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Masters Degrees (Big Data Analytics)

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The MSc covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and makes them well equipped to proceed to become the ideal choice for the best employers in internet-related industries and the areas requiring big data analytical skills. Read more
The MSc covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and makes them well equipped to proceed to become the ideal choice for the best employers in internet-related industries and the areas requiring big data analytical skills.

Degree information

Students will gain a detailed knowledge and understanding of web-related technologies and big data analytics, ranging from information search and retrieval, natural language processing, data mining and knowledge acquisition, large-scale distributed data analytics and cloud computing to e-commerce and their business economic models and the latest concepts of social networks.

MSc students undertake modules to the value of 180 credits.

The programme consists of five core modules (75 credits), three option modules (45 credits) and the research dissertation (60 credits).

Core modules
-Information Retrieval and Data Mining
-Statistical Natural Language Processing
-Complex Networks and Web
-Web Economics

Optional modules - students can choose three of the following:
-Cloud Computing
-Computer Graphics
-Entrepreneurship: Theory and Practice
-Interaction Design
-Applied Machine Learning
-Machine Vision
-Supervised Learning
-Understanding Usability and Use
-Distributed Systems and Security

Dissertation/report
All students undertake an independent research project which culminates in a substantial dissertation.

Teaching and learning
The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by laboratory work with help from demonstrators. Student performance is assessed by unseen written examinations, coursework and the dissertation.

Careers

Graduates from UCL are keenly sought by the world's leading organisations, and many progress in their careers to secure senior and influential positions. Graduates of our Web Science and Big Data Analytics programmes are expected to develop careers in scientific research, the internet-based industry and other professional areas that require big data analytics skills.

Employability
The skill set obtained from our MSc makes our students the ideal choice for the best employers in internet-related industries and sectors requiring big data analytics. The MSc has a unique industry connection as almost all our lecturers have industry experience. Through long-term collaborations with big players in the field such as Google, Microsoft and BT, research is driven by the fundamental technical challenges faced by the industry. Throughout the degree, our students have the chance to interact with our industry collaborators and previous students, and have placement opportunities to address the specific technical problems faced by the industry.

Why study this degree at UCL?

UCL Computer Science is recognised as a world leader in teaching and research, and was one of the top-rated departments in the country according to the UK government's recent Research Excellence Framework.

Our Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries, from entertainment to finance.

We take an experimental approach to our subject, enjoy the challenge and opportunity of entrepreneurial partnerships and place a high value on our extensive range of industrial collaborations.

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

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

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

Why choose us?

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

Course breakdown

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

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

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

Enhancing your employability

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

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

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

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The MRes covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and makes them well equipped to proceed to become the ideal choice for the best employers in internet-related industries and the areas requiring big data analytical skills. Read more
The MRes covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and makes them well equipped to proceed to become the ideal choice for the best employers in internet-related industries and the areas requiring big data analytical skills.

Degree information

Students will gain a detailed knowledge and understanding of the fundamental principles and technological components of the World Wide Web, learning not only the latest web search and information retrieval technologies and their underlying computational and statistical methods, but also studying essential large-scale data analytics to extract insights and patterns from vast amounts of unstructured data.

Students undertake modules to the value of 180 credits.

The programme consists of two core modules (30 credits), four option modules (60 credits), and the research dissertation (90 credits).

Core modules
-Investigating Research
-Researcher Professional Development

Optional modules
-Complex Networks and Web
-Web Economics
-Information Retrieval and Data Mining
-Distributed Systems and Security
-Multimedia Systems
-Or an elective module from other Computer Science programmes

Dissertation/report
All students undertake an independent research project which culminates in a substantial dissertation.

Teaching and learning
The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by laboratory work with help from demonstrators. For the research project, each student is liaised with their academic or industrial supervisor to choose a study area of mutual interest. Student performance is assessed by unseen written examinations, coursework and the research dissertation.

