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Big data is the description used to encompass the huge amounts of data that is common to many businesses. It has been described as the next frontier for innovation, competition and productivity in business. Read more
Big data is the description used to encompass the huge amounts of data that is common to many businesses. It has been described as the next frontier for innovation, competition and productivity in business. It is essential for companies to embrace so that they can understand their customers better, develop new products and cut operational costs. This course has been developed to create graduates who can become data scientists capable of working with the massive amounts of data now common to many businesses. It is aimed at people who want to move into this rapidly expanding and exciting area.

The modules on this course help you develop the core skills and expertise needed by the data scientist. The course can be split into three main areas, statistics, computing and management. In the statistics section you study modules on data quality, data mining and data modelling. These modules cover the three main data areas, which are ensuring that data is reliable and of a high quality, searching the data to discover new information and presenting interpretations of that data to the end user.

The computing section covers areas related to massive datasets stored in the cloud, how data is stored and utilised within the distributed systems of an enterprise and how organisations can utilise data to change and improve business processes. The management modules are focused on developing your core skills around professionalism and research. All of which are valuable skills during your university studies and in your career.

Our partnerships with business inform the course design, ensuring the content is relevant, up to date and meets the needs of industry. These partnerships also enable the inclusion of some leading edge software such as SAS, SAP Hana, and Hadroop within the course.

For more information, see the website: https://www.shu.ac.uk/study-here/find-a-course/msc-big-data-analytics

Key areas of study

Key areas of study include
-Data quality and analysis.
-Technologies to store and mine data.
-Professionalism and research.

Professional recognition

This course includes the SAP Business Intelligence with SAP BW 7.3 and SAP BI 4.0 e-academy (UB130e). You also have the opportunity to sit the SAP certification exam and the SAS 9 base certification exam. Sheffield Hallam is a member of the SAS Student Academy, the SAP Student Academy and founding member of the SAP University Alliance.

Course structure

Full time – 12 to 18 months.
Part time – up to 6 years.
Starts September.

Core modules
-Research skills and principles
-Industrial expertise
-Data integration
-Statistical modelling
-Data mining
-Handling data in the cloud
-Big data and distributed systems
-Advanced statistical modelling
-Dissertation

Options
Choose one from:
-Organisational dynamics
-Social and economic aspects of the cloud

Assessment: essays, assignments, computer-based tests, practical projects, presentations, vivas.

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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.

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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.

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Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). Read more
Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). That experience is designed, in part, to develop skills for the SAS certification that partners the programme.

Digital Innovation

The aim of this module is to develop knowledge and skills necessary for the implementation of digital business models and technologies intended to realign an organization with the changing demands of its business environment (or to capitalise on business opportunities). Example topics of study include: understanding and justifying change, change management, digital business models, managing technology risks, ethical issues in change.

Quantitative Data Analysis

The aim of the module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. You will develop a practical understanding of core methods in data science application and research (eg bi-variate and multi-variate methods, regression etc). You will also learn to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.

High Performance Computational Infrastructures

The aim of the module is to develop knowledge and skills necessary for working effectively with the large-scale data storage and processing infrastructures that underpin data science. Again, you will develop both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content here covers, highly-scalable data-storage paradigms (eg NoSQL data stores) alongside cloud computing tools (eg Amazon EC2) and in-memory approaches.

Systems Project Management

This module examines the challenges in information systems project management. Example topics of study include traditional project management techniques and approaches, the relationship between projects and business strategy, the role and assumptions underpinning traditional approaches and the ways in which the state-of-the-art can be improved.

Big Data Analytics

The aim of the module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques (eg clustering, regression, support vector machines, boosting, decision trees and neural networks).

Data Management and Business Intelligence

The aim of the module is to develop knowledge and skills to support the development of business intelligence solutions in modern organisational environments. Example topics of study include issues in data/information/knowledge management, approaches to information integration and business analytics. Practical aspects of the subject are examined in the context of the data warehousing environment, with a focus on emerging in-memory approaches.

Data Visualisation

The aim of the module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets (eg to decision-makers). Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design and augmented reality. An emphasis is also placed on visual storytelling and narrative development.

