. You will also explore real-world issues and tackle current business challenges, and interact with guest lectures and speakers from industry, giving you the opportunity to test, extend and refine your knowledge and skills.

How we assess you

You will learn and be assessed through a wide variety of teaching methods which vary depending on your chosen Masters programme. These include online materials and multimedia content, guest lectures, individual and group assignments, case studies, field visits, dealing room simulations, presentations, applied projects, consultancy work and examinations.

On average examinations form around 70% of the assessed work with the remaining 30% coming from coursework, including a written dissertation or project depending on your chosen programme. The exam period falls between April and June in the summer term, with students taking an average of 5 or 6 exams. Graduation normally takes place in December.

Ongoing support

While postgraduate students are self-motivated and determined individuals, study at this level can present additional pressures which we take seriously. Lecturers are available to discuss the content of each module and your personal tutor can meet with you regularly to discuss any additional issues. Full-time support staff are also available to help with any questions or issues that may arise during your time at Henley

Careers and accreditations

Each pathway of our MSc Information Management is designed to give a rigorous academic understanding of real-life and current business issues. Graduates of the Big Data in Business pathway will be equipped to develop strategies to manage Big Data. These skills are much in demand, in a variety of fields.  

A number of our students join our PhD programmes each year.

Students who pass the module – Business Domain and Requirements Analysis with a mark of 60 or above will be eligible for the British Computer Society Professional Certificate in Business Analysis Practice.



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Our Big Data in Culture & Society MA recognises the growing importance of Big Data in contemporary society and addresses the theory and practice of Big Data from an arts and humanities perspective. . Read more

Our Big Data in Culture & Society MA recognises the growing importance of Big Data in contemporary society and addresses the theory and practice of Big Data from an arts and humanities perspective. 

What is Big Data? Beyond the unprecedentedly large data sets that can be analysed to reveal patterns, trends, and associations, it is increasingly about our everyday lives. In short, it is about how the data we generate is transforming social, cultural, political and economic processes as well as the generation of knowledge.

This course is likely to appeal to a broad range of students across the Arts and Humanities from Sociology to Political Science to English to Business and beyond. It will attract forward-thinking students interested in emerging trends who recognise that data scientists and analysts require collaborators with domain specialisation and critical insights.

  • Taught by scholars working at the leading edge of digital studies and Big Data.
  • Offers a lively mix of theory and practical work.
  • Equips students with skills that are highly attractive to employers in our digital age.
  • Provides a series of workshops with data scientists and analysts to learn collaborative practices and applications in social media and cultural analytics, mobile platforms, and data visualisation.
  • Is at the forefront of digital developments - Big Data is transforming society, politics, the economy and culture and impacting work
  • Offers innovative interdisciplinary methods of study crossing technological and cultural perspectives
  • Links Big Data to Culture, Law & Ethics, Geography, Public Health, and Social Life
  • Located in a highly ranked department - the Digital Humanities department was ranked first in the UK for research power (2014 Research Excellence Framework)

Description

This Big Data in Culture & Society MA offers you the opportunity to develop your knowledge and understanding of the role of Big Data in culture and society. It will enable you to analyse Big Data across social, political and economic areas. In addition to the required content we cover, you will have the opportunity to pursue your own academic interests through our optional modules and to undertake an internship and a group project module.

By bringing together domain knowledge and technical skills and approaching these from an Arts and Humanities perspective, the course will help you develop highly valued employment skills and expertise for careers in Big Data.

The course will provide you with:

  • Knowledge and understanding of the effects of Big Data on contemporary society.
  • Critical and theoretical approaches to the analysis of Big Data.
  • Knowledge of the historical antecedents of Big Data.
  • Understanding of the innovative methods for generating new knowledge through the use and analysis of Big Data.
  • Understanding of Big Data in relation to the broader study of digital culture, the digital humanities and traditional humanities disciplines.
  • Understanding of appropriate personal and professional conduct in the context of digital culture as an emerging discipline. 

Course purpose

The MA Big Data in Culture and Society offers students the opportunity to develop their knowledge and understanding of the role of Big Data in culture and society. It enables them to analyse Big Data across social, political and economic areas and provides them with a background for pursuing careers in Big Data by bringing together domain knowledge and technical skills. 

Course format and assessment

Teaching

If you are a full-time student, we will provide you with 120 to 180 hours of teaching through lectures and seminars across the year. We expect you to undertake around 1,674 hours of independent study.

If you are a part-time student, we’ll provide you with 90 hours of teaching through lectures and seminars in your first year, and 50 hours in your second. We’ll expect you to undertake 720 hours of independent study in your first year and 954 hours in your second.

Typically, one credit equates to 10 hours of work.

Assessment

We assess our modules entirely through coursework. This will comprise a mixture of essays, project work, and workshop reports, depending on the modules you choose.

Regulating body

King’s College London is regulated by the Higher Education Funding Council for England.

