Masters Degrees (Data Analytics)

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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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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Our MBA (Data Analytics) will cover everything you need to know to run a successful business. Topics include finance, communications and management, while the specialised data analytics modules introduce you to the art and sciences of using raw data for business analysis and intelligence. You’ll study these modules alongside professionals and students from related disciplines, providing you with a collaborative and supportive environment for networking.

More about this course

London Met’s MBA (Data Analytics) course includes the fundamentals of business administration, an exploration of data analysis and the chance to conduct your own specialised research.

So you can grow into a successful business leader, the core modules teach you principles of business administration such as accounting and finance, leadership, management, marketing and communications.

The specialised data analytics modules are Data Mining for Business Intelligence and Data Modelling and Online Analytical Processing (OLAP) techniques. Studying these analytical processing methods will further enhance your ability to make informed and strategic decisions within companies.

Our teaching staff and visiting academics, who you’ll meet both informally and formally through lectures and social events, are experts in areas including strategy, management, coaching and data analytics.

Throughout the MBA, you’ll collaborate with students from a variety of professions and disciplines, including specialist postgraduate data analytics students. Your specialised training will be supplemented by regular informal learning activities including the weekly student-led Business Breakfast; a monthly dinner; networking events; meetings with business leaders; entrepreneurs and consultants and lively charity fundraising events in the City.

We’ll provide regular coaching sessions to help improve your career potential, while you can also make use of our Careers and Employability Unit to help you find new roles in preparation for life after the MBA.

You’ll be assessed through individual and group work. This is likely to come in a variety of forms including reports, portfolios, presentations, videos, conferences and competitions, enabling you to develop the skills to master a multitude of situations in the world of data analytics.

Modular structure

Core modules:
-Accounting and Finance for Managers
-Leadership and Strategic Management
-People and Organisations: Principles and Practices in Global Contexts
-Marketing, Marketing Communications and Operations

Data Analytics modules:
-Data Mining for Business Intelligence
-Data Modelling and OLAP Techniques for Data Analytics

Research-focused modules:
-Management Learning and Research
-Business Research Project

After the course

Graduates of the MBA may continue in their existing careers or choose to explore new opportunities. Recent graduates of our business related degrees are employed by companies including Oxademy, ALDI, Schwab Versand Hanau, Sapa, UBM plc, Carillion, Hanson Hispania SA, Triometric and BNP Paribas. They work in management roles in the fields of international sales, area management, business development, clients services and customer service.

Roles particularly relevant to the field of data analytics management include data science manager, business intelligence manager and CRM database manager.

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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.

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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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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Secure a big future in big data

In this day and age, data is very easy to gather and store; it’s knowing what to do with it that presents an obstacle. Studies have shown that one in three business leaders do not know how to transform their data into meaningful intelligence, according to IBM ‘The Four V's of Big Data’ (http://www.ibmbigdatahub.com/tag/587). The online Master of Business Administration (MBA) with Data Analytics, is designed to help you unlock that ability, without having to set foot on campus.

This course is aimed at accomplished middle managers seeking more senior, strategic roles, helping to give them an edge over other business managers by providing contemporary data analytics expertise.

Flexible and engaging online learning

NTU is committed to offering highly relevant courses, tailored to fit around your lifestyle and career. The online MBA course provides a flexible and engaging way to learn.

Through the online modules, you will cover a breadth of management subjects, with exposure to lecturers, guest speakers and students from a host of industry sectors. You will also participate regularly in business simulations and case study projects, helping you understand the challenges of managing an organisation.

Find out more here: http://landing.online.ntu.ac.uk/mba-data-analytics?utm_source=I-%20FindAUniversity&utm_medium=Listing&utm_campaign=Basic

Course curriculum

Nottingham Business School’s online MBA is a modular course, completed over three years. The MBA course consists of 10 core modules worth ten credits each, a project module worth 40 credits, plus four additional modules in Data Analytics (total 40 credits).

