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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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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.

Key benefits

- 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 visualization

- 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)

Visit the website: http://www.kcl.ac.uk/study/postgraduate/taught-courses/big-data-in-culture-and-society-ma.aspx

Course detail

- Description -

The MA Big Data in Culture & Society will cover domain knowledge and data technique and practices which augment services across sectors. In addition to the core content covered by the programme, across the areas of specialisation, our students will have the opportunity to do an internship and a group project module, providing them with key skills going into the job market.

The programme will provide:

- 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
- 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 -

- 120 credits from taught modules assessed by essays and project reports
- 60 credits from individual dissertation supervised by staff member
- Full time study – typically 6 hours of taught classes per week
- Part time study – typically 3 hours of taught classes per week
- Dissertation – 15,000 words working with dedicated member of academic staff
- Modules assessed through coursework essays, workshop projects, reports, oral presentations and through participation in seminars
- Part Time study 60 credits in year 1 and 120 credits in year 2

Career prospects

Career paths will be quite broad and are likely to be in social media management, analytics & website management, CRM management, digital advertising, metrics management, market research, marketing, and across cultural industries.

How to apply: http://www.kcl.ac.uk/study/postgraduate/apply/taught-courses.aspx

About Postgraduate Study at King’s College London:

To study for a postgraduate degree at King’s College London is to study at the city’s most central university and at one of the top 21 universities worldwide (2016/17 QS World University Rankings). Graduates will benefit from close connections with the UK’s professional, political, legal, commercial, scientific and cultural life, while the excellent reputation of our MA and MRes programmes ensures our postgraduate alumni are highly sought after by some of the world’s most prestigious employers. We provide graduates with skills that are highly valued in business, government, academia and the professions.

Scholarships & Funding:

All current PGT offer-holders and new PGT applicants are welcome to apply for the scholarships. For more information and to learn how to apply visit: http://www.kcl.ac.uk/study/pg/funding/sources

Free language tuition with the Modern Language Centre:

If you are studying for any postgraduate taught degree at King’s you can take a module from a choice of over 25 languages without any additional cost. Visit: http://www.kcl.ac.uk/mlc

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Study MSc Big Data Technologies and enter the expanding world of Big Data, Data Analytics and Cloud Technologies. Available for full-time and part-time study, this course is ideal for current practitioners who have good experience in software development and wish to enhance their skills. Read more
Study MSc Big Data Technologies and enter the expanding world of Big Data, Data Analytics and Cloud Technologies.

Available for full-time and part-time study, this course is ideal for current practitioners who have good experience in software development and wish to enhance their skills. As well as anyone who holds an undergraduate degree in technology-based disciplines such as Computer Science, Software Engineering, Web Technologies, Computer Engineering, Mathematics and Electronics.

This masters is unique as it provides you with a fundamental understanding of the architectures of Big Data systems as well as developing the enhanced skills in software application development and data analytics solutions that you need.

It is our aim to increase skills in the new technology areas that business and industry are rapidly adopting. These include big data architectures, cloud computing, web technologies, data analytics (especially SAS and IBM Watson Analytics), big data computing platforms and the ever-expanding sources of data related to the Internet of Things.

This course has several different available starts and study formats - please view the relevant web-page for more information:
SEPTEMBER 2017 (Part Time) - http://www.gcu.ac.uk/ebe/study/courses/details/index.php/P02870-1PTA-1718/Big_Data_Technologies_(Part-time)?utm_source=ZZZZ&utm_medium=web&utm_campaign=courselisting

JANUARY 2018 (Full Time) - http://www.gcu.ac.uk/ebe/study/courses/details/index.php/P02860-1FTAB-1718/Big_Data_Technologies?utm_source=ZZZZ&utm_medium=web&utm_campaign=courselisting

JANUARY 2018 (Part Time) - http://www.gcu.ac.uk/ebe/study/courses/details/index.php/P02870-1PTAB-1718/Big_Data_Technologies_(Part-time)?utm_source=ZZZZ&utm_medium=web&utm_campaign=courselisting

Programme Description

The MSc in Big Data Technologies equips students with the fundamental knowledge and practical skills required to enter the exciting and challenging world of Big Data.

The programme takes a technology-focused approach to help students gain valuable skills that can be applied immediately within business and industry. Students will also build expertise in the key enabling technologies of cloud computing and will gain skills in one of the most exciting current areas of Big Data computing, the Internet of Things.

Why Study this Programme

This programme will equip students with the fundamental knowledge and skills of the core technologies for harnessing the big data challenges, including capture, curation, storage, integration, sharing, search, analysis, mining of large distributed unstructured datasets.

