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Masters Degrees (Msc Big Data Analytics)

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


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

Key information

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

Course objectives

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

English language requirements

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

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

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

Structure and content

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


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


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

Career opportunities

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

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The 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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The MSc in Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science. Read more

The MSc in Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science.

The rate at which we are able to create data is rapidly accelerating. According to IBM, globally, we currently produce over 2.5 quintillion bytes of data a day. This ranges from biomedical data to social media activity and climate monitoring to retail transactions. These enormous quantities of data hold the keys to success across many domains from business and marketing to treating cancer or mitigating climate change.

The pace at which we produce data is rapidly outstripping our ability to analyse and use it. Science and industry are crying out for a new generation of data scientists who combine the statistical skills of data analysis and the computational skills needed to carry out this analysis on a vast scale.

The MSc in Data Science provides you with these skills. 

Studying this Masters, you will learn the mathematical foundations of statistics, data mining and machine learning, and apply these to practical, real world data.

As well as these statistical skills, you will learn the computational techniques needed to efficiently analyse very large data sets. You will apply these skills to a range of real world data, under the guidance of experts in that domain. You will analyse trends in social media, make financial predictions and extract musical information from audio files. 

The degree will culminate in a final project in which you will you can apply your skills and follow your specialist interests. You will do a novel analysis of a real world data of your choice. 

The programme includes:

  • A firm grounding in the theory of data mining, statistics and machine learning
  • Hands-on practical real world applications such as social media, biomedical data and financial data with Hadoop (used by Yahoo!, Facebook, Google, Twitter, LinkedIn, IBM, Amazon, and many others), R and other specialised software
  • The opportunity to work with real-world software such as Apache

Modules & structure

You will study the following core modules:

You will also choose from an anually approved list of modules which may include:

Skills & careers

Data Science is one of the fastest growing sectors of employment internationally. Big Data is an important part of modern finance, retail, marketing, science, social science, medicine and government. 

The study of a combination of long established fields such as statistics, data mining, machine learning and databases with very modern and strongly related fields as big data management and analytics, sentiment analysis and social web mining, offers graduates an excellent opportunity for getting valuable skills in advanced data processing. 

This could lead to a variety of potential jobs including: 

  • Data Scientist
  • Data Mining Analyst
  • Big Data Analyst
  • Hadoop Developer
  • NoSQL Database Developer
  • R Programmer
  • Python Programmer
  • Researcher in Data Science and Data Mining

Find out more about employability at Goldsmiths.

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This Masters degree provides you with a strong conceptual and theoretical understanding of big data analytics. You will gain the essential skills and confidence required to apply and produce knowledge and understanding of issues surrounding big data analytics in a range of contexts. Read more
This Masters degree provides you with a strong conceptual and theoretical understanding of big data analytics. You will gain the essential skills and confidence required to apply and produce knowledge and understanding of issues surrounding big data analytics in a range of contexts. This will enable you to evaluate, adapt, create and utilise appropriate models, methods, practices, theories and computational techniques in the face of changing and evolving technology. There is the opportunity to develop a critical understanding of visualisation concepts, modelling and algorithmic foundations, as well as to develop and evaluate new or advanced bespoke solutions for processing, analysing and making sense of big and/or complex data. The programme enables you concentrate on a specific practical area within computer science and is suitable whether you are a recent graduate or already working in the IT industry and looking to change career paths.

What will I study?

Gaining an in-depth and systematic knowledge of big data management theories, concepts, methodologies and professional practice, you will develop a systematic and critical understanding of algorithms and programming techniques for processing, storing, analysing, visualising and interpreting data.

You will learn the practical skills of mathematics that underpin the processing of data, the programming applications required to manage big data, and the visualisation techniques necessary to make sense of large data sets. There will also be the opportunity to work with emerging technologies derived from industry.

How will I study?

The course is delivered through a combination of lectures, seminars and tutorials with a mixture of daytime and evening classes. Sessions will frequently be highly interactive with a focus on the practical application of concepts and the use of case studies drawn from real life. An emphasis on small group sizes ensures that you will have plenty of opportunities for individual discussions with your tutors. Typically, you will study for approximately nine hours a week if you are studying on a full-time basis.

How will I be assessed?

Your vocational capability, academic critical thinking and intellectual development will be assessed throughout the course. This is achieved through a combination of coursework, case studies, problem-solving exercises and examinations. You may be assessed individually or as part of a group.

Who will be teaching me?

You will be taught by highly qualified, experienced and enthusiastic academic staff who are research-active and fully engaged with the wider business and academic community. The programme team specialise in a variety of subjects so you will benefit from a wide range of expertise. There will also be occasional input from external IT professionals who will be invited to teach particular sessions.

What are my career prospects?

As organisations become ever more dependent on data, there are increasing opportunities in specialist positions related to obtaining, processing and visualising data.

The MSc Big Data Analytics provides you with the skills and knowledge to develop your interests for a career in data science. You will be ideally placed to progress into roles where you will work as a data scientist or data analyst.