Career prospects

Our graduates will follow a broad range of career paths. The skills you develop are likely to be particularly transferable to work in social media management, analytics & website management, CRM management, digital advertising, metrics management, market research, marketing and across cultural industries.



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The Department of Computer Science at The University of Liverpool is delighted to announce the opportunity for Home and European students to receive industrial sponsorship to cover tuition fees for this programme. Read more
The Department of Computer Science at The University of Liverpool is delighted to announce the opportunity for Home and European students to receive industrial sponsorship to cover tuition fees for this programme. For more information visit our Postgraduate Funding Tool or contact Dr Martin Gairing.

The MSc in Big Data and High Performance Computing provides students with an in-depth understanding of big data analysis and processing using high performance computing technology. Run in conjuction with the STFC Hartree Centre, this MSc programme enables students to gain a specialist qualification in an area of computing that is in great demand worldwide.

Big data is commonly described as data that is so large that it cannot be readily processed using standard techniques. Our current global ability to collect data is such that “big data” sets are becoming common-place.

The most obvious example of this is the exponential growth of the World Wide Web; however there are many public and private enterprises where the analysis of large-scale data sets is critical to growth. Although significant computer power exists, the necessary skills-base is lagging behind the technology.

There is an employment gap looming in the field of big data, especially in the context of the skills required with respect to the application of High Performance Computing (HPC) capabilities to address big data problems.

The MSc in Big Data and High Performance Computing is designed to address this anticipated skills gap and provide those completing the programme with the necessary abilities (abilities which will be highly desirable within the employment market) to address big data centric problems in the context of HPC.

The programme has been designed and operates in close collaboration with the Hartree High Performance Computing Centre and focuses on the practical application of Big Data and HPC technology.

The Hartree centre is underpinned by £37.5 million of Government investment and hosts the UK’s premier supercomputing environment. This partnership provides a unique and unrivalled MSc programme and ensures that students completing the programme have a ready route into employment, facilitated by commercial contacts provided as part of the individual project.

You may also be interested in our Big Data Management MSc, Geographic Data Science MSc and Risk and Uncertainty MSc. For more information visit http://www.liverpool.ac.uk/study/postgraduate

The programme is organised as two taught semesters followed by an individual project undertaken over either the summer or, if desired, during the following year of study. Within each semester students study a number of modules adding up to 60 credits per semester (120 in total). This will be followed by a project dissertation, also 60 credits, making an overall total of 180 credits.

Why Computer Science?

Excellent partnerships

The MSc in Big Data and High Performance Computing programme has been developed, and operates, in close collaboration with the STFC Hartree Centre at Daresbury. The Hartree centre is underpinned by £37.5 million off Government investment and hosts the UKs premier supercomputing environment. The Department of Computer Science at Liverpool provides for a wide range of Big data, HPC and related skills and experience. This partnership means that this programme is unique and unrivalled. The partnership also ensures that students completing the programme have a ready route into employment facilitated by commercial contacts provided as part of the individual project element of the programme, which will in most cases is conducted with respect to real commercial requirements.

State of the art teaching and research

MSc Students who pursue their postgraduate study within the Department of Computer Science at the University of Liverpool will be an integral part of a department that is internationally renowned for its advanced research and teaching. The Department came seventh nationally in the 2008 research assessment exercise.

The Department of Computer Science is organised into four main research groups:

Agents
Algorithmics
Logic and Computation
Economics and Computation
Together these groups provide a critical mass of expertise equal to the most complex challenges in Computer Science, within a setting that offers world-class research facilities and support.

Teaching

You will be taught by lecturers who are internationally known for their research. The MSc in Big Data and High Performance Computing is offered full-time on-campus.

The taught components of the programme offer a choice of contemporary computing topics, a strong theoretical basis and the opportunity to gain sound practical and critical analysis skills. The programme can be taken in the form of a single year (12 months) of study with the individual project being undertaken over the summer months, or alternatively the project can be undertaken in the following academic year.

The computing resources include an extensive integrated network of workstations running the Linux operating system and the X-Windows graphical interface, together with a large number of PCs running Microsoft Windows. Staff and students have easy access to high quality laser printing facilities and a range of specialist software.

Career prospects

The MSc in Big Data and High Performance Computing (HPC) is specifically designed to fill a "skills gap" in the employment market. More specifically it is designed to provide students with the necessary skills to allow them to apply Big Data and HPC concepts to real problems. The programme has been structured to facilitate the practical application of this "cutting-edge" technology to real-world problems. The intention is that at the end of the programme students will be able to apply the knowledge gained on the programme specifically to real-world big data and HPC problems. However, the programme is also designed to furnish students with a set of transferable skills that are of particular relevance across the IT industry.