Learning Development Project

The aim of the module is to develop a team-based integrative solution to a problem/challenge drawn from the business, scientific and/or social domain (as appropriate). Working as part of a small team you will: Refine a coherent set of stakeholder requirements from an open-ended (business, scientific or social) problem/challenge; develop a solution addressing those requirements that coherently draws upon the knowledge and skills of other modules within the programme; effectively evaluate the solution (with stakeholders where appropriate).

Dissertation (including Research Methods)

Your dissertation is an opportunity to showcase your project management and subject specific skills to potential employers, and also serves as valuable experience and a solid building block if you wish to pursue a PhD on completion of the MSc. You will be encouraged to critically examine the academic and industrial contexts of your research, identify problems and think originally when proposing potential solutions that serve to demonstrate and reflect your ideas.

As preparation for the dissertation, you will be given a grounding in both quantitative and qualitative methods of data collection and analysis appropriate to conducting empirical and/or experimental research.

Read less
Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). Read more
Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). That experience is designed, in part, to develop skills for the SAS certification that partners the programme.

Digital Innovation

The aim of this module is to develop knowledge and skills necessary for the implementation of digital business models and technologies intended to realign an organization with the changing demands of its business environment (or to capitalise on business opportunities). Example topics of study include: understanding and justifying change, change management, digital business models, managing technology risks, ethical issues in change.

Quantitative Data Analysis

The aim of the module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. You will develop a practical understanding of core methods in data science application and research (eg bi-variate and multi-variate methods, regression etc). You will also learn to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.

High Performance Computational Infrastructures

The aim of the module is to develop knowledge and skills necessary for working effectively with the large-scale data storage and processing infrastructures that underpin data science. Again, you will develop both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content here covers, highly-scalable data-storage paradigms (eg NoSQL data stores) alongside cloud computing tools (eg Amazon EC2) and in-memory approaches.

Systems Project Management

This module examines the challenges in information systems project management. Example topics of study include traditional project management techniques and approaches, the relationship between projects and business strategy, the role and assumptions underpinning traditional approaches and the ways in which the state-of-the-art can be improved.

Big Data Analytics

The aim of the module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques (eg clustering, regression, support vector machines, boosting, decision trees and neural networks).

Data Management and Business Intelligence

The aim of the module is to develop knowledge and skills to support the development of business intelligence solutions in modern organisational environments. Example topics of study include issues in data/information/knowledge management, approaches to information integration and business analytics. Practical aspects of the subject are examined in the context of the data warehousing environment, with a focus on emerging in-memory approaches.

Data Visualisation

The aim of the module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets (eg to decision-makers). Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design and augmented reality. An emphasis is also placed on visual storytelling and narrative development.

Learning Development Project

The aim of the module is to develop a team-based integrative solution to a problem/challenge drawn from the business, scientific and/or social domain (as appropriate). Working as part of a small team you will: Refine a coherent set of stakeholder requirements from an open-ended (business, scientific or social) problem/challenge; develop a solution addressing those requirements that coherently draws upon the knowledge and skills of other modules within the programme; effectively evaluate the solution (with stakeholders where appropriate).

Dissertation (including Research Methods)

Your dissertation is an opportunity to showcase your project management and subject specific skills to potential employers, and also serves as valuable experience and a solid building block if you wish to pursue a PhD on completion of the MSc. You will be encouraged to critically examine the academic and industrial contexts of your research, identify problems and think originally when proposing potential solutions that serve to demonstrate and reflect your ideas.

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

Programme description

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

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

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

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

Online learning

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

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

Programme structure

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

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

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

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

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

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

Option courses

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

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

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

Learning outcomes

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

Career opportunities

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

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Data Science brings together computational and statistical skills for data-driven problem solving. Read more
Data Science brings together computational and statistical skills for data-driven problem solving. This rapidly expanding area includes machine learning, deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence.

Degree information

The programme comprises core machine learning methodology and an introduction to statistical science, combined with a set of more specialised and advanced options covering computing and statistical modelling. Projects are offered both within UCL Computer Science and from a wide range of industry partners.

Students undertake modules to the value of 180 credits.