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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.

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This programme is ideal if you wish to use your analytic skills to derive and obtain useful insights from large amounts of data. Read more
This programme is ideal if you wish to use your analytic skills to derive and obtain useful insights from large amounts of data. By equipping you with the rigorous modelling and consulting skills needed to understand, manage and communicate useful insights from ‘big data’, it prepares you to inform business decisions or government policies.

Taught modules are delivered by our group of internationally recognised management scientists who are actively working with business, government and non-profit organisations to tackle routine, strategic or policy problems.

Our industry advisory board ensures that the focus of our taught modules is of both academic and practical relevance. IBM, our partner, has jointly developed with us two modules (Customer Analytics and Leading Analytics Initiatives), and sponsors a student prize.

During the summer, you will undertake a supervised consulting or research project. This will give you the opportunity to apply powerful tools such as data mining, forecasting, optimisation, simulation and decision analysis to a particular area of business or policy, equipping you with skills highly prized by employers.

Core study areas include consulting for analytics, discovery analytics, decision analytics, managing big data, customer analytics, leading analytics initiatives, operations analytics, policy and strategy analytics, and a consulting or research project.

See the website http://www.lboro.ac.uk/study/postgraduate/programmes/departments/business-economics/business-analytics-consulting/

Programme modules

Semester 1:
- Consulting for Analytics
You will learn the craft and skills required by analytic consultants, and which employers look for but often find lacking. It will cover process aspects of analytics projects, as well as skills in client interaction, problem structuring and data elicitation (with individuals and/or groups, and with hard/soft data), presenting data-driven analyses, report writing, and developing simple bespoke decision support systems.

- Discovery Analytics
You will be introduced to common statistical methods to explore and visualise cross sectional and temporal data. You will also learn about the design and conduct of data collection efforts, together with methods for dealing with data outliers and missing data. Industry-leading tools that are in high demand from employers (e.g. SAS and SPSS) will be used.

- Decision Analytics
Your will be introduced to common operational research techniques to help determine the best course of action for a given decision or problem. Topics covered include optimisation, simulation and decision and risk analysis.

- Managing Big Data
Your will learn about the challenges and opportunities derived from the increased volume, variety, velocity and value of data that is available today. A range of big data topics will be covered including data type, data integration, data technologies, and data security.

Semester 2:
- Customer Analytics
You will focus on analytics techniques that can help organisations gain a deeper insight into customers’ behaviour and attitudes towards their products and services. It will cover approaches designed to provide a profile of customer segments, such as those grounded in data mining and multivariate statistical analysis. Industry-leading tools that are in high demand from employers (e.g. SAS and SPSS) will be used. There is an IBM sponsored student prize on this module.

- Leading Analytics Initiatives
You will learn about the issues associated with implementing an analytics capability in organisations. It will cover topics on how to develop an analytics strategy, how to embed analytics in organisational processes to ensure they deliver value, and how to deploy analytics throughout the organisation to improve decision making. There is an IBM sponsored student prize on this module.

- Operations Analytics
You will focus on analytics techniques that can help organisations to develop a better understanding of operational processes, and identify efficiency and cost reduction opportunities. Topics covered include advanced optimisation and simulation techniques.

- Policy and Strategy Analytics
You will focus on analytics techniques designed to tackle complex policy and strategic issues. It will cover approaches designed to explain the behaviour of complex social systems or assess the consequences of complex decisions, in order to provide the levers for policy and strategy making in a variety of sectors.

- Consulting or Research Project


Taught modules are assessed by a mixture of coursework and examinations.
The summer project is assessed via a written dissertation.

Careers and further study

Business analytics is a new and rapidly developing field, and individuals with analytics skills are in short supply.
Graduates from this programme can expect to work as management consultants, business analysts, policy analysts, marketing researchers, operations researchers, and data scientists.
We have developed two modules - Customer Analytics and Leading Analytics Initiatives - in close collaboration with our partner IBM, who also sponsor a student prize.

Why choose business and economics at Loughborough?

Loughborough’s School of Business and Economics is a thriving forward-looking centre of education that aims to provide an exceptional learning experience.

Consistently ranked as a Top-10 UK business school by national league tables, our graduates are highly employable and enjoy starting salaries well above the national average.

The rich variety of postgraduate programmes we offer ranges from taught masters, MBA and doctoral programmes, to short courses and executive education, with subjects spanning Management, Marketing, Finance and Economics, Work Psychology, Business Analytics, International Crisis Management and Information Management. New for 2016, we are also launching two exciting new programmes in Human Resource Management. All of this contributes to a lively and supportive learning environment within the School.

- Internationally Accredited
The School of Business and Economics is one of less than 1% of business schools in the world to have achieved accreditation from all three major international accrediting bodies: The Association to Advance Collegiate Schools of Business (AACSB International), EQUIS accreditation from the European Foundation for Management Development (EFMD) and the Association of MBAs (AMBA).