The programme has been developed, and is delivered, in close collaboration with the Hartree Centre at Daresbury which operates the UK's largest supercomputer (capable of a thousand trillion calculations per second). Hartree have close links with industry, and provide assistance with respect to the group and final individual projects, the latter conducted in partnership with commercial and/or non-commercial organisations.

Read less
Embark on a career in a leading-edge field and master the exciting and challenging world of big data!. Big data techniques are revolutionising how organisations and industries acquire and analyse data, offering valuable insights into how people live, work, play, travel, shop and exercise. Read more

Embark on a career in a leading-edge field and master the exciting and challenging world of big data!

Big data techniques are revolutionising how organisations and industries acquire and analyse data, offering valuable insights into how people live, work, play, travel, shop and exercise. These insights are valuable to marketers, researchers, designers, city planners, app developers, educators and many more.

GCU's MSc in Big Data Technologies helps students build the fundamental knowledge and practical skills for success in this fast-growing field. You'll develop competence in a range of emerging technologies: big data, cloud computing and the internet of things. You'll learn from the experts; GCU is internationally recognised for the strength of its research in these exciting subjects, driving 21st century innovation.

With both full-time and part-time study available, the programme is ideal for someone with a background in computer science, software engineering, web technologies or computer engineering who wants to enhance or update their skills. Those with backgrounds in mathematics and electronics are also well suited.

The up-to-date curriculum keeps a career-focused approach, so you'll gain valuable skills you can immediately put to work in the industry.

  • Apply leading-edge tools and technologies from companies such as IBM, Microsoft and SAS
  • Explore industry-standard open-source development platforms such as Hadoop
  • Achieve industry recognition with SAS joint certification in the programme's Data Analytics module

Your expertise in big data will enable you to provide new insights into human behaviour and psychology, which can help us build stronger and happier societies across the globe. Your work could shape smart, sustainable cities; remove barriers to education; help people make healthier choices day-to-day; improve public health… and so much more. All meaningful ways of contributing to the common good.

What you will study

Full-time students complete six taught modules; three in trimester A and three in trimester B and an MSc dissertation project in trimester C. Part-time students complete six taught modules; three in Year 1, three in Year 2and an MSc project in Year 3.

Cloud Computing and Web Services

This module provides analytical and practical coverage of cloud computing and web services. It focuses on the technology, frameworks and associated standards: cloud models, cloud platforms and scalability. It also provides coverage of current web service technology and data transport representations, and integrated cloud and web service application development. Current examples from industry technology are used throughout.

Big Data Landscape

This module covers the process of managing Big Data throughout its lifecycle, from requirements through retirement. The lifecycle crosses different application systems, databases and storage media. Students will gain an understanding of the full Big Data value chain. They will be able to analyse the challenges and opportunities associated with the different stages that Big Data passes through.

Data Analytics

This module covers the basic concepts of statistics needed to understand the critical concepts of data mining, machine learning and predictive analytics used in the visualisation and analysis of data, particularly Big data. Students will gain an understanding of data preparation, the process models used in analytics, the algorithms and their requirements, the implementation of these algorithms using current technologies, and their applicability to different types of scenario. They will also gain advanced practical skills in the design, implementation and evaluation of analytical solutions to problems involving Big Data.

Big Data Platforms

This module covers the platforms that support data storage, processing and analytics in Big Data scenarios. It focuses on highly scalable platforms that provide operational capabilities for real-time, interactive processing and on platforms that provide analytical capabilities for retrospective, complex analysis. Students will gain an advanced understanding of the principles on which these platforms are based, and their strengths, weaknesses and applicability to different types of scenario. They will also gain advanced practical skills in the design and implementation of scalable Big Data platform solutions.

Internet of Things

This module provides fundamental and practical coverage of the set of converging technologies known as the Internet of Things (IoT). It focuses on representative IoT applications, technologies, frameworks and associated standards that support and underpin IoT applications, such as sensor networks, messaging protocols, security, data storage, analytics, services and human interaction. The module provides in-depth practical coverage of representative IoT implementation frameworks including cloud-based service delivery models.

IT Professional Issues and Project Methods

This module seeks to develop understanding and practical skills in advanced project methods which are inline with industry regulations, standards and practices and are applicable to complex IT projects. Study is undertaken in an integrated fashion to ensure that the professional frameworks within which such projects are developed, deployed and managed are fully understood.

Masters Dissertation

Students will investigate a topical or emerging theme in Cloud Computing or related technologies. The dissertation acts as a vehicle for extending the knowledge and understanding of the student and the technical community in some specialist technical area. It serves through its length, complexity and rigour as a suitable vehicle for extending students' range of personal, interpersonal and communication skills. In addition it serves to develop and extend a range of high-level thinking skills, including analysing and synthesising skills and affords the opportunity for the student to demonstrate initiative and creativity in a major piece of technical work.

Work placements

Students will be made aware of placement opportunities provided by our industry partners. Typically a placement would be undertaken after graduating from the MSc programme but opportunities can sometimes be made available for students to undertake their MSc Dissertation in partnership with a commercial company.

