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Masters Degrees (Data Networks)

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

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

Objectives

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

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

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

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

Distinctive points of this course

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

Practical information

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

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

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

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

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

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

Teaching

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

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

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

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The world is awash with data and much more is on the way, creating a tidal wave of Big Data. Data Engineers develop the infrastructure to store, manage, analyse this wave of data, to bridge the gap between Data and Computer Science. Read more
The world is awash with data and much more is on the way, creating a tidal wave of Big Data. Data Engineers develop the infrastructure to store, manage, analyse this wave of data, to bridge the gap between Data and Computer Science. This unique course will give you the skills you’ll need to succeed as a Data Engineer.

Why study Data Engineering at Dundee?

The role of “Data Scientist” has been described as the “sexiest job of the 21st Century. However, there is a emerging a new role, that of Data Engineer as more companies are realising they need employees with specific skills to handle the amount of data that is being generated and the coming tidal wave from the Internet of Things.

This MSc has been created with industry input to prepare its students with the skills to handle this wave of data and to be at the forefront of its exploitation. Students on the sister programmes (“Data Science” and “Business Intelligence”) have gone on to work for some of the biggest companies in the industry and we are confident that graduates from this MSc will have the same success.

The School of Computing at the University of Dundee has been successfully offering related MSc programmes such as Business Intelligence and Data Science since 2010. These innovative programmes attract around 40 students per year, drawn from across Europe and Overseas.

What's so good about Data Engineering at Dundee?

Our facilities:
You will have 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.

Special features

The University of Dundee has close ties with the Big Data industry, including Teradata, Datastax and Microsoft. We have worked with SAS, Outplay, Tag, GFI Max, BrightSolid and BIPB, and our students have enjoyed guest lectures from Big Data users such as O2, Sainsbury’s, M&S and IBM.

You will be able to work with a range of leading researchers and tutors, including top vision and imaging researchers and BI experts. Our honorary staff include legal experts, entrepreneurs and renowned industry experts such as John Richards of the newly formed IBM Watson Group.

How you will be taught

The course will be taught by staff of the School of Computing. Depending on the modules you take this will include Andy Cobley, Professor Mark Whitehorn, and Professor Stephen McKenna.

What you will study

The course will be taught in 20 credit modules with a 60 credit dissertation. Students will require to complete 180 credits for the award of the MSc (including 60 credits for the dissertation). Students completing 120 credits (without the dissertation) will be eligible for a Postgraduate Diploma.

Course content

Each module on the course is designed to give the student the skills and understanding they need to succeed in the Data Engineering/ Science field. Content on the course includes (but is not limited to):

CAP theorem
Lamda Architecture
Cassandra, Neo4j and other nosql databases
The Storm distributed real time computation system
Hadoop, HDFS, MapReduce, and other Hadoop/SQL technologies
Spark and Shark frameworks
Data Engineering languages such as Python, erlang, R, Matlab
Vision systems, which are becoming increasingly important in data engineering for extracting features from large quantities of images such as from traffic, medical and industrial
RDBMS systems which will continue to play an important role in data handing and storage. You will be expected to research the history of RDMBS and delve in to the internals of modern systems
OLAP cubes and Business Intelligence systems, which can be the best and quickest way to extract information from data stores
Goals of machine learning and data mining
Clustering: K-means, mixture models, hierarchical
Dimensionality reduction and visualisation
Inference: Bayes, MCMC
Perceptrons, logistic regression, neural networks
Max-margin methods (SVMs)
Mining association rules
Bayesian networks

How you will be assessed

The course is assessed through a combination of examinations, coursework, presentations and interviews. Each module is different: for instance the Big data module has 40% coursework, consisting of Erlang programming and a presentation on nosql databases, along with an examination worth 60%.

Careers

Our experience suggests that graduates of this course will have most impact in the following areas:

Cloud and web based industries that handle large volumes of fast moving data that need to be stored, analysed and maintained. Examples include the publishing industry (paper, TV and internet), messaging services, data aggregators and advertising services

Internet of Things. A large amount of data is being generated by devices (robotic assembly lines, home power management, sensors etc.) all of which needs to be stored and analysed.

Health. The NHS (and others) are starting to store and analyse patient data on an unprecedented scale. The healthcare industry is also combining data sources from a large number of databases to improve patient well-being and health outcomes

Games industry. The games industry records an extraordinary amount of data about its customers' play activities, all of which needs to be stored and analysed. This course will equip students with the knowledge and skill to engage with the industry.

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The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Read more
The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Technology is growing and evolving at an incredible speed, and both the rate of growth of data we generate and the devices we use to process it can only increase.