Studies on this programme are supported and enhanced uniquely by the University’s internationally excellent research strengths, especially in cloud computing, cyber security, Internet of Things and cyber-physical systems. Of parallel importance in our programme is to cultivate the professionalism which is expected within the industry.

With all the future-proofing capabilities synthesised coherently together, graduates of the MSc in Big Data Technologies will be amongst the most highly skilled ICT graduates, responding confidently to the needs and challenges in diverse big data application domains.

What you'll learn

Students complete six taught modules.

Trimester A:
-Cloud Computing and Web Services
-Big Data Landscape
-Data Analytics.

Trimester B:
-Big Data Platforms
-Internet of Things
-IT Professional Issues and Project Methods.

Trimester C:
-MSc Dissertation

Work Placement

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

Assessment is used to demonstrate achievement of learning outcomes. 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.

Career Opportunities

Graduates of the MSc in Big Data Technologies will be amongst the most highly skilled ICT graduates, responding confidently to the needs and challenges in diverse Big Data application domains.

This programme provides key skills for those seeking employment or career enhancement as Big Data systems developers, architects and administrators, and Big Data technologist for businesses and organisations in diverse domains from engineering industries, environmental surveillance, smart cities, to service type industries.

Read less
Study MSc Big Data Technologies and enter the expanding world of Big Data, Data Analytics and Cloud Technologies. Available for full-time and part-time study, this course is ideal for current practitioners who have good experience in software development and wish to enhance their skills. Read more
Study MSc Big Data Technologies and enter the expanding world of Big Data, Data Analytics and Cloud Technologies.

Available for full-time and part-time study, this course is ideal for current practitioners who have good experience in software development and wish to enhance their skills. As well as, anyone who holds an undergraduate degree in technology-based disciplines such as Computer Science, Software Engineering, Web Technologies, Computer Engineering, Mathematics and Electronics.

Unlike many data science MSc courses, this masters provides you with a fundamental understanding of the architectures of Big Data systems as well as the enhanced skills in software application development and data analytics solutions that you need.

It is our aim to increase skills in the new technology areas that business and industry are rapidly adopting. These include big data architectures, cloud computing, web technologies, data analytics (especially SAS and IBM Watson Analytics), big data computing platforms and the ever-expanding sources of data related to the Internet of Things.

Programme Description

The MSc in Big Data Technologies equips students with the fundamental knowledge and practical skills required to enter the exciting and challenging world of Big Data.

The programme takes a technology-focused approach to help students gain valuable skills that can be applied immediately within business and industry. Students will also build expertise in the key enabling technologies of cloud computing and will gain skills in one of the most exciting current areas of Big Data computing, the Internet of Things.

Work Placement

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

Assessment is used to demonstrate achievement of learning outcomes. 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.

Career Opportunities

Graduates of the MSc in Big Data Technologies will be amongst the most highly skilled ICT graduates, responding confidently to the needs and challenges in diverse Big Data application domains.

This programme provides key skills for those seeking employment or career enhancement as Big Data systems developers, architects and administrators, and Big Data technologist for businesses and organisations in diverse domains from engineering industries, environmental surveillance, smart cities, to service type industries.

Why Study this Programme

This programme will equip students with the fundamental knowledge and skills of the core technologies for harnessing the big data challenges, including capture, curation, storage, integration, sharing, search, analysis, mining of large distributed unstructured datasets.

Studies on this programme are supported and enhanced uniquely by the University’s internationally excellent research strengths, especially in cloud computing, cyber security, Internet of Things and cyber-physical systems.

Of parallel importance in our programme is to cultivate the professionalism which is expected within the industry.

With all the future-proofing capabilities synthesised coherently together, graduates of the MSc in Big Data Technologies will be amongst the most highly skilled ICT graduates, responding confidently to the needs and challenges in diverse big data application domains.

What you'll learn

Students complete six taught modules.

Trimester A:
-Cloud Computing and Web Services
-Big Data Landscape
-Data Analytics

Trimester B:
-Big Data Platforms
-Internet of Things
-IT Professional Issues and Project Methods

Trimester C:
-MSc Dissertation

Read less
Big data is poised to change the way enterprises function and a society operates, and is changing the way science and engineering is conducted. Read more
Big data is poised to change the way enterprises function and a society operates, and is changing the way science and engineering is conducted. The MSc program in Big Data Technology jointly offered by the Departments of Computer Science and Engineering and Mathematics integrates different disciplines together to allow students to know all the important aspects of the big data and how it is used in the real world.

Programe Objectives

The program is aimed at educating students about big data and issues related to big data. The students are expected to be familiar with the workflow of big data systems and social and societal implications of big data systems.