Course modules:
• Responsible Leadership
• The Values-Led Organisation
• Global Marketing Management
• Operations Management
• Organisations and People Management
• Financial Management
• Business Information and Decision Making
• Strategic Change Management
• Business Research Project
• Professional and Leadership Development

Data Analytics modules:
• Statistical Approaches to Data Analysis
• Fundamentals of Big Data and its Infrastructure
• Practical Machine Learning Methods for Data Mining
• Deriving Business Value from Data Science

Career outlook

Throughout our online MBA with Data Analytics, you’ll gain an understanding of the entire life cycle of big data: capturing, organising, analysing, drawing conclusions and taking action to gain leverage or competitive advantage, giving you with a highly coveted set of skills that will be extremely attractive to prospective employers.

Graduates from our data analytics courses have found employment as senior managers in a variety of national and international organisations, along with more specific roles as data analysts, data engineers, data scientists, data architects and business intelligence analysts.

For more information visit http://landing.online.ntu.ac.uk/mba-data-analytics?utm_source=I-%20FindAUniversity&utm_medium=Listing&utm_campaign=Basic

Start your journey with NTU today

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About the course

Statistics is the study of the collection, analysis, interpretation, presentation and organisation of data. Statistical analysis and data analytics is listed as one of the highly desirable skills employers are looking for, and with data becoming an ever increasing part of modern life, the talent to extract information and value from complex data is scarce.

The new Statistics and Data Analytics MSc is designed to train the next generation of statisticians with a focus on the field of data analytics. Employers expect skills in both statistics and computing. This master’s programme will provide a unique and coherent blend of modern statistical methods together with the associated computational skills that are essential for handling large quantities of unstructured data. This programme offers training in modern statistical methodology, computational statistics and data analysis from a wide variety of fields, including financial and health sectors.

Aims

Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. The aim of the MSc Statistics and Data Analytics is to produce graduates that:

- Are equipped with a range of advanced statistical methods and the associated computational skills for handling large quantities of unstructured data
- Have developed a critical awareness of the underlying needs of industry and commerce through relevant case studies
- Are able to analyse real-world data and to communicate the output of sophisticated statistical models in order to inform decision making processes
- Have the necessary computational skills to build and analyse simple/appropriate solutions using statistical Big Data technologies

Course Content

Compulsory modules:

Quantitative Data Analysis
Research Methods and Case Studies
Computer Intensive Statistical Methods
Modern Regression and Classification
Data Visualisation
Big Data Analytics
Time Series Modelling
Network Models
Dissertation

Statistics with Data Analytics Dissertation
Towards the end of the Spring Term, students will choose a topic for an individual research project, which will lead to the preparation and submission of an MSc dissertation. The project supervisor will usually be a member of the Brunel Statistics or Financial Mathematics group. In some cases the project may be overseen by an external supervisor based in industry or another academic institution..

Teaching

You’ll be taught using a range of teaching methods, including lectures, computer labs and discussion groups. Lectures are supplemented by computer labs and seminars/exercise classes and small group discussions. The seminars will be useful for you to carry out numerical data analysis, raise questions arising from the lectures, exercise sheets, or self-studies in an interactive environment.

The first term provides a thorough grounding in core programming, statistical and data analysis skills. In addition to acquiring relevant statistical and computational methods, students are encouraged to engage with real commercial and/or industrial problems through a series of inspiring case studies delivered by guest speakers. Support for academic and personal growth is provided through a range of workshops covering topics such as data protection, critical thinking, presentation skills and technical writing skills.

You’ll also complete an individual student project supervised by a relevant academic on your chosen topic.

Assessment

The assessment of all learning outcomes is achieved by a balance of coursework and examinations. Assessments range from written reports/essays, group work, presentations through to conceptual/statistical modelling and programming exercises, according to the demands of particular modular blocks. Additionally, class tests are used to assess a range of knowledge, including a range of specific technical subjects.

Special Features

The Statistics Group is a growing, highly-research active group, with collaborations across industry and academia, including engineering and pharmaceutical companies, Cambridge University and Imperial College London

Brunel’s Mathematics department is a member of the London Graduate School in Mathematical Finance. This consortium of mathematical finance groups comprises Birkbeck College, Brunel University London, Imperial College London, King’s College London, London School of Economics and Political Science and University College London. 