Data science is a growing and important field of study with a fast-growing number of jobs and opportunities within the private and public sector. The application of theory and methods to real-world problems and applications is at the core of data science, which aims especially to use and to exploit big data.

If you are interested in solving real-world problems, you like to develop skills to use smart devices efficiently, you want to use and to foster your understanding of mathematics, and you are interested and keen to use statistical techniques and methods to interpret data, MSc Data Science at Essex is for you. You study a balance of solid theory and practical application including:
-Computer science
-Programming
-Statistics
-Data analysis
-Probability

Our Department of Mathematical Sciences has an international reputation in many areas including semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology.

You also benefit from being taught in our School of Computer Science and Electronic Engineering, who are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of their research rated ‘world-leading’ or ‘internationally excellent’ (REF 2014).

The collaborative work between our departments has resulted in well-known research in areas including artificial intelligence, data analysis, data analytics, data mining, data science, machine learning and operations research.

Our expert staff

Our Department of Mathematical Sciences is a small but influential department, so our students and staff know each other personally. You never need an appointment to see your tutors and supervisors, just knock on our office doors – we are one of the few places to have an open-door policy, and no issue is too big or small.

The academic staff in our School of Computer Science and Electronic Engineering 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 Paul Scott, who researches data mining, models of memory and attention, and artificial intelligence, and Professor Maria Fasli, who researches data exploration, analysis and modelling of complex, structured and unstructured data, big data, cognitive agents, and web search assistants.

Specialist facilities

-Unique to Essex is our renowned Maths Support Centre, which offers help to students, staff and local businesses on a range of mathematical problems. Throughout term-time, we can chat through mathematical problems either on a one-to-one or small group basis
-We have our own computer labs for the exclusive use of students in the Department of Mathematical Sciences – in addition to your core maths modules, you gain computing knowledge of software including Matlab and Maple
-We have six laboratories that are exclusively for computer science and electronic engineering students
-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
-You 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
-We host regular events and seminars throughout the year
-Collaborate with the Essex Institute of Data Analytics and Data Science (IADS) and the ESRC Business and Local Government (BLoG) Data Research Centre of the University of Essex
-The UK Data Archive and the Institute for Social and Economic Research (ISER) at Essex contribute to our internationally outstanding data science environment

Your future

With a predicted shortage of data scientists, now is the time to future-proof your career. Data scientists are required in every sector, carrying out statistical analysis or mining data on social media, so our course opens the door to almost any industry, from health, to government, to publishing.

Our graduates are highly sought after by a range of employers and find employment in financial services, scientific computation, decision making support and government, risk assessment, statistics, education and other sectors.

We also offer supervision for PhD, MPhil and MSc by Dissertation. We have an international reputation in many areas such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology, and our staff are strongly committed to research and to the promotion of graduate activities.

We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-Dissertation (optional)
-MSc Project and Dissertation (optional)
-Applied Statistics
-Machine Learning and Data Mining
-Modelling Experimental Data
-Text Analytics
-Artificial Neural Networks (optional)
-Bayesian Computational Statistics (optional)
-Big-Data for Computational Finance (optional)
-Combinatorial Optimisation (optional)
-High Performance Computing (optional)
-Natural Language Engineering (optional)
-Nonlinear Programming (optional)
-Professional Practice and Research Methodology (optional)
-Programming in Python (optional)
-Information Retrieval (optional)
-Data Science and Decision Making (optional)
-Research Methods (optional)
-Statistical Methods (optional)
-Stochastic Processes (optional)

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

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

Course content

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

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

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

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

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

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

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

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

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

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The MSc in Data Science & Analytics, jointly offered by the Department of Computer Science and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. Read more
The MSc in Data Science & Analytics, jointly offered by the Department of Computer Science and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. The combination of sophisticated computing and statistics modules will develop skills in database management, programming, summarisation, modelling and interpretation of data. The programme provides graduates with an opportunity, through development of a research project, to investigate the more applied elements of the disciplines.

Visit the website: http://www.ucc.ie/en/ckr49/

Course Details

The MSc in Data Science and Analytics is a significant collaboration between the Departments of Computer Science and Statistics; designed to provide graduates with the skills and knowledge required to help companies and public bodies deal with ever increasing and complex data. The programme emphasises the application of Computer Science and Statistics methodologies helping transform data into useful information that can support decision making.

Format

A typical 5 credit module:
• 2 lecture hours per week
• 1–2 hours of practicals per week
• Outside these regular hours students are required to study independently by reading and by working in the laboratories and on exercises.