The program helps to integrate different disciplines together and students in this program will learn the major components of big data:
-Big data infrastructure
-Big data integration
-Big data storage
-Big data modeling and management
-Big data computing systems
-Big data analytic and mining systems
-Big data security, policy and social implications, as well as human factors
-Big data applications in various fields (data science)

Curriculum

Students must complete 30 credits of coursework, with 12 credits of core courses and 18 credits of elective courses. Students shall take ten 3-credit taught courses or eight to nine 3-credit taught courses plus independent project(s) offered from the program. Each course listed below carries 3 credits. Subject to the approval from the program director, students may take a maximum of 6 credits of CSIT courses offered by the MSc in Information Technology program as partial fulfillment to meet the graduation requirement of the program.

Core Courses
MSBD 5001 Foundations of Data Analytics
MSBD 5002 Data Mining and Knowledge Discovery (Co-Listing with CSIT 5210)
MSBD 5003 Big Data Computing
MSBD 5004 Mathematical Methods for Data Analysis

Elective Courses
MSBD 5005 Data Visualization
MSBD 5006 Quantitative Analysis of Financial Time Series (Co-Listing with MAFS 5130)
MSBD 5007 Optimization and Matrix Computation
MSBD 5008 Introduction to Social Computing
MSBD 5009 Parallel Programming
MSBD 5010 Image Processing and Analysis
MSBD 5011 Advanced Statistics: Theory and Applications
MSBD 5012 Machine Learning
MSBD 5013 Statistical Prediction
MSBD 5014 Independent Project

*Courses are offered subject to needs and availability.

Facilities

Students can enjoy library support, computer support, sports facilities, and email account at no extra cost. Upon graduation, students could also apply for related alumni services.

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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
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.

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 Extended learning route offers extra modules on employability, and study skills including writing in English accommodating the needs of Interntional students who initially require additional support in these areas. You will study common core modules in your first semester with other students on Computing extended awards before specialising.

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.

As a full time student, you would study in the first specialist 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.

Read less
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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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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This Joint Degree between HEC Paris and Ecole Polytechnique will equip students with both the technical skills and the strategic mindset to lead successfully any business career requiring a strong expertise in Big Data. Read more
This Joint Degree between HEC Paris and Ecole Polytechnique will equip students with both the technical skills and the strategic mindset to lead successfully any business career requiring a strong expertise in Big Data.

STUDY IN TWO GLOBALLY-RECOGNIZED INSTITUTIONS

Ecole Polytechnique (https://www.polytechnique.edu/en) and HEC Paris are both world leading academic institutions, renowned for the quality of their degrees, faculties and research (see HEC rankings http://www.hec.edu/Masters-programs/About/Rankings).

Their association within this Joint Degree represents the best Business/Engineering combination Europe could possibly offer, with extraordinary added value for the students who will follow this program in Big Data and Business.

LEAD THE DIGITAL TRANSFORMATION OF THE ECONOMY

Big data marks the beginning of a major transformation of the digital economy, which will significantly impact all industries. There are three main challenges to face:

> Technological: dealing with the explosion of data by managing the spread of vast amounts of information that is often very disorganized (IP addresses, fingerprinting, website logs, static web or warehouse data, social media, etc.)
> Scientific: replacing mass data with knowledge,i.e. developing the expertise that makes it possible to structure information, even out of tons of vague or corrupt data.
> Economic: managing data both to control risks and benefit from the new opportunities they offer. On the one hand, it is absolutely vital to be able to control the flow of information, anticipate data leaks, keep the information secure and ensure privacy. On the other hand, it is also essential to come up with solutions capable of transforming this flow of data into economic results and, at the same time, discover new sources of value from the data.

ACQUIRE THE SKILLS TO MAKE A DIFFERENCE IN TOMORROW’S DIGITAL WORLD

Exploiting this vast amount of data requires the following:

> A mastery of the sophisticated mathematical techniques needed to extract the relevant information.
> An advanced understanding of the fields where this knowledge can be applied in order to be in a position to interpret the analysis results and make strategic decisions.
> A strong business mindset and an even stronger strategic expertise, to be able to fully benefit from the new opportunities involved with Big Data problematics and develop business solutions accordingly.
> The ability to suggest and then decide on the choice of IT structures, the ability to follow major changes in IT systems, etc.

Therefore the program has three objectives:

> To train students in data sciences which combines mathematic modelling, statistics, IT and visualization to convert masses of information into knowledge.
> To give students the tools to understand the newest data distributing structures and large scale calculations to ease decision-making and guide them in their choices.
> To form data ‘managers’ capable of exploiting the results from analysis to make strategic decisions at the heart of our changeable businesses.

MAKE THE MOST OF WORLDWIDE NETWORKING AND ALUMNI POWER

Students will benefit not only from the close ties that HEC Paris has developed with the business world but also those of Ecole Polytechnique, through various networking events, conferences and career fairs.

