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

Introduction

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

Key information

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

Course objectives

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

English language requirements

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

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

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

Structure and content

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

REF2014

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

Strengths

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

Career opportunities

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

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Drawing on our research excellence in this area, this innovative programme of study in big data and business intelligence is designed to give graduates a competitive advantage in the modern, fast growing business domain. Read more
Drawing on our research excellence in this area, this innovative programme of study in big data and business intelligence is designed to give graduates a competitive advantage in the modern, fast growing business domain. This is one of the first MSc programmes in the UK covering these leading-edge technologies. The programme provides students with the deeper knowledge, advanced skills and understanding that will allow them to contribute to the development and design of big data systems as well as distributed/internet-enabled decision support application software systems, using appropriate technologies, architectures and techniques (e.g. data analytics, business intelligence, NoSQL, data mining, data warehousing, distributed data management and technologies, Hadoop, etc.).

Additionally, the programme enables students to understand and assess the security and legal implications of e-commerce applications and provides students with appropriate knowledge of business and commerce relevant to transacting business on the internet. The courses take a software engineering approach to the construction of applications and focus on modern software engineering methods, tools and techniques that enable an integrated life-cycle software development view.

Through our short course centre opportunity may also be provided to study for the following professional qualifications: Microsoft Technology Associate Exams; Certified Professional Java SE Programmer; Java Certified Associate; Oracle Certified Associate (OCA).

Visit the website http://www2.gre.ac.uk/study/courses/pg/com/cgbdbi

Computing - General

Come and study in the award-winning Department of Computing & Information Systems on the magnificent Greenwich Campus. Welcoming home and international students from all backgrounds, CIS provides an exciting, diverse and friendly environment in which to study.

The latest university league table published in the Sunday Times, has rated the computer science department as seventh in the UK for teaching excellence.

What you'll study

Full time
- Year 1:
Students are required to study the following compulsory courses.

PG Project (CIS) (60 credits)
Data Warehousing (15 credits)
Database Architectures and Administration (15 credits)
Database Tools (15 credits)
Business Intelligence and Data Mining (15 credits)
Enterprise Systems Integration (15 credits)
Big Data (15 credits)
Essential Professional and Academic Skills for Masters Students
English Language Support Course (for Postgraduate Students in the School of Computing and Mathematical Sciences)

Students are required to choose 15 credits from this list of options.

Requirements Analysis & Methods (15 credits)
Software Tools and Techniques (15 credits)
User Centred Web Engineering (15 credits)

Students are required to choose 15 credits from this list of options.

System Modelling (15 credits)
Systems Development Management and Governance (15 credits)
Programming Enterprise Components (15 credits)
Multi-structured Data and NoSQL Technology (15 credits)

Part time
- Year 1:
Students are required to study the following compulsory courses.

Database Architectures and Administration (15 credits)
Business Intelligence and Data Mining (15 credits)
Enterprise Systems Integration (15 credits)
Big Data (15 credits)
Essential Professional and Academic Skills for Masters Students
English Language Support Course (for Postgraduate Students in the School of Computing and Mathematical Sciences)

- Year 2:
Students are required to study the following compulsory courses.

PG Project (CIS) (60 credits)
Data Warehousing (15 credits)
Database Tools (15 credits)

Students are required to choose 15 credits from this list of options.

Requirements Analysis & Methods (15 credits)
Software Tools and Techniques (15 credits)
User Centred Web Engineering (15 credits)

Students are required to choose 15 credits from this list of options.

System Modelling (15 credits)
Systems Development Management and Governance (15 credits)
Programming Enterprise Components (15 credits)
Multi-structured Data and NoSQL Technology (15 credits)

Fees and finance

Your time at university should be enjoyable and rewarding, and it is important that it is not spoilt by unnecessary financial worries. We recommend that you spend time planning your finances, both before coming to university and while you are here. We can offer advice on living costs and budgeting, as well as on awards, allowances and loans.

Assessment

Students are assessed through examinations, coursework and a project.

Professional recognition

This programme is accredited by the British Computer Society (BCS). On successful graduation from this degree, the student will have fulfilled the academic requirement for registration as a Chartered IT Professional (CITP) and partially fulfilled the education requirement for registration as a Chartered Engineer (CEng) or Chartered Scientist (CSci). For a full Chartered status there are additional requirements, including work experience. The programme also has accreditation from the European Quality Assurance Network for Informatics Education (EQANIE).