Structure

Students must attain 90 credits through a combination of:

- Core Modules (30 credits)
- Elective Modules (30 credits)
- Dissertation (30 credits)

Part 1 (60 credits)

- Core Modules (30 credits) -

CS6405 Data Mining (5 credits) - Dr. Marc Van Dongen
ST6030 Foundations of Statistical Data Analytics (10 credits)
ST6033 Generalised Linear Modelling Techniques (5 credits)

- Database Modules -

Students who have adequate database experience take:

CS6408 Database Technology (5 credits) - Mr. Humphrey Sorensen
CS6409 Information Storage and Retrieval (5 credits) - Mr. Humphrey Sorensen

- Students who have not studied databases take:

CS6503 Introduction to Relational Databases (5 credits)
CS6505 Database Design and Administration (5 credits)

Elective Modules (30 credits)

Students must take at least 10 credits of CS (Computer Science) modules and at least 10 credits of ST (Statistics) modules from those listed below:

CS6322 Optimisation (5 credits) - Dr. Steve Prestwich
CS6323 Analysis of Networks and Complex Systems (5 credits) - Prof. Gregory Provan
CS6509 Internet Computing for Data Science (5 credits)
ST6032 Stochastic Modelling Techniques (5 credits)
ST6034 Multivariate Methods for Data Analysis (10 credits)
ST6035 Operations Research (5 credits)
ST6036 Stochastic Decision Science (5 credits)

- Programming Modules -

Students who have adequate programming experience take:

CS6406 Large-Scale Application Development and Integration l (5 credits) - Professor Gregory Provan
CS4607 Large-Scale Application Development and Integration ll (5 credits) - Professor Gregory Provan

- Students who have not studied programming take:

CS6506 Programming in Python (5 credits)
CS6507 Programme in Python with Data Science and Applications (5 credits) - Dr. Kieran Herley

Part 2 (30 credits)

Students select one of the following modules:

CS6500 Dissertation in Data Analytics (30 credits)
ST6090 Dissertation in Data Analytics (30 credits)

Assessment

Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2015 Book and for each module in the Book of Modules 2015/2016 - http://www.ucc.ie/modules/

Postgraduate Diploma in Data Science and Analytics

Students who pass each of the taught modules may opt to exit the programme and be conferred with a Postgraduate Diploma in Data Science and Analytics.

Careers

This programme aims to prepare students to manage, analyse and interpret large heterogeneous data sources. Graduates will design, compare and select appropriate data analytic techniques, using software tools for data storage/management and analysis, machine learning, as well as probabilistic and statistical methods. Such abilities are at the core of companies that constantly face the need to deal with large data sets.

Companies currently seeking graduates with data analytics skills include: firms specialising in analytics, financial services and consulting, or governmental agencies.

Companies actively recruiting Computer Science graduates in 2014-15 include:

Accenture, Aer Lingus, Amazon, Apple, Bank of America Merrill Lynch, Bank of Ireland, BT, Cisco, CiTi-Technology, Cloudreach, Dell, Digital Turbine Asia Pacific, EMC, Enterprise Ireland, Ericsson, First Derivatives, Guidewire, IBM, Intel, Open Text, Paddy Power, Pilz, PWC, SAP Galway Transverse Technologies, Trend Micro, Uniwink, Version 1 (Software).

How to apply: http://www.ucc.ie/en/study/postgrad/how/

Funding and Scholarships

Information regarding funding and available scholarships can be found here: https://www.ucc.ie/en/cblgradschool/current/fundingandfinance/fundingscholarships/

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.

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.

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This programme is designed for those who want to pursue a career as data scientists, deriving valuable insights and business relevant information from large amounts of data. Read more
This programme is designed for those who want to pursue a career as data scientists, deriving valuable insights and business relevant information from large amounts of data. You will cover the fundamental statistical (eg machine learning) and technological tools (eg cloud platforms, Hadoop) for large-scale data analysis.

The Big Data science movement is transforming how Internet companies and researchers over the world address traditional problems. Big Data refers to the ability of exploiting the massive amounts of unstructured data that is generated continuously by companies, users, devices, and extract key understanding from it.

A Data Scientist is a highly skilled professional, who is able to combine state of the art computer science techniques for processing massive amounts of data with modern methods of statistical analysis to extract understanding from massive amounts of data and create new services that are based on mining the knowledge behind the data. The job market is currently in shortage of trained professionals with that set of skills, and the demand is expected to increase significantly over the following years.