- Career Prospects
Our graduates are in great demand. Over 94% of our postgraduate students were in work and/or further study six months after graduating.* As such, you will be equipped with skills and knowledge that will serve you well in your career or enable you to pursue further study and research.

*Source: DLHE

Find out how to apply here http://www.lboro.ac.uk/study/postgraduate/programmes/departments/business-economics/business-analytics-consulting/

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Gain the skills and knowledge to truly capitalise on the potential of big data and analytics. Boost your ability to integrate and deploy data-driven solutions that help build competitive advantage. Read more

Gain the skills and knowledge to truly capitalise on the potential of big data and analytics. Boost your ability to integrate and deploy data-driven solutions that help build competitive advantage. Develop your confidence in the practical application of the latest big data analytics tools, and use our innovative learning environment to study online from anywhere in the world.

“The best part of online study with the University of Liverpool was the teamwork with people from around the world.”

George Bagropoulos (Greece) IT graduate

Unlock the power of big data to drive business strategy

This 100% online master’s programme gives you the opportunity to:

  • Acquire a practical understanding of big data analytics and how it can empower organisations to become more effective, efficient and competitive.
  • Advance your potential career potential by acquiring a comprehensive and demonstrable understanding of big data and analytics tools and techniques.
  • Get hands-on experience of big data management frameworks and the ecosystems that can be used to support advanced data analytics.
  • Equip yourself with the tools and methods used in data mining, including data pre-processing, to generate a systematic understanding of the end-to-end process.
  • Create data warehouses using data from multiple sources and use state-of-the-art data visualisation technology to ‘tell a story’.
  • Analyse and understand the practical challenges of integrating and deploying big data management systems.
  • Demonstrate your skills in big data analytics and data-driven decision making to current or future employers via an e-portfolio of IT artefacts.

Grow with one of the world’s leading universities

The University of Liverpool is ranked in the top 1% of universities worldwide1 and is a member of the prestigious Russell Group of research-led British universities.

The 2014 Research Excellence Framework rated 97% of the research produced by the University’s Department of Computer Science as world-leading or internationally excellent – among the highest ratings of computer science department in the UK.

The University has developed an innovative, cloud-based server platform to allow online IT students to develop practical skills in an environment that mirrors real-world IT workspaces.


Study a master’s programme that puts you at the forefront of new, in-demand technologies. Position yourself to move into senior data or analytics roles2 such as:

  • Director of Analytics
  • Director of Business Analytics
  • Manager of Business Analytics
  • Director of Business Intelligence
  • Analytics Manager
  • Senior Big Data Engineer

1 As listed in the International Handbook of Universities, published by the International Association of Universities (2014).

2 Career options may require additional experience, training or other factors beyond the successful completion of this degree programme.

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Our MSc in Big Data Analytics provides a foundation for you to pursue a career applying leading edge software analytics technology or conducting research in this vitally important field. Read more
Our MSc in Big Data Analytics provides a foundation for you to pursue a career applying leading edge software analytics technology or conducting research in this vitally important field. It will give you in-depth knowledge and critical understanding of the key issues and concepts. You’ll develop powerful skills in the extraction, analysis and management of information from big data using a variety of scientific techniques and software tools.

One of the course’s key strengths is that it is designed in conjunction with SAS, the global leaders in data analytics, whose data mining and business intelligence platform is widely used in academia and industry. You’ll have the opportunity to gain SAS 9 base certification. We also boast strong links with employers through our research and high profile consultancy projects, ensuring that our teaching remains up-to-date and relevant.

You’ll be introduced to knowledge discovery, analysis and assessment of data extracted from structured and unstructured big datasets, visualisation and communication of results. You’ll process advanced knowledge and information, make deductions and form
conclusions. The practical skills you’ll develop include computer modelling and the design and analysis of big data sets. The broader
skills include communication, teamwork, management and the ability to use advanced quantitative methods.

As part of your studies, you’ll address real-world industry-based problems during supervised computer sessions and through independent work. This intellectually demanding process requires not only specialist knowledge of big data analytics, but also the ability to apply multidisciplinary concepts to today’s dynamic business and scientific areas.

With the MSc, you’ll be equipped for careers in business intelligence and data analytics in any type of industry, in consultancy or in entrepreneurship. The course also provides a foundation for progression to a PhD or MPhil, allowing you to pursue your research interests.

You’ll study modules such as:

Business Analytics with SAS
Statistical Techniques
Studying at Masters Level and Research Methods
Processing Big Data
Information Visualisation
Analytics: Ethics, Trusts and Governance
Comparative Analytics Tools
Natural Language Processing
Independent Scholarship

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

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

MSc in Data Science aims to equip students with a solid grounding in data science concepts and technologies for extracting information and constructing knowledge from data. Students of the MSc Data Science will study the computational principles, methods, and systems for a variety of real world applications that require mathematical foundations, programming skills, critical thinking, and ingenuity. Development of research skills will be an essential element of the Data Science programme so that students can bring a critical perspective to current data science discipline and apply this to future developments in a rapidly changing technological environment.