Assessment methods

The methods of assessment include class tests, coursework assignments, practical tests and technical reports. Practical implementation and evaluation form a significant part of the assessment for the taught modules and for the work of the MSc dissertation.

Graduate prospects

When you graduate, you'll be a competitive candidate for roles as a systems developer, architect or administrator in data and analytics. You'll find opportunities in a diverse range of industries: engineering, pharmaceuticals, finance, healthcare, retail, security, smart environments and more.



Read less
Embark on a career in a leading-edge field and master the exciting and challenging world of big data!. Big data techniques are revolutionising how organisations and industries acquire and analyse data, offering valuable insights into how people live, work, play, travel, shop and exercise. Read more

Embark on a career in a leading-edge field and master the exciting and challenging world of big data!

Big data techniques are revolutionising how organisations and industries acquire and analyse data, offering valuable insights into how people live, work, play, travel, shop and exercise. These insights are valuable to marketers, researchers, designers, city planners, app developers, educators and many more.

GCU's MSc in Big Data Technologies helps students build the fundamental knowledge and practical skills for success in this fast-growing field. You'll develop competence in a range of emerging technologies: big data, cloud computing and the internet of things. You'll learn from the experts; GCU is internationally recognised for the strength of its research in these exciting subjects, driving 21st century innovation.

With both full-time and part-time study available, the programme is ideal for someone with a background in computer science, software engineering, web technologies or computer engineering who wants to enhance or update their skills. Those with backgrounds in mathematics and electronics are also well suited.

The up-to-date curriculum keeps a career-focused approach, so you'll gain valuable skills you can immediately put to work in the industry.

  • Apply leading-edge tools and technologies from companies such as IBM, Microsoft and SAS
  • Explore industry-standard open-source development platforms such as Hadoop
  • Achieve industry recognition with SAS joint certification in the programme's Data Analytics module

Your expertise in big data will enable you to provide new insights into human behaviour and psychology, which can help us build stronger and happier societies across the globe. Your work could shape smart, sustainable cities; remove barriers to education; help people make healthier choices day-to-day; improve public health… and so much more. All meaningful ways of contributing to the common good.

What you will study

Full-time students complete six taught modules; three in trimester A and three in trimester B and an MSc dissertation project in trimester C. Part-time students complete six taught modules; three in Year 1, three in Year 2and an MSc project in Year 3.

Cloud Computing and Web Services

This module provides analytical and practical coverage of cloud computing and web services. It focuses on the technology, frameworks and associated standards: cloud models, cloud platforms and scalability. It also provides coverage of current web service technology and data transport representations, and integrated cloud and web service application development. Current examples from industry technology are used throughout.

Big Data Landscape

This module covers the process of managing Big Data throughout its lifecycle, from requirements through retirement. The lifecycle crosses different application systems, databases and storage media. Students will gain an understanding of the full Big Data value chain. They will be able to analyse the challenges and opportunities associated with the different stages that Big Data passes through.

Data Analytics

This module covers the basic concepts of statistics needed to understand the critical concepts of data mining, machine learning and predictive analytics used in the visualisation and analysis of data, particularly Big data. Students will gain an understanding of data preparation, the process models used in analytics, the algorithms and their requirements, the implementation of these algorithms using current technologies, and their applicability to different types of scenario. They will also gain advanced practical skills in the design, implementation and evaluation of analytical solutions to problems involving Big Data.

Big Data Platforms

This module covers the platforms that support data storage, processing and analytics in Big Data scenarios. It focuses on highly scalable platforms that provide operational capabilities for real-time, interactive processing and on platforms that provide analytical capabilities for retrospective, complex analysis. Students will gain an advanced understanding of the principles on which these platforms are based, and their strengths, weaknesses and applicability to different types of scenario. They will also gain advanced practical skills in the design and implementation of scalable Big Data platform solutions.

Internet of Things

This module provides fundamental and practical coverage of the set of converging technologies known as the Internet of Things (IoT). It focuses on representative IoT applications, technologies, frameworks and associated standards that support and underpin IoT applications, such as sensor networks, messaging protocols, security, data storage, analytics, services and human interaction. The module provides in-depth practical coverage of representative IoT implementation frameworks including cloud-based service delivery models.

IT Professional Issues and Project Methods

This module seeks to develop understanding and practical skills in advanced project methods which are inline with industry regulations, standards and practices and are applicable to complex IT projects. Study is undertaken in an integrated fashion to ensure that the professional frameworks within which such projects are developed, deployed and managed are fully understood.

Masters Dissertation

Students will investigate a topical or emerging theme in Cloud Computing or related technologies. The dissertation acts as a vehicle for extending the knowledge and understanding of the student and the technical community in some specialist technical area. It serves through its length, complexity and rigour as a suitable vehicle for extending students' range of personal, interpersonal and communication skills. In addition it serves to develop and extend a range of high-level thinking skills, including analysing and synthesising skills and affords the opportunity for the student to demonstrate initiative and creativity in a major piece of technical work.