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Data Analytics is an exciting field of rapid developments. Data is everywhere and continuing to grow massively, creating huge growth in demand for qualified experts to be able to extract the real benefit from the data.

The role of a data scientist is highly diverse overlapping many areas from computer science, to the fundamentals of mathematics, statistics, modelling and analytics while also requiring the right skills to be able to see the detail, solve the problem (having specified the problem!), and communicate effectively the findings to colleagues to empower them to make decisions.

The diversity of data analytics opens up many job opportunities from working in software companies, healthcare, banking, insurance, policing, tech companies to applying your knowledge to intelligent buildings and behaviour analytics of customers.

The programme provides a balanced route to learning through a blend of academic study and lab sessions, with a heavy focus on practical engagement with industry. In the first and second semesters, you will study 6 modules full-time which include opportunities for blended and collaborative learning. In the third semester you will undertake a significant industry based project.

COURSE DETAILS

Aim

The aim of the programme is to offer a multi-disciplinary education in data analytics that prepares graduates with key knowledge, skills and competencies necessary for employment in analytics and data science positions. In particular, the programme aims to provide students with:

• Comprehensive knowledge and understanding of the fundamental principles of statistics and computer science that underpin analytics.
• Advanced knowledge and practical skills in the theory and practice of analytics.
• The necessary skills, tools and techniques needed to embark on careers in data analytics and data science.
• Skills in a range of practices, processes, tools and methods applicable to analytics in commercial and research contexts.
• Timely exposure to, and practical experience in, a range of current software packages and emerging new applications of analytics.
• Opportunities for the development of practical skills in a commercial context.

Modules include:

• Data Analytics Fundamentals
• Databases and Programming Fundamentals
• Data Mining
• Machine Learning
• Frontiers in Data Analytics
• Analytics in Action
• Individual Industry Based Project

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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.

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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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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About the course

Businesses now frequently possess and want to exploit huge, high volume, varied dynamic data sets, known as big data. Analytics is a subset of what has become to be called Business Intelligence. This is a set of technologies and processes used to understand data and analyse business performance.

Data Analytics MSc, developed and run with SAS, has been specifically designed to equip you with the skills and abilities to address this shortage. On successful completion of the course you will have developed your analytic and technical knowledge, and enhanced your professional skills within a Business Intelligence context.

You will be equipped with the relevant skills for employment in any field of data science (such as business intelligence, data mining, SAS programming and database design) within any target industry, with the additional option to complete a placement year in industry to further enhance your employability.

There is a growing need for professionals who can combine both analytical and software techniques in appropriate ways to allow the processing of ‘big data’. Data Analytics MSc is designed to provide these analytics and processing skills embedded within a business intelligence context.

Reasons to study:

• Taught by SAS accredited teaching staff
you will be taught by experienced SAS accredited teaching staff providing you with expert knowledge and skills

• Developed to fill skills shortage
course content has been developed to enhance your employability and gain substantial knowledge and equipping you with the skills required in for the use of the SAS software as well as Hadoop Distributed File System (HDFS) in industry

• 50 years history of research and teaching in computing technology
benefit from our well established academic expertise and advance your skills in, and knowledge of, data analytics to business problems

• Industry placement opportunity
you can chose to undertake a year-long work placement gaining valuable experience and skills as well as networking opportunities to build your industry contacts

• Excellent graduate prospects
equipped with the relevant skills for business intelligence and data mining roles including SAS Programming, Database Design and Business Intelligence

Course Structure

Modules

First semester (September to January)

• Statistics
• Fundamentals of Business Intelligence Systems
• Analytics Programming
• Data Warehouse Design and OLAP

Second semester (February to May)

• Business Intelligence Systems Application and Development
• Big Data Analytics
• Data Mining
• Research Methods

Third semester (June to September)

• Individual project

Teaching and Assessment

Teaching will normally be delivered through formal lectures, informal seminars, tutorials, workshops, discussions and e-learning packages. Assessment will usually be carried out through a combination of individual and group work, presentations, reports, projects and exams.

The course is run in association with SAS, the leading independent vendor in the business intelligence industry, and you will gain substantial SAS software skills as part of your study.