Key Features of the MSc Data Science

The MSc Data Science programme focuses on three core technical themes: data mining, machine learning, and visualisation. Data mining is fundamental to data science and the students will learn how to mine both structured data and unstructured data. Students will gain practical data mining experience and will gain a systematic understanding of the fundamental concepts of analysing complex and heterogeneous data. They will be able to manipulate large heterogeneous datasets, from storage to processing, be able to extract information from large datasets, gain experience of data mining algorithms and techniques, and be able to apply them in real world applications. Machine learning has proven to be an effective and exciting technology for data and it is of high value when it comes to employment. Students of the Data Science programme will learn the fundamentals of both conventional and state-of-the-art machine learning techniques, be able to apply the methods and techniques to synthesise solutions using machine learning, and will have the necessary practical skills to apply their understanding to big data problems. We will train students to explore a variety visualisation concepts and techniques for data analysis. Students will be able to apply important concepts in data visualisation, information visualisation, and visual analytics to support data process and knowledge discovery. The students of the Data Science programme also learn important mathematical concepts and methods required by a data scientist. A specifically designed module that is accessible to students with different background will cover the basics of algebra, optimisation techniques, statistics, and so on. More advanced mathematical concepts are integrated in individual modules where necessary.

The MSc Data Science programme delivers the practical components using a number of programming languages and software packages, such as Hadoop, Python, Matlab, C++, OpenGL, OpenCV, and Spark. Students will also be exposed to a range of closely related subject areas, including pattern recognition, high performance computing, GPU processing, computer vision, human computer interaction, and software validation and verification. The delivery of both core and optional modules leverage on the research strength and capacity in the department. The modules are delivered by lecturers who are actively engaged in world leading researches in this field. Students of the Data Science programme will benefit from state-of-the-art materials and contents, and will work on individual degree projects that can be research-led or application driven.


Modules for the MSc Data Science programme include:

- Visual Analytics

- Data Science Research Methods and Seminars

- Big Data and Data Mining

- Big Data and Machine Learning

- Mathematical Skills for Data Scientists

- Data Visualization

- Human Computer Interaction

- High Performance Computing in C/C++

- Graphics Processor Programming

- Computer Vision and Pattern Recognition

- Modelling and Verification Techniques

- Operating Systems and Architectures


The Department of Computer Science is well equipped for teaching, and is continually upgrading its laboratories to ensure equipment is up-to-date – equipment is never more than three years old, and rarely more than two. Currently, our Computer Science students use three fully networked laboratories: one, running Windows; another running Linux; and a project laboratory, containing specialised equipment. These laboratories support a wide range of software, including the programming languages Java, C# and the .net framework, C, C++, Haskell and Prolog among many; integrated programme development environments such as Visual Studio and Netbeans; the widely-used Microsoft Office package; web access tools; and many special purpose software tools including graphical rendering and image manipulation tools; expert system production tools; concurrent system modelling tools; World Wide Web authoring tools; and databases.

As part of the expansion of the Department of Computer Science, we are building the Computational Foundry on our Bay Campus for computer science and mathematical science.

Career Destinations

- Data Analyst

- Data mining Developer

- Machine Learning Developer

- Visual Analytics Developer

- Visualisation Developer

- Visual Computing Software Developer

- Database Developer

- Data Science Researcher

- Computer Vision Developer

- Medical Computing Developer

- Informatics Developer

- Software Engineer

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There has been a recent upsurge in commercial interest in the new role of "data scientist". A data scientist is a person who excels at manipulating and analysing data, particularly large data sets that don't fit easily into tabular structures (so-called "Big Data"). Read more
There has been a recent upsurge in commercial interest in the new role of "data scientist". A data scientist is a person who excels at manipulating and analysing data, particularly large data sets that don't fit easily into tabular structures (so-called "Big Data").

Why study Data Science at Dundee?

The School of Computing has been working on 'big data' and data analysis for at least five years; not only working with data but also developing new algorithms and techniques for data scientists. The School already runs the most successful Business Intelligence Masters course in the UK.

This course will be led by Professor Mark Whitehorn and Andy Cobley. Mark is an emeritus professor at the University of Dundee and also runs a successful consultancy company that specialises in BI, Data Sciences and analytics. Andy is the course organiser for both the existing BI course and the new Data Science course.

This course will enhance your employability by providing you with knowledge, skills and understanding of data science research and implementation. You will also acquire skills in the professional procedures necessary to ensure that data science research and implementation is both valid and actionable and engage with contemporary debate about the role, ethics and utility of data science in commercial and other settings.

What is the difference between Data Science and Business Intelligence?

There is clearly a huge overlap with Business Intelligence. A BI specialist will need to understand data and data analytics. However there is a bias towards understanding how data is stored in the current operational systems within an enterprise the design and the implementation of an analytical system such as a data warehouse. A data scientist will be less concerned with the construction of a data warehouse and more interested in the message the specific sets of data can deliver.