The course leverages the world-leading expertise in research at Queen Mary with our strategic partnership with IBM and other leading IT sector companies to offer to students a foundational MSc on the field of Data Science. The MSc modules cover the following aspects:

-Statistical Data Modelling, data visualization and prediction
-Machine Learning techniques for cluster detection, and automated classification
-Big Data Processing techniques for processing massive amounts of data
-Domain-specific techniques for applying Data Science to different domains: Computer Vision, Social Network Analysis, Bio-Engineering, Intelligent Sensing and Internet of Things
-Use case-based projects that show the practical application of the skills in real industrial and research scenarios.
-Students will be offered lectures that explain the core concepts, techniques and tools required for large-scale data analysis. -Laboratory sessions and tutorials will put these elements to practice through the execution of use cases extracted from real domains. -Students will also undertake a large project where they will demonstrate the application of Data Science skills in a complex scenario.

The programme is offered by academics from the Networks, Centre for Intelligent Sensing, Risk and Information Management, Computer Vision and Cognitive Science research groups from the School of Electronic Engineering and Computer Science. This is a team of more than 100 researchers (academics, post-docs, research fellows and PhD students), performing world leading research in the fields of Intelligent Sensing, Network Analytics, Big Data Processing platforms, Machine Learning for Multimedia Pattern Recognition, Social Network Analysis, and Multimedia Indexing.

Industrial Experience

The industrial placement currently takes place towards the end of the first year for a maximum of 12 months. It is the student’s responsibility to secure their placement, the school will offer guidance and support in finding and securing the placement but the onus is on the student to secure the job and arrange the details of the placement.

Currently if you are not able to secure a placement by the end of your second semester we will transfer you onto the 1 year FT taught programme without the Industrial Experience, this change would also be applied to any visa if you were here on a student visa.

The industrial placement consists of 8-12 months spent working with an appropriate employer in a role that relates directly to your field of study. The placement is currently undertaken between the taught component and the project. This will provide you with the opportunity to apply the key technical knowledge and skills that you have learnt in your taught modules, and will enable you to gain a better understanding of your own abilities, aptitudes, attitudes and employment potential. The module is only open to students enrolled on a programme of study with integrated placement.

If you do not secure a placement you will be transferred onto the 1 year FT programme.

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.

Read less
With the proliferation of mobile and pervasive devices with network capability, along with widespread popularity of the Internet, more and more users and application providers expect services to be available anytime and anywhere. Read more
With the proliferation of mobile and pervasive devices with network capability, along with widespread popularity of the Internet, more and more users and application providers expect services to be available anytime and anywhere.

The Master of Networks and Security (MNS) gives you the skills to manage and administer computer networks and security, and prepares you for a career in network administration or network management, or as a systems analyst, systems designer, data communications specialist, or network security engineer or administrator.

As a network and security professional, your specialised skills will always be in high demand, as well as highly rewarded.

The MNS expands your knowledge of how to design, deploy and maintain networks and application services, by combining theory with practice. You explore issues faced both by users and application providers, and devise possible solutions.

The MNS caters to students from a variety of backgrounds by including preparatory IT units. However, if you already have a degree in IT or engineering, you may accelerate your study with an exemption from these preparatory units, or perhaps choose to take further networks and security electives.

The advanced studies of an MNS include a range of topics, from network structure, design, quality of service and protocols, to information, software and network security.

The course emphasises the principles and management of computer networks and the security technologies upon which organisations rely. You will learn how to evaluate the security needs of an organisation's infrastructure and create plans to protect it against potential attacks and security breaches.

In your final semester, you may take part in an Industry Experience program, working in a small team with industry mentors to develop entrepreneurial IT solutions. Or you may undertake a minor-thesis research project, investigating cutting-edge problems in networks and security under the supervision of internationally recognised researchers.

High-achieving students who complete the research component may progress to further research study.Graduates may be eligible for Australian Computer Society (ACS) professional membership.

Visit the website http://www.study.monash/courses/find-a-course/2016/networks-and-security-c6002?domestic=true

Overview

With the proliferation of mobile and pervasive devices with network capability and the popularity and the availability of Internet, more and more users and application providers are seeking and providing services with the access and delivery paradigm of anytime and anywhere. In order to harness such rapidly changing technology, one needs to have clear understanding, knowledge and experience that transcends these technologies in order to able to design, deploy and maintain networks and application services. This course not only provides an in-depth knowledge on the principles of these technologies, but also explores issues that are faced both by the users and the application providers, and provides possible solutions. The theory is interrelated with the practice which makes this course unique.