Work placements

Students will be made aware of placement opportunities provided by our industry partners. Typically a placement would be undertaken after graduating from the MSc programme but opportunities can sometimes be made available for students to undertake their MSc Dissertation in partnership with a commercial company.

Assessment methods

The methods of assessment include class tests, coursework assignments, practical tests and technical reports. Practical implementation and evaluation form a significant part of the assessment for the taught modules and for the work of the MSc dissertation.

Graduate prospects

When you graduate, you'll be a competitive candidate for roles as a systems developer, architect or administrator in data and analytics. You'll find opportunities in a diverse range of industries: engineering, pharmaceuticals, finance, healthcare, retail, security, smart environments and more.



Read less
The modern world is experiencing a growth of online data in a variety of forms, including social networks, web documents, digital libraries, blogs, medical records, biological data, remote sensing, imaging, forecasting etc. Read more
The modern world is experiencing a growth of online data in a variety of forms, including social networks, web documents, digital libraries, blogs, medical records, biological data, remote sensing, imaging, forecasting etc. This data may not be fully structured but still contains valuable information that needs discovering, such as emerging opinions in social networks, consumer purchase behaviour, trends from search engines, and other patterns that emerge from such huge data sources.

These developments mean traditional applications are no longer appropriate to the processing and analysis of the amount of data available. Companies, such as Google, are leading the movement from a large-scale relational database reflecting the desire to analyse data automatically and on a larger scale than previously seen.

Course content

The course is designed to respond to critical skill shortages in the rapidly expanding field of Big Data. It offers a balance of practical skills combined with academic rigour in the field of Big Data. This is a unique offering which builds on the strengths and experience of Staffordshire University in delivering practical scholarship relevant to real world situations.

It is intended to assist students and career professionals enter and succeed in the growing, high demand analytics workforce. The course recognises and acknowledges the changing patterns in study including the growing demand for extended and distance learning modes of study and builds on the many years of experience the faculty has of delivering these modes.

As a full time student, you would study in the first semester:
-Managing Emerging Technologies
-Data Harvesting and Data Mining
-Distributed Storage
-Distributed Processing

This first semester is concerned with those areas of big data fundamentals and is used to examine how big data is stored, processed and how an organisation can start to use tools to examine this data and start to improve businesses awareness of its customer base.

In the second semester you will study:
-Research Methods
-Virtualisation
-Big Data Applications
-Data Modelling and Analysis

This semester encompasses a module on how to manage big data within a network, a maths module on algorithms that are required to enhance big data and a module which will prepare you for the master project in the last semester. The last module will examine existing big data applications that can help get the most out of big data.

The final semester is a major research project. The actual content is open to discussion with the award leader and project supervisor must be a discipline related to Big Data.

On completion of the award you will have developed detailed knowledge and understanding of Big Data and the ability to apply this knowledge in an academic or commercial context.

The award also aims to instil sound academic & professional skills required for lifelong learning & development - for example, skills in research methods, critical thinking & analysis, academic and professional report writing, and communication skills.

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This programme provides students with the knowledge of cutting-edge methodologies, approaches and skills in the emerging field of data science and big data applications, including advanced software development, systems for big data analytics, statistical data analysis data mining, distributed systems, data privacy and security, and data visualization and exploration. Read more
This programme provides students with the knowledge of cutting-edge methodologies, approaches and skills in the emerging field of data science and big data applications, including advanced software development, systems for big data analytics, statistical data analysis data mining, distributed systems, data privacy and security, and data visualization and exploration.

The programme of study culminates in a dissertation, enabling you to bring what you have learnt together in a significant piece of project work.

In summary, the MSc Big Data Science and Technology offers you the opportunity to build your own path of study - from the advanced computing modules, the extended list of optional modules available, as well as the dissertation - so as to match your specific career aspirations in the area of big data and data science.

For more information on the part time version of this course, please view this web-page: http://www.brad.ac.uk/study/courses/info/big-data-science-and-technology-msc-part-time

Why Bradford?

This programme intends to equip graduates with the cutting-edge knowledge and skills to work in the industry as a Data Scientist, Big Data Architect, or Big Data Analyst.

MSc Big Data Science and Technology provides industry with graduates that are ready and able to develop solutions to address challenges for big data analytics and developing big data systems.

Modules

-Software Development
-Big Data Systems and Analytics
-Information Theory and Data Communication
-Security, Privacy and Data Protection
-Mobile Applications
-Statistical Data Analysis
-Data Mining
-Concurrent and Distributed Systems
-Data visualization
-Dissertation

Career support and prospects

The University is committed to helping students develop and enhance employability and this is an integral part of many programmes. Specialist support is available throughout the course from Career and Employability Services including help to find part-time work while studying, placements, vacation work and graduate vacancies. Students are encouraged to access this support at an early stage and to use the extensive resources on the Careers website.