Career options

Graduates from this programme can pursue careers as data scientists, database designers and administrators, consultants, senior team members, programmers, analysts.

Find out how to apply here - http://www2.gre.ac.uk/study/apply

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This programme is aimed at students who require both academic and technical training in advanced information technology. It is designed to meet the needs of practising IT professionals and to foster an appreciation of the theoretical foundations and academic framework for such personnel. Read more
This programme is aimed at students who require both academic and technical training in advanced information technology. It is designed to meet the needs of practising IT professionals and to foster an appreciation of the theoretical foundations and academic framework for such personnel. It also provides an entry point to the IT industry for graduates by supplying high-level technical training to supplement the academic content.

The programme is of particular interest to those who wish to develop enterprise-level database systems. It is supported by Oracle technology, which is widely used in industry for a diverse range of information needs. Those working in the IT industry, as well as anyone seeking a higher academic qualification in database technology and information systems, gain practical skills in Oracle technology (which are in short supply) and are able to develop database systems using the latest technology. Students also acquire the project management skills necessary for IT consultancy and strategic decision making.

Through our short course centre opportunity may also be provided to study for the following professional qualifications: Microsoft Technology Associate Exams; Certified Professional Java SE Programmer; Java Certified Associate; Oracle Certified Associate (OCA).

Visit the website http://www2.gre.ac.uk/study/courses/pg/inftec/esdw

Computing - Information Technology

The School of Computing and Mathematical Sciences is an extremely successful part of the university and is recognised both nationally and internationally for its cutting edge research and its innovative approach to curriculum development.

Our up-to-date, relevant and exciting programs are designed in close collaboration with industry to provide the skills that employers really want. Our research record is outstanding, focusing on practical and important real-life problems.

What you'll study

Full time
- Year 1:
Students are required to study the following compulsory courses.

PG Project (CIS) (60 credits)
Data Warehousing (15 credits)
System Administration and Security (15 credits)
Database Architectures and Administration (15 credits)
Database Tools (15 credits)
Enterprise Systems Integration (15 credits)
User Centred Web Engineering (15 credits)
Essential Professional and Academic Skills for Masters Students
English Language Support Course (for Postgraduate Students in the School of Computing and Mathematical Sciences)

Students are required to choose 15 credits from this list of options.

Mobile Application Development (15 credits)
Enterprise Software Engineering Development (15 credits)
Big Data (15 credits)

Students are required to choose 15 credits from this list of options.

System Modelling (15 credits)
Programming Enterprise Components (15 credits)
Network Architectures and Services (15 credits)
Multi-structured Data and NoSQL Technology (15 credits)

Part time
- Year 1:
Students are required to study the following compulsory courses.

Data Warehousing (15 credits)
System Administration and Security (15 credits)
Database Architectures and Administration (15 credits)
Database Tools (15 credits)
Essential Professional and Academic Skills for Masters Students
English Language Support Course (for Postgraduate Students in the School of Computing and Mathematical Sciences)

- Year 2:
Students are required to study the following compulsory courses.

PG Project (CIS) (60 credits)
Enterprise Systems Integration (15 credits)
User Centred Web Engineering (15 credits)

Students are required to choose 15 credits from this list of options.

Mobile Application Development (15 credits)
Enterprise Software Engineering Development (15 credits)
Big Data (15 credits)

Students are required to choose 15 credits from this list of options.

System Modelling (15 credits)
Programming Enterprise Components (15 credits)
Network Architectures and Services (15 credits)
Multi-structured Data and NoSQL Technology (15 credits)

Fees and finance

Your time at university should be enjoyable and rewarding, and it is important that it is not spoilt by unnecessary financial worries. We recommend that you spend time planning your finances, both before coming to university and while you are here. We can offer advice on living costs and budgeting, as well as on awards, allowances and loans.

Assessment

Students are assessed through examinations, coursework and a project.