Course Structure

The course is structured in three parts, A, B and C. All students complete Part B (core studies). Depending upon prior qualifications, you may receive credit for Part A Foundations for advanced networks and security studies or Part C Advanced practice or a combination of the two.

Note that if you are eligible for credit for prior studies you may elect not to receive the credit.

PART A. Foundations for advanced networks and security studies
These studies will provide an orientation to the field of networks and security at graduate level. They are intended for students whose previous qualification is not in a cognate field.

PART B. Core Master's study
These studies draw on best practices within the broad realm of IT networks and security theory and practice. You will gain an understanding of information and computer security and IT project management principles. Your study will focus on your choice of units within Networks and Security.

PART C. Advanced practice
The focus of these studies is professional or scholarly work that can contribute to a portfolio of professional development. You have two options.

The first option is a research pathway including a thesis. Students wishing to use this Masters course as a pathway to a higher degree by research should take this first option.

The second option is a program of coursework involving advanced study and an Industry experience studio project.

Students admitted to the course, who have a recognised honours degree in a discipline cognate to networks and security, will receive credit for Part C, however, should they wish to complete a 24 point research project as part of the course they should consult with the course coordinator.

For more information visit the faculty website - http://www.study.monash/media/links/faculty-websites/information-technology

Find out how to apply here - http://www.study.monash/courses/find-a-course/2016/networks-and-security-c6002?domestic=true#making-the-application

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This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling… Read more
This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations, from retailers such as Tesco or Amazon, to manufacturers like BMW, to health-care providers, and to public administration.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/mscdatascienceandanalytics.aspx

Why choose this course?

- Big Data is now part of every sector and function of the global economy. Planning and strategic decision-making processes rely on large pools of data that need to be captured, aggregated, stored, and analysed.

- You will acquire both the foundational knowledge and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations from retailers such as Tesco or Amazon, to manufacturers like BMW, health-care providers, or public administration. People with this set of skills are in short supply and high demand.

- You will have the opportunity to choose options among an exciting range of topics in Computer Science, Economics, Information Security, Management and Mathematics.

- You will also be well prepared to pursue studies at PhD level, which several companies prefer for their research laboratories and more advanced roles.

- Industry connections have informed the content and design of the course. External contacts in both academia and industry enrich the programme of seminars and guest lectures, which are an integral part of the course.

- Royal Holloway is located in the ‘M4 corridor’, west of London, a major high-technology hub also called ‘England’s Silicon Valley’.

- Royal Holloway is a very prestigious university in which to study. We are ranked not only as one of the 16 most beautiful universities in the world, but also one of the best: in 2012/13, the Times Higher Education World University Rankings placed the College 15th in the UK, 45th in Europe and 119th in the world.

Department research and industry highlights

- The excellence of our research in Machine Learning – the science behind ‘Big Data’ – is recognized worldwide, and the topics taught reflect that excellence.

- In the most recent Research Assessment Exercise (RAE 2008), the Department ranked 11th among UK Computer Science departments for its research output.

- The Department is ranked third in the UK for graduate employability by the Times Good University Guide 2013.

- The Department has an Industrial Liason Board that comprises senior representatives from Microsoft, Cognex, CSC, Bank of America Merrill Lynch, Kalido, Bathwick Group, Pentatonix, Blackrock, Oracle, Investec and QubeSoft.

Course content and structure

You will take taught modules during Term One (October to December) and Term Two (January to March). Examinations are held in May. You then take an industrial placement, after which you come back for your project/dissertation (12 weeks).
Please visit our websitefor additional information on this degree.

On completion of the course graduates will have:
- A highly analytical approach to problem solving.
- A strong background in data modelling and business intelligence.
- Knowledge of computational and statistical data analysis.
- A background in machine learning, statistics, and data mining.
- Ability to develop, validate, and use effectively machine learning models and statistical models.
- Ability to apply machine learning and data mining techniques to Information Retrieval and Natural Language Processing.
- Knowledge of and ability to work with software to automate tasks and perform data analysis.
- Knowledge of and ability to work with structured, unstructured, and time-series data.
- Ability to extract value and insight from data.
- Knowledge of and ability to work with methods and techniques such as clustering, regression, support vector machines, boosting, decision trees, neural networks.
- Appreciation and knowledge of non-statistical approaches to data analysis and machine learning.
- Ability to work with software packages such as MATLAB and R.
- Knowledge of and ability to work with relational databases (SQL), non-relational databases (mongodb), as well as with Hadoop/pig scripting and other big data manipulation techniques.
- Knowledge of and ability to work with Python, Perl, and Shell Scripting.