Careers

Graduates from UCL are keenly sought by the world's leading organisations, and many progress in their careers to secure senior and influential positions. Graduates of our Web Science and Big Data Analytics programmes are expected to develop careers in scientific research, the internet-based industry and other professional areas that require big data analytics skills.

Employability
The skill set obtained from our MRes makes our students the ideal choice for the best employers in internet-related industries and sectors requiring big data analytics. The MRes has a unique industry connection as almost all our lecturers have industry experience. Through long-term collaborations with big players in the field such as Google, Microsoft and BT, their research is driven by the fundamental technical challenges faced by the industry. Throughout the degree, our students have the chance to interact with our industry collaborators and previous students, and have placement opportunities to address specific technical problems faced by the industry.

Why study this degree at UCL?

UCL Computer Science is recognised as a world leader in teaching and research, and was one of the top-rated departments in the country according to the UK government's recent Research Excellence Framework.

Our Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries, from entertainment to finance.

We take an experimental approach to our subject, enjoy the challenge and opportunity of entrepreneurial partnerships and place a high value on our extensive range of industrial collaborations.

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The Master of Science in Big Data Analytics for Business is a unique program that trains business professionals in the field of (online) marketing, finance, and operations. Read more

The Master of Science in Big Data Analytics for Business is a unique program that trains business professionals in the field of (online) marketing, finance, and operations.

Students are exposed to the leading-edge fundamentals in decision-making by extracting knowledge from Big Data, including social media data, customer web traffic data, Bloomberg’s financial data, and inventory process logs.

Students will learn to solve managerial problems by critically asking questions in the spirit of ‘What do we know?’ (Data driven) rather than ‘What do we think? (Gut feeling).

Program Advantages:

- Introduction of leading tools that convert data to knowledge

- Possibility to obtain business-relevant certificates

- Exposure to both academic and applied industry research

Career Opportunities:

- Digital/Web Analyst

- Customer Analyst

- Data Scientist

- Credit Risk Analyst

This program is under the process of being accredited with the Université Catholique de Lille as diplôme universitaire and with the Conférence des Grandes Ecoles.

Program

The Master of Science in Big Data Analytics for Business offers core modules in business, technology, and methodology as well as specialized modules in marketing, finance, and operations.

These modules will be covered over two semesters and the students will take their newfound knowledge and apply it in a professional environment during a 4 – 6 month internship.

Internship -

Students acquire real-life experience through a 4 – 6 month internship in France, or anywhere in the world.

The objective is to provide an opportunity where MSc in Big Data Analytics for Business students learn how to approach assignments and working relationships in a professional environment. They can apply their newfound knowledge in real world situations while receiving guidance and feedback from managers and colleagues.

New contacts made during their internships help create their professional networks.

French language classes -

French language lessons are mandatory for non-Francophone international students. Francophone students may choose German, Italian, Chinese, or Spanish.

Admission & Fees

The MSc in Big Data and Analytics for Business is for students with a bachelor’s degree with a quantitative component or business administration interested in a new and expanding field.

Admission requirements -

The program is open to candidates with a bachelor’s degree from a recognized university with good academic performance and a good command of English.

Native English speakers or students who have had two years of courses taught in English are exempt. A GMAT score is optional, not mandatory.

No prior knowledge of French is needed; however French language classes are mandatory for non-French speakers as part of the program.

Application process -

The application process is based on students’ online application available at https://application.ieseg.fr/ and review of the required documents.

Rolling admission is offered from October 2017.

Checklist requirements:

- Online application form

- Transcripts and diploma translated into English or French if necessary

- English proficiency test (IELTS 6.5 TOEFL IBT 85, TOEIC 800) if required

- CV / Resume

- Copy of passport

- 80€ application fee

Tuition 2017-2018:

- € 15,000 for domestic and international students

- International merit-based scholarships are available

Funding and scholarship-

IÉSEG has a merit-based International Scholarship Program with a tuition waiver of 15 to 50% per year. Selection is based on the applicant’s previous academic performance and overall application portfolio.