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

Core modules
-Applied Machine Learning
-Introduction to Supervised Learning
-Introduction to Statistical Data Science

Optional modules - students choose a minimum of 30 credits and a maximum of 60 credits from the following optional modules:
-Cloud Computing (Birkbeck)
-Machine Vision
-Information Retrieval & Data Mining
-Statistical Natural Language Processing
-Web Economics

Students choose a minimum of 0 credits and a maximum of 30 credits from these optional Statistics modules:
-Statistical Design of Investigations
-Applied Bayesian Methods
-Decision & Risk

Students choose a minimum of 15 credits and a maximum of 15 credits from these elective modules:
-Supervised Learning
-Graphical Models
-Bioinformatics
-Affective Computing and Human-Robot Interaction
-Computational Modelling for Biomedical Imaging
-Stochastic Systems
-Forecasting

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning
The programme is delivered though a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed through a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

Careers

Data science professionals are increasingly sought after as the integration of statistical and computational analytical tools becomes more essential to organisations. A thorough understanding of the fundamentals required from the best practitioners, and this programme's broad base, assists data scientists to adapt to rapidly evolving goals. This is a new degree and information on graduate destinations is not currently available. However, MSc graduates from across the department frequently find roles with major tech and finance companies including:
-Google Deepmind
-Microsoft Research
-Dunnhumby
-Index Ventures
-Last.fm
-Cisco
-Deutsche Bank
-IBM
-Morgan Stanley

Why study this degree at UCL?

The 2014 Research Excellence Framework ranked UCL first in the UK for computer science. 61% of its research work is rated as world-leading and 96% as internationally excellent.

UCL Computer Science staff have research interests ranging from foundational machine learning and large-scale data analysis to commercial aspect of business intelligence. Our extensive links to companies provide students with opportunities to carry out the research project with an industry partner.

The department also enjoys strong collaborative relationships across UCL; and exposure to interdisciplinary research spanning UCL Computer Science and UCl Statistical Science will provide students with a broad perspective of the field. UCL is home to regular machine learning masterclasses and big data seminars.

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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
Optimisation
Independent Scholarship

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Surrey Business School’s Business Analytics programme is dedicated to producing creative and knowledgeable Business Analysts with the ability to convert Big Data to actionable insight in business. Read more
Surrey Business School’s Business Analytics programme is dedicated to producing creative and knowledgeable Business Analysts with the ability to convert Big Data to actionable insight in business.

Whether it’s using Artificial Intelligence to improve a chess programme, or understanding the power of visualisation from a simple graph.

With input from industry experts in class and on-site, you will engage with real-world business problems.

PROGRAMME OVERVIEW

Artificial Intelligence and Machine Learning, Big Data. New technologies and ways of working are changing the way we make decisions.

This programme will take your career to the next level and develop your ability to confidently make high impact businesses decisions that are driven by data.

The programme focuses on two key areas: analysing business data, and solving business challenges analytically. Optional modules allow you to further specialise in areas such as the economic, managerial or finance or aspects of the subject.

Furthermore, you will benefit from hands-on experience of a wide range of analytics software such as simulators and mathematical tools.

PROGRAMME STRUCTURE

The programme is studied full-time over one academic year. It consists of eight taught modules and a dissertation. The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.
-Data Analytics
-Supply Chain Analytics
-Econometrics I
-Machine Learning and Visualisations
-Principles of Accounting
-Foundations of Finance: Finance and Investments
-Supply Chain and Logistics Management
-Information for Decision Making
-Managing Decisions Implementation
-Introduction to Marketing Analytics
-Econometrics II
-Business Process Management
-Innovation Management
-Investment Analysis
-Dissertation

CAREER PROSPECTS

Business analytics students often pursue careers as consultants, researchers, managers, and analysts.

SOFTWARE

You will get hands-on experience using a wide range of tools in the course. An indicative list of the software tools is as follows:
-Excel (using the Solver and Data Analysis Add-Ins) and Tableau for decision making and visual analytics
-COGNOS and SQL Server for Business Intelligence for analytical processing
-Apache Hadoop (Map Reduce) with Amazon’s Elastic Cloud or IBM’s Smart Cloud for distributed Big Data analytics
-SAP for Enterprise Resource Planning
-R, SPSS and EViews for coding, statistics and forecasting
-ILOG’s Optimisation Studio (Cplex) for optimisations
-Matlab for algorithms and programming and Simulink (SimEvents) for simulations
-Arena (or Simul8) for Discrete Event Simulations

EDUCATIONAL AIMS OF THE PROGRAMME

The programme’s aim is to provide a high quality education that is both intellectually rigorous and at the forefront of management science research, relevant for problem solving and decision making by managers.