Assessment

Assessment is carried out by a variety of methods including coursework and a dissertation. The placement is assessed as part of your degree.

Employability & career opportunities

Our graduates are among the most employable in the UK – we rank third in the UK for graduate employability – and, in recent years, have entered many different Computer Science-related roles including network systems design and engineering, web development and production. Other graduates choose to enter careers with a management or financial slant. Our graduates have found employment at a wide range of organisations including Logica, British Telecom, British Aerospace, Microsoft, Amazon.com, American Express, Sky and Orbis Technology. At the same time, this course also equips you with a solid foundation for continued PhD studies.

Your careers ambitions are supported by our College Careers Service, located right next door to the Department. They offer application and interview coaching, career strategy discussions, and the opportunity to network with major employers on campus. Our careers service is provided by the Careers Group, the main provider of graduate recruitment services in London.

How to apply

Applications for entry to all our full-time postgraduate degrees can be made online https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

Read less
This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling… Read more
This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations, from retailers such as Tesco or Amazon, to manufacturers like BMW, to health-care providers, and to public administration.

As part of the course, you will take an industrial placement, where you will gain valuable experience by putting your knowledge and skills into practice.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/mscdatascienceandanalytics(yearinindustry).aspx

Why choose this course?

- Big Data is now part of every sector and function of the global economy. Planning and strategic decision-making processes rely on large pools of data that need to be captured, aggregated, stored, and analysed.

- You will acquire both the foundational knowledge and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations from retailers such as Tesco or Amazon, to manufacturers like BMW, health-care providers, or public administration. People with this set of skills are in short supply and high demand.

- You will have the opportunity to choose options among an exciting range of topics in Computer Science, Economics, Information Security, Management and Mathematics.

- You will also be well prepared to pursue studies at PhD level, which several companies prefer for their research laboratories and more advanced roles.

- Taking a placement is an excellent opportunity to gain industrial experience (which gives you an extra edge when applying for jobs in the future) and acquire skills that can only be fully picked up in a work environment.

- Industry connections have informed the content and design of the course. External contacts in both academia and industry enrich the programme of seminars and guest lectures, which are an integral part of the course.

- Royal Holloway is located in the ‘M4 corridor’, west of London, a major high-technology hub also called ‘England’s Silicon Valley’.

- Royal Holloway is a very prestigious university in which to study. We are ranked not only as one of the 16 most beautiful universities in the world, but also one of the best: in 2012/13, the Times Higher Education World University Rankings placed the College 15th in the UK, 45th in Europe and 119th in the world.

Department research and industry highlights

- The excellence of our research in Machine Learning – the science behind ‘Big Data’ – is recognized worldwide, and the topics taught reflect that excellence.

- In the most recent Research Assessment Exercise (RAE 2008), the Department ranked 11th among UK Computer Science departments for its research output.

- The Department is ranked third in the UK for graduate employability by the Times Good University Guide 2013.

- The Department has an Industrial Liaison Board that comprises senior representatives from Microsoft, Cognex, CSC, Bank of America Merrill Lynch, Kalido, Bathwick Group, Pentatonix, Blackrock, Oracle, Investec and QubeSoft.

Course content and structure

You will take taught modules during Term One (October to December) and Term Two (January to March). Examinations are held in May. You then take an industrial placement, after which you come back for your project/dissertation (12 weeks).

Your placement will take up to one year and, if you are an overseas student, your visa will cover the two years of the programme. The placement attracts a salary and is assessed as part of your degree. You will be assigned a supervisor by the host company, who is responsible for directing your work. You will be assigned an academic supervisor, who visits to check if you are integrating successfully and the type of work being undertaken is appropriate, and supports you in general during your placement. If you cannot or decide not to take a placement, you revert to the normal one-year degree.

On completion of the course graduates will have:
Throughout your degree, you will have the opportunity to acquire the following skills:

- A highly analytical approach to problem solving.
- A strong background in data modelling and business intelligence.
- Knowledge of computational and statistical data analysis.
- A background in machine learning, statistics, and data mining.
- Ability to develop, validate, and use effectively machine learning models and statistical models.
- Ability to apply machine learning and data mining techniques to Information Retrieval and Natural Language Processing.
- Knowledge of and ability to work with software to automate tasks and perform data analysis.
- Knowledge of and ability to work with structured, unstructured, and time-series data.
- Ability to extract value and insight from data.
- Knowledge of and ability to work with methods and techniques such as clustering, regression, support vector machines, boosting, decision trees, neural networks.
- Appreciation and knowledge of non-statistical approaches to data analysis and machine learning.
- Ability to work with software packages such as MATLAB and R.
- Knowledge of and ability to work with relational databases (SQL), non-relational databases (mongodb), as well as with Hadoop/pig scripting and other big data manipulation techniques.
- Knowledge of and ability to work with Python, Perl, and Shell Scripting.
- Work experience and appreciation of how your work fits into the organizational and development processes of a company.