Discussing options with specialist advisers helps to clarify plans through exploring options and refining skills of job-hunting. In most of our programmes there is direct input by Career Development Advisers into the curriculum or through specially arranged workshops.

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Boost innovation with analytics and big data opportunities. Become an international professional able to discover insights and drive innovation in any organization. Read more
Boost innovation with analytics and big data opportunities. Become an international professional able to discover insights and drive innovation in any organization.

See the website for more information: http://barcelonatechnologyschool.com/?utm_source=Find%20a%20master&utm_medium=profile&utm_campaign=bts%20generic

OVERVIEW

Barcelona is the European capital of innovation and an international tech hub that holds leading Big Data Corporate projects.

Many global companies are relocating their Big data operations into the city, what means professional opportunities for those who be ready.

The Master in Big Data & Innovation Analytics is a program lead by BTS, Eurecat (the Technology Center of Catalonia) and Big Data industry leaders.

With the Master in Big Data & Innovation Analytics you will develop the most demanded Big Data skills while you discover in first person the innovative vision of data from the industry leaders.

WHAT WILL I LEARN?

The Master in Big Data & Innovation Analytics will help you to become an international professional able to discover insights and drive innovation in any organization.

You will learn to extract relevant insights from data and to generate strategic solutions for any kind of organization or industry using the most advanced analytics and data technologies.

A key part of the program is project-based learning, where participants will either work on their own Big Data projects & solutions.

This methodology reflects digital industry demand for new IT professionals with skills such as:

Core Skills
-Empathy and Communication
-International scope
-Flexibility
-Collaboration and Network
-Transversal vision and Curiosity
-Compromise and Responsability

Technical Skills
-Data-Driven Busines
-Statistics & programming
-Data Science
-Algorithms for data science
-Infrastructure & Tools
-Data Visualization & explotation
-Cloud Computing

Business And Innovation Skills
-Digital business administration
-Mobile business
-Creativity and Innovation
-Entrepreneurship

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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.

Objectives

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
• Internship (9 ECTS, 22 weeks minimum)
Thesis
• Master thesis (9 ECTS, 150h)

Teaching

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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Institute for Digital Technologies. The. Institute for Digital Technologies. aims to address major technological challenges, with a focus on Big Data, Interactive and Creative Media, Cyber Security, Smart Living, Green Digital Technologies, and Advanced 5G Systems. Read more

Institute for Digital Technologies

The Institute for Digital Technologies aims to address major technological challenges, with a focus on Big Data, Interactive and Creative Media, Cyber Security, Smart Living, Green Digital Technologies, and Advanced 5G Systems.

Renowned for its teaching and research excellence, the Institute for Digital Technologies has built strong collaborations with national and international academic, research, and industrial organisations including Thales, BBC, Telefonica, IRT, and Rohde & Schwarz.

Each programme offers teaching from pioneering researchers and creative innovators, to expose students to the latest theories and developments from across the discipline. Our programmes are shaped by the principles and discoveries of our current research, and students are encouraged to participate in development projects and industry-focused work experience opportunities where possible.

For further information, see our programme page for Cyber Security and Big Data MSc

This programme

This programme aims to provide students with the very latest Cyber Security and Big Data principles, practices, tools, and techniques through analysing and evaluating practical application problems in the Cyber Security and Big Data industry and responding to important challenges the world is facing.

Our students will have a comprehensive understanding of the challenges in Cyber Security and Big Data faced by industry and society and the necessary skills to address those challenges in the most effective way. Our programme is designed to build students’ knowledge and develop their expertise in network security, cryptography, data science, and big data analytics through action-based learning, analysis and evaluation of application problems.

An essential element built in the programme is to develop our students’ employability skills that are essential to the Cyber Security and Big Data industries or related businesses, e-commerce, and governmental organisations.

Your personal development

Enterprise Through the Curriculum is an intrinsic element of every master’s programme at Loughborough University London and has been carefully designed to give students the best possible chance of securing their dream role. From employability profiling to live group projects set by a business or organisation, and from site visits to organisation-based dissertation opportunities, Loughborough University London is the first of its kind to develop a suite of activities and support that is positioned as the underpinning of every student’s experience.

Future career prospects

Our graduates will be in a very strong position to take on digital technology posts in a wide range of sectors, including Internet and cloud based businesses, finance firms, governmental organisations, consultancy companies operating in information, communication and network security, as well as those sectors dealing with massive personal data, such as health and wellbeing, where users’ privacy and data security needs safeguarding.

Graduates will also have the opportunity to enhance their knowledge and career prospects further by undertaking an MRes or PhD programme.