However, without some understanding of data warehouses the data scientist will find it difficult to interrogate the data for its secrets. For this reason there is overlap between the two courses.

If you already have a strong grounding in Business Intelligence and would like to upgrade your knowledge to include topics from the Data Science MSc, we offer the relevant Data Science modules either on a stand alone basis or as a PGCert.

What's so good about Data Science at Dundee?

Our facilities will give you 24-hour access to our award winning and purpose-built Queen Mother Building. It has an unusual mixture of lab space and breakout areas, with a range of conventional and special equipment for you to use. It's also easy to work on your own laptop as there is wireless access throughout the building. Our close ties to industry allows us access to facilities such as Windows Azure and Teradata, and university and industry standard software such as Tableau for you to evaluate and use.

A booming Postgraduate culture where the School of Computing maintains a friendly, intimate and supportive atmosphere, and we take pride in the fact that we know all of our students - you're far more than just a matriculation number to us. We have a thriving postgraduate department with regular seminars and guest speakers.

Duncan Ross (Director of Data Sciences at Teradata) has said that: "The first and most important trait is curiosity. Insane curiosity. In many walks of life evolution selects against the kind of person who decides to find out what happens 'if I push that button'. Data Science selects for it."

How you will be taught

The programme will be delivered by Prof. Mark Whitehorn with input from Andy Cobley, Yasmeen Ahmad, Chris Hillman and other specialists from within the School of Computing in an innovative blend of live co-presented master-classes, video seminars and recorded materials. A series of guest speakers from industry will provide case studies across both semesters.

The programme will be provided predominantly on-campus, with two intensive study weeks in each of the semesters. Other classes may be taken off-campus using the university’s VLE, remote desktop, Adobe Connect and video conferencing systems along with telephone conferencing.

What you will study

Semester 1
Big Data - 20 Credits
Business Intelligent Systems - 20 Credits
Data Analysis and Visualisation - 20 Credits

Semester 2
Analytical Database Models and Design - 20 Credits
Advanced statistics and data mining - 20 credits
MDX - 20 Credits

Semester 3
Data Science Mini Project - 20 credits (for Certificate)
Data Science Research Project - 60 credits

The PGCert is intended for students who have a strong grounding in Business Intelligence and would like to upgrade their knowledge to include topics from the Data Science MSc. The modules are available stand alone for those who want to take their time studying the material and perhaps build up to a PGCert.

The three modules that make up the PGCert are:
Big Data
Advanced Anlaysis
Mini Project

For more information about the content of the course, please visit the course webpage on the School of Computing website.

How you will be assessed

Assessment will be by examination, practical coursework and research project.


Various job sites now report an increase in jobs carrying the title of data scientist. Other career opportunities are in intelligence analysis, data management/database maintenance, data processing manager, database development and research, business intelligence consultant and more.

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Why this course?. Our MSc in data analytics is designed to create rounded data analytics problem-solvers. Read more

Why this course?

Our MSc in data analytics is designed to create rounded data analytics problem-solvers.

This course focuses on the uses of data analytics techniques within business contexts, making informed decisions about appropriate technology to extract knowledge from data and understanding the theoretical principles by which such technology operates.

You'll gain a comprehensive skill set that will enable you to work in a variety of sectors using a blended learning approach that combines theory, intensive practice and industrial engagement.

Strathclyde's MSc in data analytics is unique by bringing together essential skills from three departments, Management Science, Mathematics & Statistics, and Computer & Information Sciences (CIS), in order to address the needs of a fast-growing industry.

This collaboration avoids the narrow interpretation of this subject offered by competitor institutions and presents significant opportunities for businesses to recruit data analytics experts with a high-level expertise and knowledge.

What you’ll study

The course will have a duration of 1 year, with two semesters of classes (120 credits in total) followed by an MSc dissertation project (60 credits) during the summer.

The class Data Analytics in Practice (20 credits) will be run over both semesters to provide you with a practical environment to apply methodological learnings from other classes into challenging projects from industry.

Semester 1

Semester 1 will additionally consist of five 10-credit core modules as listed under 'Course Content' which will provide the technical background to students. The contributions in Semester 1 will be split evenly between three departments.

This semester is designed to provide you with the fundamental technical analytics knowledge from all three departments.

  • Computer & Information Sciences courses will cover core techniques including machine learning and data mining as well as data visualisation and big data platforms
  • Mathematics courses will ensure you gain strong computational skills while establishing a broad knowledge of statistical tools essential for analytics
  • Management Science courses will build the foundations of business skills including problem structuring as well as decision analysis, in addition to providing essential practical skills

Semester 2

Semester 2 will additionally consist of a 10-credit core module as well as 40 credits worth of elective modules. To ensure breadth of knowledge, you'll be required to choose electives from at least two departments. This semester is designed to extend your core skills and provide you with opportunities through a broad range of electives to specialise in areas that you are particularly interested to excel.