The scholarship application is automatic; students do not need to apply separately.

All international students are encouraged to check with Campus France and their own government to see if there are any scholarships available. For American students please check with Sallie Mae for private loan options.



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

What will I study?

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

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

How will I study?

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

How will I be assessed?

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

Who will be teaching me?

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

What are my career prospects?

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

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

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

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

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

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

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

You’ll study modules such as:

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

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This Postgraduate Certificate provides you with a practical understanding of big data analytics and emerging technologies. You will gain the essential skills and confidence required to apply knowledge and understanding of issues surrounding big data analytics in a range of contexts. Read more

This Postgraduate Certificate provides you with a practical understanding of big data analytics and emerging technologies. You will gain the essential skills and confidence required to apply knowledge and understanding of issues surrounding big data analytics in a range of contexts. There is the opportunity to develop a critical understanding of visualisation concepts and emerging technologies, as well as to develop and evaluate new or advanced bespoke solutions for making sense of big and/or complex data. The programme enables you concentrate on a specific practical area within computer science and is suitable whether you are a recent graduate or already working in the IT industry and looking to change career paths.

What will I study?

Gaining knowledge of big data applications and visualization, along with a critical understanding of new technologies, you will develop a systematic and critical understanding of the algorithms and programming techniques required for processing, storing, analysing, visualising and interpreting data.

How will I study?

The course is delivered through a combination of seminars and tutorials in evening classes. Sessions will frequently be highly interactive with a focus on the practical application of concepts and the use of case studies drawn from real life. You will apply your knowledge in a practical environment using the state of the art facilities in the Tech Hub. An emphasis on small group sizes ensures that you will have plenty of opportunities for individual discussions with your tutors. Typically, you will study for approximately nine hours a week.

How will I be assessed?

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

Who will be teaching me?

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



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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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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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Big data is the description used to encompass the huge amounts of data that is common to many businesses. It has been described as the next frontier for innovation, competition and productivity in business. Read more

Big data is the description used to encompass the huge amounts of data that is common to many businesses. It has been described as the next frontier for innovation, competition and productivity in business. It is essential for companies to embrace so that they can understand their customers better, develop new products and cut operational costs.

This course has been developed to create graduates who can become data scientists capable of working with the massive amounts of data now common to many businesses. It is aimed at people who want to move into this rapidly expanding and exciting area.

The modules on this course help you develop the core skills and expertise needed by the data scientist. The course can be split into three main areas, statistics, computing and management.

In the statistics section you study modules on data mining and data modelling. These modules cover the three main data areas, which are ensuring that data is reliable and of a high quality, searching the data to discover new information and presenting interpretations of that data to the end user.

The computing section covers areas related to data integration, massive datasets stored in the cloud, how data is stored and utilised within the distributed systems of an enterprise and how organisations can utilise data to change and improve business processes.

The management modules are focused on developing your core skills around professionalism and research. All of which are valuable skills during your university studies and in your career.

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

Key areas of study

Key areas of study include • data quality and analysis • technologies to store and mine data • professionalism and research

Professional recognition

This course includes the SAP Business Intelligence with SAP BW 7.3 and SAP BI 4.0 e-academy (UB130e). You also have the opportunity to sit the SAP certification exam and the SAS 9 base certification exam.

Sheffield Hallam is a member of the SAS Student Academy, the SAP Student Academy and founding member of the SAP University Alliance.

Course structure

Full time – September start – typically 12 or 18 months

Part time – September start – typically 36 months

Core modules

  • research skills and principles
  • industrial expertise
  • data integration
  • statistical modelling
  • data mining
  • handling data in the cloud
  • big data and distributed systems
  • social and economic aspects of the cloud
  • advanced statistical modelling
  • dissertation

Options

Choose one from :

  • organisational dynamics
  • social and economic aspects of the cloud

Assessment

  • essays
  • assignments
  • computer-based tests
  • practical projects
  • presentations
  • vivas

Employability

Many jobs for data scientists, data analysts and data mining analysts are available with salaries ranging from £35,000 to £80,000.