Professional recognition

This programme is accredited by the British Computer Society (BCS) and can lead to full exemption from the BCS Postgraduate Diploma and Postgraduate Diploma Project. Additionally, this qualification gives partial chartered engineer (CEng) status and can be combined with a partial CEng from an accredited BSc programme to give full CEng status. The programme also has accreditation from the European Quality Assurance Network for Informatics Education (EQANIE).

Career options

Graduates from this programme can pursue careers as database administrators, IT consultants, systems and network administrators or Oracle developers. Opportunities exist to develop a career working as independent consultants or within teams in diverse areas such as banking IT support systems, networking, business and IT, research, teaching and training.

Find out about the teaching and learning outcomes here - http://www2.gre.ac.uk/?a=643967

Find out how to apply here - http://www2.gre.ac.uk/study/apply

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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 MSc in Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science- http://www.gold.ac.uk/pg/msc-data-science/. Read more
The MSc in Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science- http://www.gold.ac.uk/pg/msc-data-science/

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

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

The MSc in Data Science provides you with these skills.

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

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

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

The programme includes:

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

Contact the department

If you have specific questions about the degree, contact the Programme Director, Dr Daniel Stamate.

Modules & Structure

You will study the following:
Data Programming- 15 credits
Data Science Research- 15 credits

Skills & Careers

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

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

This could lead to a variety of potential jobs including:

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

Funding

The Department of Computing offers a number of scholarships for students with remarkably good applications. The scholarships will be a one-off payment of £2,000. You don't need to submit a separate application to be considered for one of these awards. You can find out more from the department.

Funding

Please visit http://www.gold.ac.uk/pg/fees-funding/ for details.

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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.

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.

Read less
Gartner defines Big data as high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. Read more
Gartner defines Big data as high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. A recent IDC forecast shows that the market for Big Data technology and services will grow at a 26.4% compound annual growth rate through 2018, or about six times the growth rate of the overall information technology market.

Saint Mary’s new Master of Science in Computing & Data Analytics (MSc CDA) is a graduate-level, 16-month professional program designed to meet the complex challenges associated with Big Data. It combines two essential aspects of computing and data analytics:

- Software design, development, customization, and management
- Analytics and Business intelligence: the acquisition, storage, management, and analysis of huge amounts of data to improve efficiency, innovation, and decision making

The primary focus of the MSc CDA program is to develop highly qualified computing and data analytics professionals who will drive innovation and organizational success. MSc CDA prepares students for rewarding and lucrative careers in the data science industry through experiential learning opportunities and industry interaction.

Program Structure

In the first two semesters of the MSc CDA program, students are introduced to big data challenges and solutions through eight foundation courses:
- Software Development in Business Environment
- Statistics and its Applications in Business
- Human Computer Interaction
- Managing and Programming Databases
- Business Intelligence
- Managing Information Technology and Systems
- Data Mining
- Web, Mobile, and Cloud Application Development


The second half of the program features three applied learning choices:

- Applied Master Project - System and Functional Analysis / Implementation and Analysis of Results
- Internship
- Research Thesis


Visit smu.ca/academics/msccda-courses.html for course outlines

Benefits of the MSc CDA

- Develop in-demand skills and knowledge that lead to exceptional career opportunities
- Study with award winning instructors from Saint Mary’s Faculty of Science and the Sobey School of Business
- Interact with industry professionals through core courses, paid internships, sponsored projects, industry workshops, expert guest speakers, hackathons, and special events
- MSc CDA studnets create a rich portfolio of apps and software solutions from a wide variety of in-demand platforms (Java/J2EE, C#/.Net, JavaScript/jQuery/jQuery Mobile/node.js, HTML5, PHP, iOS, Android, IBM Bluemix, Azure, SAS, Cognos, SQL/MySQL, NoSQL/Mongo DB, R, Python, IBM Watson)

Why Saint Mary's University

Saint Mary’s approach to learning will uniquely nurture the promise you possess. With one-on-one access to award winning professors, who care about your academic performance and your future, you will have the kind of personal support that makes a difference. Saint Mary’s is located in the historic city of Halifax, the bustling economic and cultural centre of Nova Scotia on Canada's east coast.

Halifax is Atlantic Canada’s Innovation Hub - a leader in the information technology sector with a growing concentration of companies and research organizations focused on analytics innovation.