The only technical core class will provide you with a thorough theoretical and practical understanding of optimisation techniques essential for data analytics, whereas each of the three departments will offer four to five elective courses, the majority of which are accessible to everyone on the course without any prerequisites. The final component of the MSc course will be a summer dissertation project, which can be completed either through a client-based project or a desk-based research project, depending on your interests. You will submit your dissertation in September to complete your degree requirements (pending any resits).

Work placement

You will have optional opportunities to complete your MSc summer dissertation projects in client-based projects, where a number of host organisations will be arranged by the department. These projects will be normally unpaid, however, all costs such as travel and accommodation will be covered by the host organisation if out of town.

Major projects

The taught modules on the programme introduce you to a variety of tools, techniques, methods and models. However, the practical reality of applying analytical methods in business is often far removed from the classroom. Working with decision-makers on real issues presents a variety of challenges.

For example, data may well be ambiguous and hard to come by, it may be far from obvious which data analytics methods can be applied and managers will need to be convinced of the business merits of any suggested solutions. While traditional teaching can alert students to such issues, understanding needs to be reinforced by experience.

This is primarily addressed by the core module ‘Data Analytics in Practice’, which takes place over both semesters. Every year, case studies and challenging projects are presented to our students by various organisations.


Strathclyde Business School (SBS) is one of the 76 triple-accredited business schools in the world, and is one of the largest of its kind in Europe. SBS was also recently selected as the "Business School of the Year" in Times Higher Education (THE) Awards.

The three departments involved in this course work together to provide a dynamic, fully-rounded and varied programme of specialist and cross-disciplinary postgraduate course.

Learning & teaching

The course is delivered in various ways. While most classes have regular lectures, tutorials and hands-on software sessions, experiential learning is a crucial part of the course. This is delivered through projects and case studies with various external organisations, and MSc projects.

There are also guest lectures and recruitment events throughout the year, as well as a number of career support sessions that provide you with invaluable career information and generic job hunting skills such as CV writing and how to handle interviews.


Every module has its own methods of assessment appropriate to the nature of the material. These include written assignments, exams, practical team projects, presentations and individual projects. Many modules involve more than one method of assessment to realise your potential.


The aim of the MSc in data analytics is to develop graduates who can use data analytics technology, understand the statistical principles behind the technologies and understand how to apply these technologies to solve business problems.

Graduates will be able to bridge the various knowledge domains that are relevant for tackling data analytics problems as well as being able to identify emerging themes and directions within data analytics. Graduates will display abilities across the three component disciplines

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Big Data and Data Engineering. Big data has turned out to have giant potential, but poses major challenges at the same time. On the one hand, big data is driving the next stage of technological innovation and scientific discovery. Read more

Big Data and Data Engineering

Big data has turned out to have giant potential, but poses major challenges at the same time. On the one hand, big data is driving the next stage of technological innovation and scientific discovery. Accordingly, big data has been called the “gold” of the digital revolution and the information age. On the other hand, the global volume of data is growing at a pace which seems to be hard to control. In this light, it has been noted that we are “drowning in a sea of data”.

Faced with these prospects and risks, the world requires a new generation of data specialists. Data engineering is an emerging profession concerned with big data approaches to data acquisition, data management and data analysis. Providing you with up-to-date knowledge and cutting-edge computational tools, data engineering has everything that it takes to master the era of big data.

Program Features

The Data Engineering program is located at Jacobs University, a private and international English-language academic institution in Bremen, Germany. The two-year program offers a fascinating and profound insight into the foundations, methods and technologies of big data. Students take a tailor-made curriculum comprising lectures, tutorials, laboratory trainings and hands-on projects. Embedded into a vibrant academic context, the program is taught by renowned experts. In a unique setting, students also team up with industry professionals in selected courses. Core components of the program and areas of specialization include:

- The Big Data Challenge

- Data Analytics

- Big Data Bases and Cloud Services

- Principles of Statistical Modeling

- Data Acquisition Technologies

- Big Data Management

- Machine Learning

- Semantic Web and Internet of Things

- Data Visualization and Image Processing

- Document Analysis

- Internet Security and Privacy

- Legal Aspects of Data Engineering and Data Ethics

For more details on the Data Engineering curriculum, please visit the program website at http://www.jacobs-university.de/data-engineering.

Career Options

Demand for data engineers is massive – in industry, commerce and the public sector. From IT to finance, from automotive to oil and gas, from health to retail: companies and institutions in almost every domain need experts for data acquisition, data management and data analysis. With an MSc degree in Data Engineering, you will excel in this most exciting and rewarding field with very attractive salaries. Likewise, an MSc degree in Data Engineering allows you to move on to a PhD and to a career in science an research.

Application and Admission

The Data Engineering program starts in the first week of September every year. Please visit http://www.jacobs-university.de/graduate-admission or use the contact form to request details on how to apply. We are looking forward to receiving your inquiry.

Scholarships and Funding Options

All applicants are automatically considered for merit-based scholarships of up to € 12,000 per year. Depending on availability, additional scholarships sponsored by external partners are offered to highly gifted students. Moreover, each admitted candidate may request an individual financial package offer with attractive funding options. Please visit http://www.jacobs-university.de/study/graduate/fees-finances to learn more.