Jobs typically list the skills to be in areas such as statistical analysis and machine learning techniques, database and programming technologies, and expertise in statistical theory, which are all areas you cover on this course.

You also gain skills and knowledge in HaDoop, MapReduce, Java, SAS, MSQL which are some of the common technologies used in data scientist roles.



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1. Big Challenges being addressed by this programme – motivation. Globally, there is a reported shortage of data analytics talent, particularly of individuals with the required deep technical and analytical skills. Read more

About the Course

1. Big Challenges being addressed by this programme – motivation

• Globally, there is a reported shortage of data analytics talent, particularly of individuals with the required deep technical and analytical skills.
• Accenture, Gartner and McKinsey have all identified Data Analytics as one of the fastest growing employment areas in computing and one most likely to make an impact in the future.
• The Irish Government’s policy is for Ireland to become a leading country in Europe for big data and analytics, which would result in 21,000 potential new employment opportunities in Ireland alone.
• CNN has listed jobs in this area in their Top 10 best new jobs in America.

2. Programme objectives & purpose

This is an advanced programme that provides Computing graduates with advanced knowledge and skills in the emerging growth area of Data Analytics. It includes advanced topics such as Large-Scale Data Analytics, Information Retrieval, Advanced Topics in Machine Learning and Data Mining, Natural Language Processing, Data Visualisation and Web-Mining. It also includes foundational modules in topics such as Statistics, Regression Analysis and Programming for Data Analytics. Students on the programme further deepen their knowledge of Data Analytics by working on a project either in conjunction with a research group or with an industry partner.

Graduates will be excellently qualified to pursue careers in national and multinational industries in a wide range of areas. Our graduates currently work for companies as diverse as IBM, SAP, Cisco, Avaya, Google, Fujitsu and Merck Pharmaceuticals as well as many specialised companies and startups. Opportunities will be found in:
• Multinational companies, in Ireland and elsewhere, that provide services and solutions for analytics and big data or whose business depend on analytics and big data technologies;
• Innovative small to medium-sized companies and leading-edge start-ups who provide analytics solutions, services and products or use data analytics to develop competitive advantage
• Companies looking to extend their research and development units with highly trained data analytic specialists
• PhD-level research in NUI Galway, elsewhere in Ireland, or abroad

3. What’s special about CoEI/NUIG in this area:

• The MSc in Computer Science (Data Analytics) is being delivered by the Discipline of Information Technology in collaboration with the Insight Centre for Data Analytics (http://insight-centre.org) and with input from the School of Mathematics, Statistics and Applied Mathematics in NUI Galway
• The Discipline of Information Technology at NUI Galway has 25-year track record of education, academic research, and industry collaboration in the field of Computer Science
• The Insight centre at NUI Galway is Europe’s largest research centre for Data Analytics

4. Programme Structure – ECTS weights and split over semester; core/elective, etc.:

• 90ECTS programme
• one full year in duration, beginning September and finishing August
• comprises:
- Foundational taught modules (20 ECTS)
- Advanced taught modules (40 ECTS)
- Research/Industry Project (30 ECTS).

5. Programme Content – module names

Sample Foundational Modules:

• Tools and Techniques for Large Scale Data Analytics
• Programming for Data Analytics
• Machine Learning and Data Mining
• Modern Information Management
• Probability and Statistics
• Discrete Mathematics
• Applied Regression Models
• Digital Signal Processing

Sample Advanced Modules:

• Advanced Topics in Machine Learning and Information Retrieval
• Web Mining and Analytics
• Systems Modelling and Simulation
• Natural Language Processing
• Data Visualisation
• Linked Data Analytics
• Case Studies in Data Analytics
• Embedded Signal Analysis and Processing

6. Testimonials

Ms. Gofran Shukair, MSc, Research Engineer at ZenDesk, Ireland

After graduating with an MSc at NUI Galway, Gofran worked with Fujitsu’s Irish Research Lab as a research engineer before moving to a software engineering position at Zendesk, Ireland.