Speak to a programme specialist

If you'd like to know more about this programme, you can request an email or telephone call from an academic responsible for the teaching of this programme.

Complete the contact request form

Scholarships for 2018 entry

Our ambition is to inspire high achieving students from all backgrounds, to benefit from our outstanding teaching and cutting edge research facilities.

Inspiring Success Scholarship

The Inspiring Success Scholarship offers 100% off the full cost of tuition fees for selected unemployed and underemployed graduates, who obtained GCSE or A-level (or equivalent) qualifications from Hackney, Tower Hamlets, Newham or Waltham Forest.

East London Community Scholarship

The East London Community Scholarship offers 50% off the full cost of tuition fees for students who obtained GCSE or A-level (or equivalent) qualifications from Barking and Dagenham, Greenwich, Hackney, Newham, Tower Hamlets or Waltham Forest.

Excellence scholarship

The Excellence scholarship automatically awards high-achieving students 20% off the full cost of our master’s tuition fees, regardless of their full-time programme or nationality. To be eligible for this scholarship, students must have an upper-second class degree or equivalent qualification recognised by Loughborough University.

Alumni Bursary

The Alumni Bursary automatically awards graduates of Loughborough University 10% off the full cost of our master's tuition fees, regardless of their full-time programme or nationality.

Further details about the full range of scholarships we offer are available on our website.



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The MSc covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and makes them well equipped to proceed to become the ideal choice for the best employers in internet-related industries and the areas requiring big data analytical skills. Read more
The MSc covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and makes them well equipped to proceed to become the ideal choice for the best employers in internet-related industries and the areas requiring big data analytical skills.

Degree information

Students will gain a detailed knowledge and understanding of web-related technologies and big data analytics, ranging from information search and retrieval, natural language processing, data mining and knowledge acquisition, large-scale distributed data analytics and cloud computing to e-commerce and their business economic models and the latest concepts of social networks.

MSc students undertake modules to the value of 180 credits.

The programme consists of five core modules (75 credits), three option modules (45 credits) and the research dissertation (60 credits).

Core modules
-Information Retrieval and Data Mining
-Statistical Natural Language Processing
-Complex Networks and Web
-Web Economics

Optional modules - students can choose three of the following:
-Cloud Computing
-Computer Graphics
-Entrepreneurship: Theory and Practice
-Interaction Design
-Applied Machine Learning
-Machine Vision
-Supervised Learning
-Understanding Usability and Use
-Distributed Systems and Security

Dissertation/report
All students undertake an independent research project which culminates in a substantial dissertation.

Teaching and learning
The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by laboratory work with help from demonstrators. Student performance is assessed by unseen written examinations, coursework and the dissertation.

Careers

Graduates from UCL are keenly sought by the world's leading organisations, and many progress in their careers to secure senior and influential positions. Graduates of our Web Science and Big Data Analytics programmes are expected to develop careers in scientific research, the internet-based industry and other professional areas that require big data analytics skills.

Employability
The skill set obtained from our MSc makes our students the ideal choice for the best employers in internet-related industries and sectors requiring big data analytics. The MSc has a unique industry connection as almost all our lecturers have industry experience. Through long-term collaborations with big players in the field such as Google, Microsoft and BT, research is driven by the fundamental technical challenges faced by the industry. Throughout the degree, our students have the chance to interact with our industry collaborators and previous students, and have placement opportunities to address the specific technical problems faced by the industry.

Why study this degree at UCL?

UCL Computer Science is recognised as a world leader in teaching and research, and was one of the top-rated departments in the country according to the UK government's recent Research Excellence Framework.

Our Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries, from entertainment to finance.

We take an experimental approach to our subject, enjoy the challenge and opportunity of entrepreneurial partnerships and place a high value on our extensive range of industrial collaborations.

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Cloud computing is revolutionising the way that large, and often complex, datasets are stored and analysed. Our course aims to produce experts in cloud computing and big data required by academia and industry. Read more

Cloud computing is revolutionising the way that large, and often complex, datasets are stored and analysed. Our course aims to produce experts in cloud computing and big data required by academia and industry.

The MRes can only be applied for as part of the four-year (MRes plus PhD) EPSRC Centre for Doctoral Training in Cloud Computing for Big Data. The programme is suitable for students from both computing and mathematical backgrounds. It is very skills-focussed and also offers a high degree of research training.

Our course focuses on both theory and practice so that you can understand and implement cloud computing applications. You will cover key subjects such as advanced object-oriented programming, data mining and big data analytics.

All academic staff involved in teaching cloud computing modules have international reputations for their contributions to the field and some have extensive experience as practitioners in industry.

Delivery

During the MRes you will undertake advanced Masters’ level training in cloud computing and data analytics. The training will begin with a module in either computing science for mathematicians (for those with a statistics background) or statistics for computing scientists (for those from a computer science background).