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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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Distributed and networked computation is now the paradigm that underpins the software-enabled systems that are proliferating in the modern world, with huge impact in the economy and society, from the sensor and actuator networks that are now connecting cities, to cyberphysical systems, to patient-centred healthcare, to disaster-recovery systems. Read more
Distributed and networked computation is now the paradigm that underpins the software-enabled systems that are proliferating in the modern world, with huge impact in the economy and society, from the sensor and actuator networks that are now connecting cities, to cyberphysical systems, to patient-centred healthcare, to disaster-recovery systems.

This new Masters course will educate and train you in the fundamental principles, methods and techniques required for developing such systems. Given the number of elective modules offered, you will be able to acquire further skills in one or more of Cloud Computing, Data Analytics and Information Security.

Facilities include a laboratory where you can experiment with physical devices that can be interconnected in a network, and a cluster facility configured to run the Hadoop MapReduce stack.

A Year in Industry option is also available for this course.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/msc-distributed-and-networked-systems.aspx

Why choose this course?

This course will develop a highly analytical approach to problem solving and a strong background in distributed and networked systems, fault-tolerance and data replication techniques, distributed coordination and time-synchronisation techniques (leader-election, consensus, and clock synchronisation), data communication protocols and software stacks for wireless, sensor, and ad hoc networking technologies in virtualisation, and cloud computing technologies.

The course develops an advanced understanding of principles of failure detection and monitoring, principles of scalable storage, and in particular NoSQL technology.

Students will acquire the ability to:
- apply well-founded principles to building reliable and scalable distributed systems
- analyse complex distributed systems in terms of their performance, reliability, and correctness
- design and implement middleware services for reliable communication in unreliable networks
- work with state-of-the-art wireless, sensor, and ad hoc networking technologies
- design and implement reliable data communication and storage solutions for wireless, sensor, and ad hoc networks
- detect sources of vulnerability in networks of connected devices and deploy the appropriate countermeasures to information security threats.
- enforce privacy in “smart” environments
- work with open source and cloud tools for scalable data storage (DynamoDB) and coordination (Zookeeper)
- work with modern network management technologies (Software-Defined Networking) and standards (OpenFlow)
- design custom-built application-driven networking topologies using OpenFlow, and other modern tools
- work with relational databases (SQL), non-relational databases (MongoDb), as well as with Hadoop/Pig scripting and other big data manipulation techniques.

Department research and industry highlights

Royal Holloway is recognised for its research excellence in Machine Learning, Information Security, and Global Ubiquitous Computing.
We work closely with companies such as Centrica (British Gas, Hive), Cognizant, Orange Labs (UK), the UK Cards Association, Transport for London and ITSO.
We host a Smart Card Centre and we are a GCHQ Academic Centre of Excellence in Cyber Security Research (ACE-CSR).

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. If you are in the Year-in-Industry pathway, you then take an industrial placement, after which you come back for your project/dissertation (12 weeks).

Core course units are:
Interconnected Devices
Advanced Distributed Systems
Wireless, Sensor and Actuator Networks
Individual Project

Elective course units are:

Computation with Data
Databases
Introduction to Information Security
Data Visualisation and Exploratory Analysis
Programming for Data Analysis
Semantic Web
Multi-agent Systems
Advanced Data Communications
Machine Learning
Concurrent and Parallel Programming
Large-Scale Data Storage and Programming
Data Analysis
On-line Machine Learning
Smart Cards, RFIDs and Embedded Systems Security
Network Security
Computer Security
Security Technologies
Security Testing
Software Security
Introduction to Cryptography

Assessment

Assessment is carried out by a variety of methods including coursework, practical projects and a dissertation.

Employability & career opportunities

Our graduates are highly employable and, in recent years, have entered many different [department]-related areas, including This taught masters course equips postgraduate students with the subject knowledge and expertise required to pursue a successful career, or provides a solid foundation for continued PhD studies.

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Have you ever wanted to ‘Mung’ data? Apply Machine Learning techniques? Search for hidden patterns? Be part of Big Data?. This course is your opportunity to specialize as a Data Scientist, one of the most in demand roles across all sectors including health, retail, and energy. Read more
Have you ever wanted to ‘Mung’ data? Apply Machine Learning techniques? Search for hidden patterns? Be part of Big Data?