It will respond to the emergent needs of corporations and academia for professionals who are able to work with analytical tools to generate value from available Information depots and take advantage of the vast amounts of data now provided by the modern ICT and ERP systems, which underlie the operations of modern corporations.

The program will implant understanding of the theoretical base around knowledge management and knowledge work, practical skills and experience in using analytical software tools.

It will allow future professional managers and consultants to cope with an increasingly complex and global operational environment of the modern corporation.

Completion of the programme will provide a sound foundation for those considering continuing their academic development towards a PhD degree in the management disciplines.

The programme is structured in a way that would provide students with a choice between a more quantitative intensive track of modules or a qualitative analytic (business development track) which would reflect students’ personal strengths and preferences and match future career aspirations.

The compulsory modules provide a sound foundation which builds an analytical skillset using relevant statistical and management theories, and supports the development of practical hands-on experience applying the theoretical aspects using real-world data to address corporate challenges and find solutions to actual problems.

The readings in the module will build a sound basis which would allow students to access and understand the academic literature and undertake empirical investigations in the areas of decision modelling and business development.

PROGRAMME LEARNING OUTCOMES

The programme provides opportunities for students to develop and demonstrate knowledge and understanding, skills, qualities and other attributes in the following areas:

Knowledge and understanding
-A systematic, in-depth understanding of the development; issues and influences relevant to discipline of Management Decision Making, Management Science, and Data Science.
-Deep and thorough understanding of quantitative analytical methodologies and hands-on experience with decision-making software and data management tools.
-Knowledge about issues, application and analysis of Big Data
-An understanding of the academic research process.

Intellectual / cognitive skills
-Demonstrate deep learning, understanding of the material and ability to apply the knowledge and demonstrate skills in problem solving in the topic space of the modules studied
-Carry out assessments of data in a repository, select the appropriate analysis tools, design and execute an analytical methodology (not required for PG Certificate), apply adequate visualization methodologies to present the results and interpret the findings and finally to communicate the results effectively to a select audience

Professional practical skills
-Demonstrate the ability to independently evaluate critical approaches and techniques relevant to Business Analytics
-Know and apply a range of techniques and tools to analyse data related to business operations
-Capability of selecting the right methodology and software to solve management and operational business issues
-Relate existing knowledge structures and methodologies to analytical business challenges

Key / transferable skills
-Conduct critical literature review; to select, define and focus upon an issue at an appropriate level
-Develop and apply relevant and sound methodology
-Apply the methodology to analyse the issue
-Develop logical conclusions and recommendations
-Be aware of the limitations of the research
-Identify modifications to existing knowledge structures and theoretical frameworks and therefore to prose new areas for investigation, new problems, new or alternative applications or methodological applications

GLOBAL OPPORTUNITIES

We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.

In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.

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Business Intelligence is basically about concepts and methods to improve business decision making by using fact-based support systems. Read more
Business Intelligence is basically about concepts and methods to improve business decision making by using fact-based support systems. Business Intelligence as a discipline is made up of several related activities, including data mining, analytical processing and business process improvement.

The programme provides you with in-depth knowledge of methods for analysing data to support decision-making and for improving business processes on the basis of business analytics.

The programme provides you with an in-depth knowledge about

- Methods for analysing data to support decision making
- How to improve business processes on the basis of business analytics

The courses of the programme will provide you with analytical skills to identify new business opportunities or identify inefficient business processes. The teaching form of the program encourages student participation and this in combination with the final thesis work will provide you with self-management and communication skills.

PROGRAMME STRUCTURE

PREREQUISITE COURSES

In the first semester you follow the prerequisite courses that form the methodological and academic basis for the further study programme.

In Business Analytics gives the student a set of tools and models that are essential for the design and evaluation of empirical investigations that can support decisions in the business intelligence area. The course will cover major research tools including research design, experiments,response models and forecasting.

IS Development & Implementation in a Business Context introduces a range of methods and techniques that can be used to understand, plan and execute the processes in which information systems are developed, implemented, evaluated and modified to enable the student to participate in the development, acquisition and implementation of information systems.

Data Warehousing provides the student with knowledge about the wide variety of database management systems available for a data warehouse solution and how to choose a solution that is relevant for the business intelligence project in question.