The HEC Alumni network alone, consists of more than 52,300 members in 127 countries.

Program Details

http://www.hec.edu/Masters-programs/Master-s-Programs/Dual-Degree-Programs-with-Partner-Institutions/MSc-Big-Data-for-Business-Joint-Degree-with-Ecole-Polytechnique/Program-Details

Campuses

http://www.hec.edu/Masters-programs/Master-s-Programs/Dual-Degree-Programs-with-Partner-Institutions/MSc-Big-Data-for-Business-Joint-Degree-with-Ecole-Polytechnique/Campuses

CAREERS

As “Big Data” affects all kinds of companies and all sectors, students will have a very large range of career options upon graduation, from consulting firms to digital start-ups, not to mention very large multi-national companies.
In fact, as can be seen in all areas of cutting-edge innovation, there is a growing demand for high level managers who can combine strong technical skill with business know-how.

This is especially true when it comes to Big Data topics, and students graduating from data science and Big Data programs are therefore highly sought after on the job market.

http://www.hec.edu/Masters-programs/Master-s-Programs/Dual-Degree-Programs-with-Partner-Institutions/MSc-Big-Data-for-Business-Joint-Degree-with-Ecole-Polytechnique/Careers

FAQs

http://www.hec.edu/Masters-programs/Master-s-Programs/Dual-Degree-Programs-with-Partner-Institutions/MSc-Big-Data-for-Business-Joint-Degree-with-Ecole-Polytechnique/FAQ

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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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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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Our MSc Big Data Analytics programme is designed to provide students with in-depth knowledge of the new field of big data analytics from a computing perspective. Read more
Our MSc Big Data Analytics programme is designed to provide students with in-depth knowledge of the new field of big data analytics from a computing perspective. Data is being generated on an exponential scale by individuals and organisations. Valuable insights can be drawn from this data to inform strategic decisions, resulting in increased market share, profitability, possible cost savings and procedural efficiency. You will develop a critical understanding of the contemporary tools, techniques and models used for big data analytics.

The programme will enable you to develop practical skills, using tools and techniques from the forefront of business computing, and use these effectively to conduct big data analytics. It also seeks to promote an awareness of the moral, ethical and professional framework, within which you will operate as an IT professional in a business environment.

A key aspect of the programme philosophy is that the learning experience tightly integrates the use of Oracle commercial software (a world leader in this field), with investigation of the wider theoretical context. You will also learn about the skills needed to become a successful entrepreneur in the IT sector.

Why choose us?

-Our course is accredited by the British Computer Society, ensuring our course is fresh and relevant.
-The University is one of Oracle’s university-based academies, as well as being a member of UK Oracle User Group.
-This is the only big data analytics programme developed in partnership with Oracle, which is a major global leading IT vendor in the field.
-Previous graduates have progressed into roles with established companies such as Hewlett Packard, BT, Capgemini, Cisco, IBM and more.

Course breakdown

The MSc programme is normally studied over one year full-time or two years part-time (one year and one term full-time for January start). You may move between full and part-time modes of attendance. The course is divided into taught modules of 20 credits and a Masters project of 60 credits. Students complete 60 credits for Postgraduate Certificate, 120 credits for Postgraduate Diploma and 180 credits for the full MSc. Each credit represents 10 notional hours of student learning and assessment. The structure of the course, the module, levels and credit ratings and the awards that can be gained are shown below.

A range of assessment methods are employed, assessment criteria being published in each assignment brief. Knowledge and skills are assessed, formatively and summatively, by a number of methods: coursework, examinations (seen and unseen, open and closed-book), presentations, practical assignments, vivas, online forums, podcasts and project work.

Modules
-Research Methods and Project Management 20 credits
-Applied Statistics 20 credits
-Databases for Enterprise 20 credits
-Big Data Management 20 credits
-Data Mining 20 credits
-Web/Social Media Analytics and Visualisation 20 credits
-Master’s Project 60 credits

Enhancing your employability

This course is suitable for undergraduates and those who have worked in the industry but do not have the recognised qualifications. Students will be provided with the opportunity to complete industry-recognised Oracle Professional Certification.

The school boasts graduates who have gone on to work for Hewlett Packard, Bell Micro, Birmingham City Council, BT, Cap Gemini, Cisco, Deloitte, Ericsson, Fujitsu, IBM, Intel Corporation, NHS, Motorola, National Express, NEC, Royal Mail, Shell IT, JP Morgan Chase and Co, Carillion plc, Siemens and Nokia and many more.

You will also be provided with the opportunity to complete industry recognised Oracle Professional Certification.

Read less
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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