Assessment

Assessment is carried out by a variety of methods including coursework, examinations and a dissertation. The placement is assessed as part of your degree.

Employability & career opportunities

Our graduates are among the most employable in the UK – we rank third in the UK for graduate employability – and, in recent years, have entered many different Computer Science-related roles including network systems design and engineering, web development and production. Other graduates choose to enter careers with a management or financial slant.

Our graduates have found employment at a wide range of organisations including Logica, British Telecom, British Aerospace, Microsoft, Amazon.com, American Express, Sky and Orbis Technology. At the same time, this course also equips you with a solid foundation for continued PhD studies.

Your careers ambitions are supported by our College Careers Service, located right next door to the Department. They offer application and interview coaching, career strategy discussions, and the opportunity to network with major employers on campus. Our careers service is provided by the Careers Group, the main provider of graduate recruitment services in London.

How to apply

Applications for entry to all our full-time postgraduate degrees can be made online https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

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The Mobile and High Speed Telecommunication Networks course is designed to provide you with in-depth knowledge of modern high-speed telecommunication systems and to enhance your professional development in the rapidly expanding field of personal communications. Read more
The Mobile and High Speed Telecommunication Networks course is designed to provide you with in-depth knowledge of modern high-speed telecommunication systems and to enhance your professional development in the rapidly expanding field of personal communications.

This course has two main components: 2G - 4G mobile communications, and fixed high-speed and multi-service networks. Emphasis is given to developing essential industrial and commercial skills. The project is a major element of the course and gives you the opportunity to enhance your career prospects by acquiring in-depth knowledge of a key aspect of telecommunications technology.

Why choose this course?

You will be taught industrially relevant techniques using some of the same tools and software used by the communications industry. Our telecommunications laboratories are equipped for the design, testing and analysis of mobile wireless and optical networks using industry standard tools such Asset, Ranopt, OptSim, OpNet and Matlab. You will have the opportunity to analyse real data from operational 2G and 3G networks and to design 3G and LTE networks.

Our networking laboratories are equipped with modern Cisco routers, switches and security devices to enable design construction and testing of complete high bandwidth secure, wired and wireless networks. You will have the opportunity to put the skills you have gained into practice if you choose to undertake our 1 year optional placement. The universal nature of the technical skills developed in our programmes means our courses are of equal relevance to both new graduates and those with many years of industrial experience.

This course in detail

MSc in Mobile and High Speed Telecommunication Networks has a modular course-unit design providing you with maximum flexibility and choice. To qualify for a master’s degree, you must pass modules amounting to 180 credits. This comprises six taught modules (20 credits each) plus your dissertation (60 credits).

The MSc in Mobile and High Speed Telecommunication Networks with placement enables you to work in industry for a year in the middle of your course to give valuable workplace experience. Placements are not guaranteed, but the departments dedicated placement team will help with the process of finding and applying for placements. To qualify for a master’s degree with placement, you must pass modules amounting to 180 credits plus the zero credit placement module. This comprises six taught modules (20 credits each) plus your dissertation (60 credits).

The Postgraduate Diploma in Mobile and High Speed Telecommunication Networks allows you to concentrate on the taught part of the degree and is ideal for people working in the communications industry who wish to brush up their skills. To qualify for a Postgraduate Diploma, you must pass modules amounting to 120 credits. This comprises six taught modules (20 credits each). In some cases, it may be possible for a student on a Postgraduate Diploma to do 3 taught modules (20 credits each) plus your dissertation (60 credits).

The Postgraduate Certificate in Mobile and High Speed Telecommunication Networks allows you to concentrate on the taught part of the degree and is ideal for people working in the communications industry who wish to learn a specific area in this rapidly changing discipline. To qualify for a Postgraduate Certificate, you must pass modules amounting to 60 credits. This comprises three taught modules (20 credits each).

We also offer a Postgraduate Certificate Mobile and High Speed Telecommunication Networks Research Project.