SAS and SQL for Business Analytics provides the student with skills to conduct proper data analysis using some of the most flexible environments available. Focus will be on data management and data manipulation with the purpose to prepare for a statistical analysis.

SPECIALISATION COURSES

In the second semester you follow the specialisation courses of the programme.

Data Mining for Business Decisions teaches students how to work with large datasets and how relationships in such data can be detected with the purpose to transform data into knowledge. Business applications cover a broad range from marketing to accounting, logistics and supply chain management.

In Advanced Market Research the focus is on analytical customer relationship management. The course is devoted to customer base analysis and predictive modelling with a primary focus on customer lifetime value and customer retention.

Supply Chain Management aims to provide an introduction and a framework of the design and operations of performance management in contemporary supply chains.

Project Management aims to introduce the contents of general project management competences and provide the students with skills to manage a BI project.

In the third semester you can choose elective courses within your areas of interest. The courses can either be taken at the school during the semester, at the Summer University or at one of our more than 200 partner universities abroad. You can also participate in internship programmes either in Denmark or abroad.

The fourth semester is devoted to the final thesis. You may choose the topic of the thesis freely and so get a chance to concentrate on and specialise in a specific field of interest. The thesis may be written in collaboration with another student or it may be the result of your individual effort. When the thesis has been submitted, it is defended before the academic advisor as well as an external examiner.

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The focus of this programme is on contemporary substantive issues in criminology and criminal justice and on criminological research methods. Read more
The focus of this programme is on contemporary substantive issues in criminology and criminal justice and on criminological research methods. It is particularly appropriate for those engaged in criminal justice policy analysis and development or similar work in allied fields.

The programme develops a theoretical, policy and technical understanding of key issues within criminology, criminal justice and research methods. More specifically, it aims to develop an advanced understanding of the complex nature of crime, harm and victimisation together with an appreciation of the role of the state/criminal justice system in the regulation of human behaviour, deviance and crime. The programme will equip you to design and implement social scientific research using a broad range of methodologies, consider research ethics, analyse and present the material such research generates.

Through combining criminology and research methods, the programme enables you to think logically and in an informed manner about criminological issues. The programme fosters a critical awareness of the relationship between theory, policy and practice and enables you to utilize your research knowledge of research skills and translate these into research practice in the field of criminology and broader social science research professions.

See the website http://www.lsbu.ac.uk/courses/course-finder/criminology-social-research-methods-msc

Modules

You'll undertake modules from a broad base of subject areas including:

- Criminological theory
This module charts the development of criminological thinking from the onset of modernity through to the present day. It will place discrete theories in their proper sociological, historical, political and cultural contexts. It will seek to establish the implications and relationships of various theories to criminal justice policy. A number of contemporary issues (terrorism, urban disturbances, and gang culture) will be explored with a view to critically evaluating the value of competing theoretical frameworks.

- Crime, harm and victimisation
The module aims to deconstruct the fundamental elements of criminology: the crime, the criminal and the victim. It begins by examining historical and contemporary patterns of crime and criminality, as officially measured, within the UK and beyond. It then engages with more critical academic debates about defining and measuring crime, considering definitions of crime as: a breach of criminal law; a violation of collective conscience; a product of conduct norms; a social construct; ideological censure; a gendered reality; a violation of human rights, and; social or environmental harm. The module engages with critical deconstructions of the 'offender' and the 'victim', considering how these are socially constructed and how our understanding of these, like of 'crime', has changed and continues to change in late-/post-modern society.

- Responding to crime: justice, social control and punishment
This module explores some of the key issues and controversies in the delivery of justice, social control and punishment. It begins with a critical consideration of the concept of justice and emphasises the significance of this in relation to how the state responds to various forms of crime. It encourages you to think critically about the role of the state in the regulation of behaviour and provides an overview of key changes that have occurred in the field of crime control and criminal justice. One of the key features of contemporary crime control discourse is the rise of risk management and the pursuit of security. This module outlines the ways in which such a discourse has transformed criminal justice thinking and practices of both policing and penal policy, and also of crime (and harm) prevention.