“The mix of technical and soft skills I gained through my Masters studies at NUI Galway is invaluable. I had the chance to work with great people and to apply my work on real world problems. With the data management and analysis skills I gained, I am currently pursuing my research in an international research project with one of the leading IT companies. I will be always thankful for studying at NUI Galway, a great historic place based in a culturally-rich vibrant city with an international mix of young and ambitious students that made me eager to learn and contribute back the moment I graduated.”

For further details

visit http://www.nuigalway.ie/courses/taught-postgraduate-courses/msc-in-computer-science-data-analytics.html

How to Apply:

Applications are made online via the Postgraduate Applications Centre (PAC) https://www.pac.ie
Please use the following PAC application code for your programme:

M.Sc. Computer Science – Data Analytics - PAC code GYE06

Scholarships :

Please visit our website for more information on scholarships: http://www.nuigalway.ie/engineering-informatics/internationalpostgraduatestudents/feesandscholarships/

Visit the M.Sc. Computer Science – Data Analytics page on the National University of Ireland, Galway web site for more details!

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

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

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

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

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

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

Programme modules

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

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

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

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

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

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

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

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

Summer:
- Consulting or Research Project

Assessment

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

Careers and further study

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

Why choose business and economics at Loughborough?

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

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

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

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

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

*Source: DLHE

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

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Our Big Data in Culture & Society MA recognises the growing importance of Big Data in contemporary society and addresses the theory and practice of Big Data from an arts and humanities perspective. . Read more

Our Big Data in Culture & Society MA recognises the growing importance of Big Data in contemporary society and addresses the theory and practice of Big Data from an arts and humanities perspective. 

What is Big Data? Beyond the unprecedentedly large data sets that can be analysed to reveal patterns, trends, and associations, it is increasingly about our everyday lives. In short, it is about how the data we generate is transforming social, cultural, political and economic processes as well as the generation of knowledge.

This course is likely to appeal to a broad range of students across the Arts and Humanities from Sociology to Political Science to English to Business and beyond. It will attract forward-thinking students interested in emerging trends who recognise that data scientists and analysts require collaborators with domain specialisation and critical insights.

  • Taught by scholars working at the leading edge of digital studies and Big Data.
  • Offers a lively mix of theory and practical work.
  • Equips students with skills that are highly attractive to employers in our digital age.
  • Provides a series of workshops with data scientists and analysts to learn collaborative practices and applications in social media and cultural analytics, mobile platforms, and data visualisation.
  • Is at the forefront of digital developments - Big Data is transforming society, politics, the economy and culture and impacting work
  • Offers innovative interdisciplinary methods of study crossing technological and cultural perspectives
  • Links Big Data to Culture, Law & Ethics, Geography, Public Health, and Social Life
  • Located in a highly ranked department - the Digital Humanities department was ranked first in the UK for research power (2014 Research Excellence Framework)

Description

This Big Data in Culture & Society MA offers you the opportunity to develop your knowledge and understanding of the role of Big Data in culture and society. It will enable you to analyse Big Data across social, political and economic areas. In addition to the required content we cover, you will have the opportunity to pursue your own academic interests through our optional modules and to undertake an internship and a group project module.

By bringing together domain knowledge and technical skills and approaching these from an Arts and Humanities perspective, the course will help you develop highly valued employment skills and expertise for careers in Big Data.

The course will provide you with:

  • Knowledge and understanding of the effects of Big Data on contemporary society.
  • Critical and theoretical approaches to the analysis of Big Data.
  • Knowledge of the historical antecedents of Big Data.
  • Understanding of the innovative methods for generating new knowledge through the use and analysis of Big Data.
  • Understanding of Big Data in relation to the broader study of digital culture, the digital humanities and traditional humanities disciplines.
  • Understanding of appropriate personal and professional conduct in the context of digital culture as an emerging discipline. 