In Semester 1 you can choose from the following modules:
-Research and Scholarship Methods (compulsory for MSc)
-Digital Mobile Communications (alternative compulsory for MSc and PGDip)
-Digital Communications (alternative compulsory for MSc)
-Network Principles (alternative compulsory for MSc)

In Semester 2 you can choose from the following modules:
-Advanced Mobile Communications (compulsory for MSc and PGDip)
-High Speed Mobile Communications (compulsory for MSc and PGDip)
-Optical and Broadband Networks (alternative compulsory for MSc)
-Multiservice Networks (alternative compulsory for MSc)

As courses are reviewed regularly, the list of taught modules you choose from may vary from the list here.

Students undertaking an MSc with placement will do a 1 year placement in industry. The placement will be undertaken after the taught component and before doing the dissertation.

Students studying for an MSc will also take:
-MSc Dissertation (completed over summer)

Teaching and learning

The taught modules include lectures, seminars, library and internet research, and practical design and experimentation. Assessments include coursework exercises, presentations, essays and examinations (maximum 50% for taught modules).

Teaching staff include experienced academic staff and recent recruits from the telecommunications industry. Visiting speakers give you relevant and up-to-date developments from within the industry.

Laboratory facilities include the latest industry standard tools for mobile and wireless network analysis and software modelling facilities to enable network design.

Careers and professional development

Our MSc students come from all over the world and follow careers in many countries after their graduation. They are engaged in activities such as 3G network design, WiMax and LTE roll-out, handset compliance, DVB-H planning, communications software development and university lecturing. Many of them have commented on how the course content and training enabled their careers to flourish.

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This course takes an immersive approach to learning both the principles and practices of computer systems with much of the material based around examples and practical exercises. Read more
This course takes an immersive approach to learning both the principles and practices of computer systems with much of the material based around examples and practical exercises. Students completing this course will have a firm grasp of the current practices and directions in computer systems and will be able to design and build for example, distributed systems for the Web using Internet, Intranet and other technologies.

Programme Objectives
To provide the foundations for understanding of core ideas, methods and technologies in computer science.
To provide the technical skills and background material so that the postgraduate will be able to conduct a near state-of-the-art research or development project;
To provide the graduate with a range of specialist and transferable skills;
To provide the educational base for further professional development and lifelong learning.
Course Topics
Data networks and communications, project foundations and management tools, broadband communication systems, technologies for Internet systems, agent technologies and Artificial Intelligence, introduction to distributed systems and mobile systems, project and dissertation.

Taught Modules:

Java programming: This module provides students with an in-depth understanding of current and emerging Java programming concepts and programming variations. The module teaches the basic and advanced structures of Java and makes use of the object-oriented approach to software implementation. It also gives an in-depth understanding of advanced Java concepts in the area of user interfaces and will enable students to apply the theoretical knowledge of the Java language onto a test-case software development scenario.

Introduction to distributed systems: This module will introduce key ideas in distributed Systems and its role and application in operating systems and middleware. On completion of this module students will have an understanding of the key issues for distributed systems at OS level or as middleware, they will understand core concepts of concurrency, be able to program multithreaded and distributed applications and understand the issues and use of algorithms for transactional systems.

Data networks and communications: This module will provide an in-depth understanding of how real communication networks are structured and the protocols that make them work. It will give the students an ability to understand in detail the process required to provide an end-to-end connection.

Technologies for Internet Systems: In this module, students will be introduced to state of the art technologies and tools for Internet Systems and in particular e-commerce systems.

Agent Technologies: This module provides an in-depth understanding of technologies from Artificial Intelligence research such as machine learning, data mining, information retrieval, natural language processing, and evolutionary programming. It will look at the application of agent-oriented technologies for Artificial Life, for building Web search engines, for use in computer games and in film (such as the MASSIVE software developed for the Lord of the Rings movies), and for robotics. It will also provide an introduction to agent-oriented programming using the NetLogo programming language.

Foundations of computer graphics: This module will teach techniques, algorithms and representations for modelling computer graphics and enable students to code 2D and 3D objects and animations.

Database systems: Students completing this module will gain an in depth understanding of DBMS/Distributed DBMS architecture, functionality, recovery and data storage techniques. Students will also have a full understanding of how queries are processed and the importance of database maintenance. This module is designed to enable students to perform research into one or two areas of databases; for example, object oriented databases and deductive databases.

Project foundations and management tools: This module prepares students for their MSc research project, including reference search and survey preparation and familiarisation with project management tools.

MSc Research project: After the successful completion of the taught component of the MSc programme, students will spend the remainder of the time undertaking a research project and producing an MSc Dissertation. During this process, students will conduct project work at state-of-the-art research level and to present this work as a written dissertation. Completing a project and dissertation at this level will train students in: problem solving; researching new topics; organizing knowledge; exercising elementary time and project management skills; reporting and writing skills.

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