Campus Life and Accommodation

Jacobs University’s green and tree-shaded campus provides much more than buildings for teaching and research. It is home to an intercultural community which is unprecedented in Europe. A Student Activities Center, various sports facilities, a music studio, a student-run café/bar, concert venues and our Interfaith House ensure that you will always have something interesting to do. In addition, Jacobs University offers accommodation for graduate students on or off campus.

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IN BRIEF. Study a course that is unique in the UK and has been specifically developed to meet the skills gap. Course content can be applied to very diverse fields- there are many job opportunities in this area. Read more


  • Study a course that is unique in the UK and has been specifically developed to meet the skills gap. Course content can be applied to very diverse fields- there are many job opportunities in this area.
  • Gain SAS certification.
  • Learn to tell a story from data. Become immersed in Big Data techniques and platforms, working with real-world messy data to gain experience across the data science stack.
  • Part-time study option
  • International students can apply


Have you ever wanted to ‘Mung’ data? Apply Machine Learning techniques? Search for hidden patterns? Be part of Big Data?      

This course is your opportunity to specialize as a Data Scientist, one of the most in demand roles across all sectors including health, retail, and energy. Companies such as Google and Microsoft, and also public organisations such as the NHS are struggling to fill their vacancies in this field due to    a  lack of suitably qualified people. This course is unique in the UK in that it has been developed as a MSc conversion course – if you have a good honours degree in any discipline with a demonstrable mathematical aptitude, an enquiring mind, a practical and analytical approach to problem solving,    and  an ambition for a career in data science; then this course is for you.    

During your time with us, you will develop an awareness of the latest developments in the fields of Data Science and Big Data including advanced databases, data mining and big data tools such as Hadoop. You will also gain substantial knowledge and skills with the SAS business intelligence software suite  due  to    the  partnership of the University with the SAS Student Academy.  

"We are especially pleased to endorse the new MSc in Data Science. With the explosion of interest and investment in data science teams, our customers cannot get enough graduates with SAS-based analytical skills. Courses such as this new MSc are an important step forward by the University to addressing this skills shortage, especially amongst home students." - SAS


This course covers a very comprehensive range of topics split in to four large modules worth 30 credits each plus the MSc Project worth 60 credits. External speakers from blue-chip and local companies will give seminars to complement your learning, that will be real-world case studies related to the subjects you are studying in your modules. These are designed to improve the breadth of your learning and could lead to ideas that you can develop for your MSc Project.


The course is focused around the underpinning knowledge and practical skills needed for employment within the data sciences industry. There will be 22 hours of lectures; 11 hours of tutorials and 22 hours workshops; 2 hours of examination-based assessment; and 245 hours of independent study, assessed coursework and preparation for examination. This makes a total of 300 hours total learning experience.

  • Lectures will be used to introduce ideas, and to stimulate group discussions.
  • Tutorials will be used to develop problem solving strategies and to provide practice and feedback with scenarios to help with exam preparation.
  • Workshops will be used to develop expertise in SAS tools, by analysing example datasets of increasing complexity.


  • 50% of the assessment will comprise a practical project where students will be given some data, will devise and carry out an analysis strategy and will present their interpretations and explain their strategy. 
  • 50% will comprise an examination, which will assess more theoretical aspects of the course and will explore students’ immediate response to unseen scenarios or data.


A recent report by e-Skills and SAS (Big Data Analytics: An assessment of the demand for labour and skills, 2012-1017) indicates the demand forecast for staff with big data skills is predicted to ”rise by 92% between 2012 and 2017, and by 2017 there will be at least 28,000 job openings for big data staff in the UK each year…”

With this qualification, you’ll be equipped with the skill set and technical knowledge relevant for the data science and big data job market.


The Informatics Research Centre in the School of Computing, Science and Engineering at the University of Salford builds on the history, success and achievements of the research in Computer Science and Information Systems developed at the University of Salford over the last thirty years.

Evolving around Data and Information in all their types and usages, the Centre covers all phases and processes from data pre-processing to engineering and visualisation. The Centre is developing novel methods and systems for the analysis and recognition of various data sets, learning behaviours and causal models. The techniques and systems developed have a wide range of potential applications including digitisation of historical documents, medical diagnosis, semantic tagging, segmentation of types of viewers and their behaviours, text mining and retrieval and data visualisation.

Forensic computing, digital investigation and Cyber security is another area of expertise supported by the centre both at the theoretical and application levels.