Course purpose

The MA Big Data in Culture and Society offers students the opportunity to develop their knowledge and understanding of the role of Big Data in culture and society. It enables them to analyse Big Data across social, political and economic areas and provides them with a background for pursuing careers in Big Data by bringing together domain knowledge and technical skills. 

Course format and assessment

Teaching

If you are a full-time student, we will provide you with 120 to 180 hours of teaching through lectures and seminars across the year. We expect you to undertake around 1,674 hours of independent study.

If you are a part-time student, we’ll provide you with 90 hours of teaching through lectures and seminars in your first year, and 50 hours in your second. We’ll expect you to undertake 720 hours of independent study in your first year and 954 hours in your second.

Typically, one credit equates to 10 hours of work.

Assessment

We assess our modules entirely through coursework. This will comprise a mixture of essays, project work, and workshop reports, depending on the modules you choose.

Regulating body

King’s College London is regulated by the Higher Education Funding Council for England.

Career prospects

Our graduates will follow a broad range of career paths. The skills you develop are likely to be particularly transferable to work in social media management, analytics & website management, CRM management, digital advertising, metrics management, market research, marketing and across cultural industries.



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

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

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

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

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

What you’ll study

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

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

Semester 1

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

This semester is designed to provide you with the fundamental technical analytics knowledge from all three departments.
-Computer & Information Sciences courses will cover core techniques including machine learning and data mining as well as data visualisation and big data platforms
-Mathematics courses will ensure you gain strong computational skills while establishing a broad knowledge of statistical tools essential for analytics
-Management Science courses will build the foundations of business skills including problem structuring as well as decision analysis, in addition to providing essential practical skills

Semester 2

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

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

Work placement

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

Major projects

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

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

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

Facilities

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

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

Guest lectures

Every year, guest speakers attend our course, sharing their invaluable experiences. As part of the Data Analytics in Practice module, we host several presentations from external bodies.

Course content

Compulsory classes
-Big Data Fundamentals
-Big Data Tools & Techniques
-Data Analytics in R
-Business & Decision Modelling
-Optimisation for Analytics
-Data Analytics in Practice
-Dissertation in Data Analytics

Optional classes
Students are required to choose 40 credits worth of elective classes, and at least from two departments. All optional classes take place in Semester 2.

Learning & teaching

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

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

Assessment

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

Careers

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

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

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Embark on a career in a leading-edge field and master the exciting and challenging world of big data!. Big data techniques are revolutionising how organisations and industries acquire and analyse data, offering valuable insights into how people live, work, play, travel, shop and exercise. Read more

Embark on a career in a leading-edge field and master the exciting and challenging world of big data!

Big data techniques are revolutionising how organisations and industries acquire and analyse data, offering valuable insights into how people live, work, play, travel, shop and exercise. These insights are valuable to marketers, researchers, designers, city planners, app developers, educators and many more.

GCU's MSc in Big Data Technologies helps students build the fundamental knowledge and practical skills for success in this fast-growing field. You'll develop competence in a range of emerging technologies: big data, cloud computing and the internet of things. You'll learn from the experts; GCU is internationally recognised for the strength of its research in these exciting subjects, driving 21st century innovation.

With both full-time and part-time study available, the programme is ideal for someone with a background in computer science, software engineering, web technologies or computer engineering who wants to enhance or update their skills. Those with backgrounds in mathematics and electronics are also well suited.

The up-to-date curriculum keeps a career-focused approach, so you'll gain valuable skills you can immediately put to work in the industry.