All students will then be taught topics including statistics for big data, programming for big data, cloud computing, machine learning, big data analytics and time series analysis. The taught component will finish with a substantial group project, where you will have the opportunity to work with students from different backgrounds on a practical industry-focused data analysis problem.

Following this in years 2-4, you will carry out PhD research, guided by PhD supervisors from within the EPSRC Centre for Doctoral Training in Cloud Computing for Big Data, and typically additional advisors from industry.

Facilities

You will have access to free cloud computing resources to manage your research, a purpose-built Decision Theatre and 3D visualisation facility and a 3D printing learning lab.

You will be based in The Core building, where you will have the opportunity to work alongside experts in key areas of computing science, as well as access to industrial partners. You will also receive funding to attend selected conferences in emerging areas of your research discipline. We also offer funding for equipment and software to support your research.



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The MRes covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and makes them well equipped to proceed to become the ideal choice for the best employers in internet-related industries and the areas requiring big data analytical skills. Read more
The MRes covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and makes them well equipped to proceed to become the ideal choice for the best employers in internet-related industries and the areas requiring big data analytical skills.

Degree information

Students will gain a detailed knowledge and understanding of the fundamental principles and technological components of the World Wide Web, learning not only the latest web search and information retrieval technologies and their underlying computational and statistical methods, but also studying essential large-scale data analytics to extract insights and patterns from vast amounts of unstructured data.

Students undertake modules to the value of 180 credits.

The programme consists of two core modules (30 credits), four option modules (60 credits), and the research dissertation (90 credits).

Core modules
-Investigating Research
-Researcher Professional Development

Optional modules
-Complex Networks and Web
-Web Economics
-Information Retrieval and Data Mining
-Distributed Systems and Security
-Multimedia Systems
-Or an elective module from other Computer Science programmes

Dissertation/report
All students undertake an independent research project which culminates in a substantial dissertation.

Teaching and learning
The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by laboratory work with help from demonstrators. For the research project, each student is liaised with their academic or industrial supervisor to choose a study area of mutual interest. Student performance is assessed by unseen written examinations, coursework and the research dissertation.

Careers

Graduates from UCL are keenly sought by the world's leading organisations, and many progress in their careers to secure senior and influential positions. Graduates of our Web Science and Big Data Analytics programmes are expected to develop careers in scientific research, the internet-based industry and other professional areas that require big data analytics skills.

Employability
The skill set obtained from our MRes makes our students the ideal choice for the best employers in internet-related industries and sectors requiring big data analytics. The MRes has a unique industry connection as almost all our lecturers have industry experience. Through long-term collaborations with big players in the field such as Google, Microsoft and BT, their research is driven by the fundamental technical challenges faced by the industry. Throughout the degree, our students have the chance to interact with our industry collaborators and previous students, and have placement opportunities to address specific technical problems faced by the industry.

Why study this degree at UCL?

UCL Computer Science is recognised as a world leader in teaching and research, and was one of the top-rated departments in the country according to the UK government's recent Research Excellence Framework.

Our Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries, from entertainment to finance.

We take an experimental approach to our subject, enjoy the challenge and opportunity of entrepreneurial partnerships and place a high value on our extensive range of industrial collaborations.

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Increasingly, big data are used to track and trace social trends and behaviours. In turn, governments, business and industries worldwide are rapidly recruiting graduates who can understand and analyse big data. Read more
Increasingly, big data are used to track and trace social trends and behaviours. In turn, governments, business and industries worldwide are rapidly recruiting graduates who can understand and analyse big data. This course addresses how big data challenge traditional research processes, and impact on security, privacy, ethics, and governance and policy. You will learn practical and theoretical data skills, both in quantitative methods and the wider theoretical implications about how big data are transforming disciplinary boundaries.

You will take three core modules and a dissertation. Three option modules (see below) allow further specialisation. Lab work, report writing, data skills training and guest lectures by industry experts will form an integral part of your learning experience. You will be invited to attend short certified ‘Masterclasses’ to further extend your methodological repertoire. An annual Spring Camp on a key theme (e.g. health; networks; food) is also provided, allowing you to gain expertise in a wide range of cutting-edge quantitative methods.

You don’t need a computer science, mathematics or statistics background to apply. The focus is on conducting and understanding applied quantitative social science, so a willingness to engage with real world social science issues is essential.

Course Overview

Core Modules
-Big Data Research: Hype or Revolution?
-Principles in Quantitative Research
-Advanced Quantitative Research
-Dissertation

Masters Optional Modules
-Visualisation
-Social Informatics
-Big Data Research
-Hype or Revolution?
-Complexity in the Social Sciences
-Media and Social Theory
-Digital Sociology
-Post Digital Books
-User Interface Cultures
-Design, Method and Critique
-Playful Media
-Ludification in the Digital Age

Assessment
A combination of essays, reports, design projects, technical report writing, practice assessments, group work and presentations and an individual research project.

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