- Criminological research in practice
This module uses examples from recent and current research conducted by members of the Crime and Justice Research Group at LSBU and external guest speakers to develop both the research training and subject understanding elements of the MSc, demonstrating how research becomes knowledge – generating theoretical advances, policy initiatives, new research questions and university curricula. Lectures/seminars will take the form of a research commentary, talking you through a research project from idea inception through research design, fieldwork, analysis and dissemination and, where appropriate, on to the influences research has had (or could have) on subsequent academic works and policy developments. Particular emphasis will be placed on challenges peculiar to criminological research.

- Methods for social research and evaluation: philosophy, design and data collection
This module introduces you to core concepts in social research and shows how they can be used to address social scientific questions and practical issues in policy evaluation. You'll be introduced to central topics in the philosophy of social sciences and the effect they have on research choices. You are then introduced to different ways research can be designed and the ways design affects permissible inferences. You are then introduced to the theory of measurement and sampling. The final third of the module focuses on acquiring data ranging from survey methods through qualitative data collection methods to secondary data.

- Data analytic techniques for social scientists
You are introduced to a range of analytic techniques commonly used by social scientists. It begins by introducing you to statistical analysis, it then moves to techniques used to analyse qualitative data. It concludes by looking at relational methods and data reduction techniques. You'll also be introduced to computer software (SPSS, NVivo and Ucinet) that implements the techniques. Students will gain both a conceptual understanding of the techniques and the means to apply them to their own research projects. An emphasis will be placed on how these techniques can be used in social evaluation.

- Dissertation
The dissertation is a major part of your work on the MSc, reflected in its value of 60 credits. The aim of the dissertation is to enable students to expand and deepen their knowledge of a substantive area in criminology, whilst simultaneously developing their methodological skills. You'll choose an area of investigation and apply the research skills of design and process, modes of data generation and data analysis techniques to undertake a 15,000 word dissertation. You'll be allocated a dissertation supervisor from the departmental team and will meet regularly for personal supervision meetings.

Employability

This MSc will enable you to pursue a range of professional careers in criminal justice related work in statutory, commercial or community voluntary sectors and operating at central, regional and local government levels, for example, the Home Office; police forces; local government; crime and disorder reduction partnerships and their equivalencies throughout the world.

The acquisition of specific criminological and research methods knowledge will also enhance the career opportunities if you are currently working in the field. The specialist focus on research methods also offers an excellent foundation for those interested in undertaking subsequent doctoral research in the field.

LSBU Employability Services

LSBU is committed to supporting you develop your employability and succeed in getting a job after you have graduated. Your qualification will certainly help, but in a competitive market you also need to work on your employability, and on your career search. Our Employability Service will support you in developing your skills, finding a job, interview techniques, work experience or an internship, and will help you assess what you need to do to get the job you want at the end of your course. LSBU offers a comprehensive Employability Service, with a range of initiatives to complement your studies, including:

- direct engagement from employers who come in to interview and talk to students
- Job Shop and on-campus recruitment agencies to help your job search
- mentoring and work shadowing schemes.

Professional links

The Crime and Criminal Justice Research Group, (CCJRG), at LSBU has developed a strong national and international reputation for delivering high quality and real life impact research. It has worked closely with a range of government agencies, including the Office for Criminal Justice Reform (Ministry of Justice); Government Office for London; the Scottish Executive, Northern Ireland Office and the Equalities and Human Rights Commission. It has also undertaken extensive research in collaboration with various London local authorities together with a range of voluntary and charity-based agencies.

Placements

Our criminology programme also has a strong voluntary work scheme.You're encouraged to undertake voluntary work in a variety of criminal justice related agencies. Recent positions have been within the police service, the prison service, legal advice, victim support, domestic violence and child abuse agencies and youth offending and youth mentoring schemes.

Teaching and learning

Study hours:
Year 1 class contact time is typically 6 hours per week part time and 12 hours per week full time plus individual tutorial and independent study.

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The Master of Health Sciences (MHSc) degree is a professional degree designed to give the graduate strong research methodology skills that can be applied to their academic and clinical interests. Read more

General Information

The Master of Health Sciences (MHSc) degree is a professional degree designed to give the graduate strong research methodology skills that can be applied to their academic and clinical interests. The program is adapted towards students who have an MD and want to gain research experience by applying epidemiological and statistical methods to a major paper under the supervision of a faculty member.