Many students go on to further research in the fields of:

  • Actionable Knowledge Discovery and Semantic Web
  • Software Engineering and applications
  • Big Data, Data Mining and Analytics
  • Image and document processing and analysis
  • Cyber Security and Forensics
  • Information visualisation and virtual environments


Facilities include a new Dell Cloud Computing platform with OpenStack and lab workstations, providing access to software platforms and languages specialized in Machine Learning, Data Mining, Statistical Analysis and Big Data including:

  • R, SAS Enterprise Guide & Miner, Python, Apache Hadoop & Spark, RapidMiner
  • NoSQL databases ie MongoDB

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Developed to meet the demand for data science professionals, our postgraduate Data Analytics course enables you to effectively structure, analyse and gain insight from a wide range of complex data across different industries. Read more

Developed to meet the demand for data science professionals, our postgraduate Data Analytics course enables you to effectively structure, analyse and gain insight from a wide range of complex data across different industries.

Designed in close consultation with industry partners including the NHS Business Services Authority, Teradata, BT, SAS, the Pensions Regulator and local Brighton companies, your learning is informed by current business developments through case studies looking at real-world data sets, research questions and scenarios. You have the opportunity to collaborate on projects with our industry partners, and can also use your own data, project ideas and industry links.

Guest lecturers will share their knowledge and expertise with you, such as Tom Khabaza who is a founding chairman of the Society of Data Miners, author of 9 Laws of Data Mining and was involved in designing the course.

You will develop a skill set in specialist data analytics and associated software, quantitative methods and techniques, and business intelligence. Our staff are experts in their field and you have the chance to develop your knowledge in specialist areas where we have ongoing research and expertise, such as sequential forecasting, natural language processing and image processing.

Whether you are a recent graduate or an experienced professional wanting to gain data analysis skills, this course is available on a full or part-time basis to help you manage your studies around other commitments. 

Course structure

The course covers three main areas:

  • data management – structuring and manipulating data for analysis purposes
  • data interpretation – statistical analysis using advanced features of industry-standard software such as SAS, SPSS and R
  • project management – the business-specific and strategic aspects of analytics.

You will learn how to assess project viability, propose sound business cases and strategies for analysis, perform and oversee analysis and manage large data projects successfully as well as developing your critical appraisal and presenting techniques. 

Based at our Moulsecoomb campus, you will have access to computer and research labs equipped with specialist, sophisticated software including SAS, SPSS Statistics and SPSS Modeller. Affordable student licences for home use are also available. 

With a flexible timetable to suit full-time or part-time students and commuters, and lecturers available to support you in your module choices, there are different study routes available to you.


You will study five core modules. One of these involves a major project, potentially in collaboration with industry. You will also choose option modules, subject to availability, allowing you to focus on particular areas of interest.

Core modules

  • Data Management – provides an understanding of contemporary database management systems. Explores a methodology for database design and development, and develops skills in searching, reporting and analysing the data. Topics covered include database implementation and administration, data modelling and business intelligence.
  • Programming for Analytics – provides competencies in computer programming and algorithm design with emphasis on statistical programming and data analysis. The module covers both general issues of algorithm design and data structures and implementation issues in R and SAS.
  • Data Visualisation and Analysis – covers principles of data visualisation and specialised tools for data visualisation and analysis such as SAS Visual Analytics and Qlikview. The module also explores the mathematical and statistical theory behind data analysis.
  • Business Analytics Strategy and Practice – develops analytics-specific project planning concepts within this context, enabling students to design and manage analytics projects and present the business case to senior management.
  • Industry project – substantial, independent project undertaken with the supervision of a member of the teaching team. Projects are normally industry-based using real data sets.

Option modules*

  • Multivariate Analysis and Statistical Modelling – design statistical experiments, analyse multivariate data and apply classical and modern statistical modelling techniques. Enhances skills in the use of specialist software such as R, SPSS or SAS.
  • Data Mining and Knowledge Discovery in Data – find useful and relevant patterns, trends and anomalies in data sets, and summarise them in a form which may be used to support enterprise decisions – one of the great challenges of the information age. Emphasis is on the big, real-world picture rather than inside-the-box systems design engineering details. 
  • Stochastic Methods and Forecasting – an understanding of stochastic models and their applications in a business context. The module also covers forecasting methods with the emphasis on selecting the best forecasting method for a business problem and correct application of that method.
  • Risk Analysis and Retail Finance – introduction to the statistical methods used to estimate risk and reward in retail credit. The focus is on retail finance especially the provision of credit and lending services.
  • Medical Statistics – introduction to the methods originally designed for clinical trials and now being used in other contexts including sociology and marketing research. Topics include assessment of risk factors, comparing treatments and assessing survival data.

*Option modules are indicative and may change, depending on timetabling and staff availability.  


A wide variety of organisations draw upon data analytics specialists to help produce valuable information for decision-making, for example commodity price forecasting, customer intelligence, clinical trials, R&D and many other areas utilising large amounts of data.

Graduates are able to choose from a range of private, governmental and academic roles, depending on their personal interests. Some of our full-time students find a full-time job and switch to part-time study in the middle of the course.

Graduate destinations include:

  • government bodies such as the Pensions Regulator and local councils
  • transnational corporations such as Capgemini
  • local companies such as iCrossing.

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

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

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

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

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

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

Our expert staff

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

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

Specialist facilities

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

Your future

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

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

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

Example structure

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

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

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