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

Key benefits

• We welcome applications from students who may not have formal/traditional entry criteria but who have relevant experience or the ability to pursue the course successfully.

• The Accreditation of Prior Learning (APL) process could help you to make your work and life experience count. The APL process can be used for entry onto courses or to give you exemptions from parts of your course.

• Two forms of APL may be used for entry: the Accreditation of Prior Certificated Learning (APCL) or the Accreditation of Prior Experiential Learning (APEL).

Visit the website: http://www.salford.ac.uk/pgt-courses/msc-data-science

Course detail

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

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

Suitable For

Students who want to become trained professionals in:

• Data Science and Analysis Consultancy
• Implementing and designing Big Data platforms ie Data Warehouses, Hadoop, NoSQL databases
• Modelling and Visualisation of data

Format

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

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

Modules

• Principles of Data Science
• Advanced Databases
• Applied Statistics and Data Mining
• Big Data Tools and Techniques

Assessment

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

Career Prospects

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

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

How to apply: http://www.salford.ac.uk/study/postgraduate/applying

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The International Master's program in Data Analytics enables students to take up challenging research in the interdisciplinary field with major focus on data analytics. Read more

Course description

The International Master's program in Data Analytics enables students to take up challenging research in the interdisciplinary field with major focus on data analytics.
During the program you will learn deeply about bleeding edge models and methods in machine learning as well as about turning ideas into code and thus design, implement and run your own experiments. Training on a largescale compute cluster and big data applications is part of the program, too.
During your study project you will learn how to write research proposals, how to manage research projects as a team and how to present results to a critical.

The Data Analytics program combines complex mathematical and statistical models and methods from machine learning with problems from an application domain using tools and techniques for processing Big Data. The programs is structured in a way that it builds on the core data modeling and data analytics knowledge with application on the large volume of real-world data using modern big data technologies such as the Apache Hadoop, Apache Spark, NoSQL etc.

Core Modules

* Machine Learning
* Advanced Machine Learning
* Modern Optimization Techniques
* Programming machine Learning
* Big Data Analytics
* Distributed Data Analytics
* Planning and Optimal Control

Application and Admission

The program starts at University of Hildesheim in the beginning of October every year. For details on how to apply please visit our website http://www.ismll.uni-hildesheim.de/da

FAQs

The list of frequently asked questions can be found here http://www.ismll.uni-hildesheim.de/da/faq_en.html. We recommend you to go through these questions. If you didn't find your answer, please feel free to contact us.

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This course will allow you to select leading classes that span the breadth of both computer and information sciences, including theoretical computer science, human-computer interaction, information sciences, software engineering, machine learning and big data. Read more
This course will allow you to select leading classes that span the breadth of both computer and information sciences, including theoretical computer science, human-computer interaction, information sciences, software engineering, machine learning and big data.

You’ll study

You'll gain an understanding of the new challenges posed by the advent of the big data revolution, particularly in relation to its modelling, storage, and access. You'll also come to understand the key algorithms and techniques embodied within data analytics solutions, and be exposed to a number of different big data technologies and techniques, seeing how they can achieve efficiency and scalability, while also addressing design trade-offs and their impacts.

You'll learn key technologies that are at the heart of big data analytics such as NoSQL databases and Hadoop and the Map-Reduce programming paradigm. You will also be equipped with a sound understanding of the principles of machine learning and a range of popular approaches, along with the knowledge of how and when to apply these.

You will also have the opportunity to implement and experiment with these machine learning algorithms using the most popular languages such as R and Python, and explore their applications to areas as diverse as analysing activity-related data captured using a smartphone to financial time-series prediction.

Individual project/dissertation

You’ll take on an individual research project on an approved topic related to your selected pathway. You’ll pursue a specific interest in further depth, giving scope for original thought, research and technical presentation of complex ideas.

Course content

Compulsory classes
-Legal, ethical and professional issues for the information society
-Distributed Information Systems
-Big Data Technologies
-Machine Learning for Data Analytics
-Research project

Elective classes - Choose two from the following:
-Advanced Topics in Software Engineering
-Mobile Software Applications
-Evolutionary Computing for Finance

Teaching methods include lectures, tutorials and practical laboratories. Dissertation is by supervision.

You’ll also have the opportunity to meet industry employers and participate in recruitment events.

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