Students pursue course work in clinical research skills. These skills will include survey and questionnaire design and analysis, systematic reviews, clinical trial design, data analysis and presentation. Qualitative as well as program and economic evaluation methodology courses are available. The underlying precepts for all courses are critical thinking skills. There will be an opportunity to explore health policy and population health concepts but not in depth due to the limited number of credits needed to graduate.

Quick Facts

- Degree: Master of Health Science
- Specialization: Health Sciences
- Subject: Health and Medicine
- Mode of delivery: On campus
- Program components: Coursework + Major Project/Essay required
- Faculty: Faculty of Medicine

Master of Health Science

The M.H.Sc. program is designed to provide graduate education primarily for physicians. The emphasis is on the application of rigorous methodology to the study of health issues in populations, spanning assessment, and policy development and assurance.

Course work is in a wide variety of topics: etiologic research; data base analysis; assessment of diagnostic tests; clinical trials; policy analysis; utilization studies; investigations of occupational and environmental health issues. The program's goal is to enhance the students' development of critical thinking and skills in research design and conduct.

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Our MSc Data Networks and Security course will provide opportunities for you to engage in the design and implementation of secured and optimized communication network solutions, including SDN (Software Defined Networks and wireless technologies. Read more
Our MSc Data Networks and Security course will provide opportunities for you to engage in the design and implementation of secured and optimized communication network solutions, including SDN (Software Defined Networks and wireless technologies.

This will be achieved by industry-led, research-informed, practice-based teaching and learning. Using industry-standard resources, you will apply the skills and knowledge gained to real-life project scenarios across industry, commerce and public sector.

The programme of study will include areas such as requirements capture, network design, evaluation, securing and optimisation, ranging from hardware configuration to protocol analysis and software definition. The programme will include research and scholarly activity in order to incorporate the latest thinking into the proposed solution.

What's covered in the course?

On this course, you will learn to:
-Critically evaluate and apply knowledge of advanced routing principles.
-Evaluate and apply advanced routing protocols for specific networking solutions.
-Evaluate a variety of routing techniques for a given network environment.
-Critically evaluate routing policy requirements for a network.
-Design and implement ethernet-based LANs. Ensuring security within the given environment.
-Apply mathematical analysis to use VLSM efficiently.
-Apply security considerations to the design and management of networks.
-Design/plan and implement LAN/WAN solutions which require switched hybrid.
-Critically assess SDN solutions in both the industry and research domains.
-Design an SDN-based network for a given system, identifying appropriate components and network structure.
-Implement an appropriate SDN controller to manage device configuration, and any other relevant network policies within an SDN network.
-Select, plan and implement an appropriate testing strategy to validate security requirements against a threat model.
-Critically evaluate the requirements for penetration testing, ethical hacking and effectively communicate security audit results to a variety of audiences.
-Design and conduct security assessment experiments to expose security vulnerabilities and to interpret, analyse and critically evaluate the resulting data to recommend remedial actions.
-Critically appraise the role of security testing within the wider context of continuous security improvements to the information assurance processes within the organisation.

Why choose us?

-The Centre for Cloud Computing houses the Cisco Networking Academy, which has an international reputation for delivering high-quality teaching, training and support acrossEurope, the Middle East and Africa.
-In six purpose-built rooms, the Centre also houses £500,000 of computer networking and communications equipment, together with more than £200,000 of web-based equipment and bookable resources.
-The course provides opportunities for you to engage in advanced studies using problem-based learning and flipped curricula strategies. You will work in groups and on your own to deliver solutions to industry-related problems and scenarios.
-The unique combination of employer-led, research-informed technical knowledge and practical experience on industry-standard resources makes our graduates more employable and sought after.
-The course encourages critical thinking and problem solving, giving you the opportunities for research.

Course in depth

All the modules are practice-based and learning is carried out in the labs. Each 20-credit module will have two hours contact, and you are expected to undertake approximately six additional hours of learning, research and assessment preparation for each module.

Assessment is carried out through presentations (both group and individual), timed tests and exams, written reports, research activity and publication of findings, and practical-based time assessments.

At the start of the course, there will be a three-week, full-time induction tool kit, comprising of a review of CCNA and associated technologies.

The course also provides the base knowledge for students to undertake the CCNA certification, and with an additional boot camp to undertake the individual CCNP certification exams.