  • Apply leading-edge tools and technologies from companies such as IBM, Microsoft and SAS
  • Explore industry-standard open-source development platforms such as Hadoop
  • Achieve industry recognition with SAS joint certification in the programme's Data Analytics module

Your expertise in big data will enable you to provide new insights into human behaviour and psychology, which can help us build stronger and happier societies across the globe. Your work could shape smart, sustainable cities; remove barriers to education; help people make healthier choices day-to-day; improve public health… and so much more. All meaningful ways of contributing to the common good.

What you will study

Full-time students complete six taught modules; three in trimester A and three in trimester B and an MSc dissertation project in trimester C. Part-time students complete six taught modules; three in Year 1, three in Year 2and an MSc project in Year 3.

Cloud Computing and Web Services

This module provides analytical and practical coverage of cloud computing and web services. It focuses on the technology, frameworks and associated standards: cloud models, cloud platforms and scalability. It also provides coverage of current web service technology and data transport representations, and integrated cloud and web service application development. Current examples from industry technology are used throughout.

Big Data Landscape

This module covers the process of managing Big Data throughout its lifecycle, from requirements through retirement. The lifecycle crosses different application systems, databases and storage media. Students will gain an understanding of the full Big Data value chain. They will be able to analyse the challenges and opportunities associated with the different stages that Big Data passes through.

Data Analytics

This module covers the basic concepts of statistics needed to understand the critical concepts of data mining, machine learning and predictive analytics used in the visualisation and analysis of data, particularly Big data. Students will gain an understanding of data preparation, the process models used in analytics, the algorithms and their requirements, the implementation of these algorithms using current technologies, and their applicability to different types of scenario. They will also gain advanced practical skills in the design, implementation and evaluation of analytical solutions to problems involving Big Data.

Big Data Platforms

This module covers the platforms that support data storage, processing and analytics in Big Data scenarios. It focuses on highly scalable platforms that provide operational capabilities for real-time, interactive processing and on platforms that provide analytical capabilities for retrospective, complex analysis. Students will gain an advanced understanding of the principles on which these platforms are based, and their strengths, weaknesses and applicability to different types of scenario. They will also gain advanced practical skills in the design and implementation of scalable Big Data platform solutions.

Internet of Things

This module provides fundamental and practical coverage of the set of converging technologies known as the Internet of Things (IoT). It focuses on representative IoT applications, technologies, frameworks and associated standards that support and underpin IoT applications, such as sensor networks, messaging protocols, security, data storage, analytics, services and human interaction. The module provides in-depth practical coverage of representative IoT implementation frameworks including cloud-based service delivery models.

IT Professional Issues and Project Methods

This module seeks to develop understanding and practical skills in advanced project methods which are inline with industry regulations, standards and practices and are applicable to complex IT projects. Study is undertaken in an integrated fashion to ensure that the professional frameworks within which such projects are developed, deployed and managed are fully understood.

Masters Dissertation

Students will investigate a topical or emerging theme in Cloud Computing or related technologies. The dissertation acts as a vehicle for extending the knowledge and understanding of the student and the technical community in some specialist technical area. It serves through its length, complexity and rigour as a suitable vehicle for extending students' range of personal, interpersonal and communication skills. In addition it serves to develop and extend a range of high-level thinking skills, including analysing and synthesising skills and affords the opportunity for the student to demonstrate initiative and creativity in a major piece of technical work.

Work placements

Students will be made aware of placement opportunities provided by our industry partners. Typically a placement would be undertaken after graduating from the MSc programme but opportunities can sometimes be made available for students to undertake their MSc Dissertation in partnership with a commercial company.

Assessment methods

The methods of assessment include class tests, coursework assignments, practical tests and technical reports. Practical implementation and evaluation form a significant part of the assessment for the taught modules and for the work of the MSc dissertation.

Graduate prospects

When you graduate, you'll be a competitive candidate for roles as a systems developer, architect or administrator in data and analytics. You'll find opportunities in a diverse range of industries: engineering, pharmaceuticals, finance, healthcare, retail, security, smart environments and more.



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