Modules
-Information Security 20 credits
-Software Defined Network Engineering 20 credits
-Advanced Networking Systems 20 credits
-Network Management 20 credits
-Advanced Ethical Hacking 20 credits
-Research Methods 20 credits
-Project and placement 60 credits

Enhancing your employability skills

The University is eager to recognise students have made the effort to gain industry experience and stand out from the typical graduate. Thus, we offer a range of options for you to get extra awards and recognition for your work in industry.

We also have our Graduate+ programme, an extracurricular awards framework that is designed to augment the subject-based skills that you’ve developed throughout the programme with broader employability attributes, which will enhance your employability options upon graduating.

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A solid, theoretical understanding of computer technology with plenty of attention for the wide range of ICT applications. The enormous and rapidly growing power of ICT is the main driving force shaping our modern society. Read more
A solid, theoretical understanding of computer technology with plenty of attention for the wide range of ICT applications.

The enormous and rapidly growing power of ICT is the main driving force shaping our modern society. This goes beyond the technical and economical aspects. ICT is also essential in research as all sciences benefit from the raw power of software in processing huge quantities of data. But how do we manage and control the complexity of modern software? How can we make the most of the opportunities? And, not to be forgotten, how can we secure the ICT infrastructures we so heavily rely on? The Master’s programme in Computing Science covers all these aspects.

We offer specialisations in each terrain: security, software, data and the mathematics at the base of it all. These are not, however, isolated disciplines. We also look at the interesting interplay between them. For example, by taking privacy into account when dealing with big data. And by doing a thorough analysis of newly designed software to prevent security breaches later. Thanks to a large number of optional courses, you can decide where you want your focus to be.

The job opportunities in computer science are excellent: many of our students get offered jobs before they’ve even graduated and almost all have positions within six months after graduating. Many of our graduates find jobs as systems builders, ICT specialists or ICT managers and a few continue as researchers.

See the website http://www.ru.nl/masters/computingscience

Specialisations

- Cyber Security
You’ll learn to assess the security of existing ICT solutions, and how to develop more secure solutions for the future. This specialisation is offered in collaboration with the Eindhoven University of Technology, meaning you get taught by many of the best cyber security experts in the country.

- Data Science
You’ll learn how to turn real-world data sets into tools and useful insights, with the help of software and algorithms. Radboud University and the iCIS research institute are leading in research on legal and privacy aspects of data science and on the societal and administrative impact of data science.

- Mathematical Foundations of Computer Science
You’ll come to understand the fundamental mathematical concepts of computation and information in order to stretch the boundaries of computer technology. We’re the only specialisation in the country – and one of the few in the world – to focus on the theoretical and abstract playing field linking mathematics and computer science.

- Software Science
You’ll learn how to design high-level software that guarantees safety while controlling its complexity. At Radboud University, we are specialised in model based development. In other words, writing and testing code before they are unleashed in the real world or built into an expensive prototype.

- Societal Master's specialisations
You can either follow one of the above-mentioned research Master's specialisations as a whole (2 years), or you can combine the first year of the research specialisation with an additional year of one of the societal Master’s specialisations, namely:
- Science in Society
- Science, Management and Innovation

Why study Computing Science at Radboud University?

- All of our specialisations are closely related to the research carried out within the Institute for Computing and Information Science (iCIS).
- Our approach is pragmatic as well as theoretical. As an academic, we don’t just expect you to understand and make use of the appropriate tools, but also to program and develop your own.
- There are plenty of high profile companies in the vicinity such as Philips and ASML, where you could do an internship or the research for your Master’s project.
- Exceptional students who choose the Data Science specialisation have the opportunity to do a double degree in Computing Science together with the specialisation in Web and Language Interaction (Artificial Intelligence). This will take three instead of two years.

Career prospects

There is a serious shortage of well-trained information specialists. Often students are offered a job before they have actually finished their study. About 20% of our graduates choose to go on to do a PhD but most find jobs as systems builders, ICT specialists or ICT managers in the private sector or within government.

Our research in this field

The Institute for Computing and Information Science (iCIS) is the research institute that is connected to Radboud University. Within this institute there are three research sections:
- Model Based System Development
- Digital Security
- Intelligent Systems

Within each research section there are different departments/groups that have their own research. On the websites of the research sections you will find more information about their research, publications, the departments/groups and contact information.

See the website http://www.ru.nl/masters/computingscience

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