First semester modules provide you with fundamental abilities in the use of statistics so that you can gain insights and practice of using business intelligence systems and analytics programming to exploit multidimensional data sets.

In the second semester you are exposed to a variety of business intelligence systems, including those that use big data and data mining techniques. A further module prepares students to undertake an individual research project. This project module allows you to undertake extensive research into an aspect of big data, and/or provides an opportunity to develop and demonstrate your analytical and processing abilities in response to a given practical problem.

Contact and learning hours

You will normally attend 3 hours of timetabled taught sessions each week for each module undertaken during term time, for full time study this would be 12 hours per week during term time. You are expected to undertake around 24 further hours of independent study and assignments as required per week.

Industry Association

The Data Analytics MSc was developed and is run in conjunction with SAS. SAS is the world's largest independent business analytics company. It provides an integrated set of software products and services to more than 45,000 customer sites in 118 countries. Across the globe, both the public and private sector use SAS software to assist in their efforts to compete and excel in a climate of unprecedented economic uncertainty and globalization.

To find out more

To learn more about this course and DMU, visit our website:
Postgraduate open days: http://www.dmu.ac.uk/study/postgraduate-study/open-evenings/postgraduate-open-days.aspx

Applying for a postgraduate course:
http://www.dmu.ac.uk/study/postgraduate-study/entry-criteria-and-how-to-apply/entry-criteria-and-how-to-apply.aspx

Funding for postgraduate students:
http://www.dmu.ac.uk/study/postgraduate-study/postgraduate-funding-2017-18/postgraduate-funding-2017-18.aspx

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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. 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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Develop a different set of skills with this new one year conversion masters course.

Do you enjoy problem solving? Are you looking for a demanding career with a salary to match? Southampton Solent University’s MSc Data Analytics Engineering programme teaches students to make sense of a world where every action and transaction we perform has some aspect of data attached to it. Data analysts use these data sets to make meaningful inferences that can support business decisions, governmental policy changes and system designs.

As a conversion course, this master’s degree is well suited to students from a wide range of academic backgrounds. The course will help you to develop sought-after skills within the technology and big data environment, fully preparing you for a range of careers after graduation.

‌•Students work on high-spec Alienware computers and benefit from access to device labs, usability suites and a comprehensive library.
‌•Projects are a major part of the course, encouraging students to think about the problem solving process from start to finish.
‌•Students learn how data is created, stored and analysed.
‌•The course hosts regular enrichment activities including industry guest lectures, code jams, and employability events.
‌•Students will have the chance to attend lectures given by the British Computer Society - the chartered institute for IT in the UK.
‌•Small group teaching allows for tailored support, helping students to shape the course to their own interests.
‌•The course comes to a close with students conducting their own research projects. This can be an excellent way to specialise knowledge towards desired careers, or act as a springboard for PhD study.

The industry

The UK’s IT industry is worth well over £58 billion annually. With employment of IT professionals expected to grow nearly twice as fast as the UK average between now and 2020 (e-skills UK), it looks like demand for well-qualified information technology graduates is set to remain strong.
This is echoed from reports and comments from industry:

“As a small software house, dealing with a group of international corporates, we have a regular need for graduates with strong software engineering and database skills. Given that we design systems that link to E-commerce, a good understanding of data analytics engineering is also key. Over the years we have found the pool of graduates with the required technical skills has seriously diminished, so the more technical graduates from Solent University are an important source for us.”
John Noden Zentive - Managing Director/Executive Director Technical Design

“There is absolutely no doubt that he demand for analytics talent is increasing rapidly. This is happening for two reasons, firstly many more job roles are requiring data skills and secondly there are many more specialist openings for people with data skills than they were in recent years.”
Tom Brown, The Information Lab

The programme

The one-year master’s level conversion course is designed to prepare students from a range of academic backgrounds for work in data analytics engineering. Students are also able to tailor the course to their own personal career ambitions through a research project. Many use this piece of work to springboard the start of their career or a further research study.

Topic covered include databases, data management, web technologies, analysis and computing fundamentals. Students will also study academic research methods, which will then inform their final research project.

Students are also supported to gain a range of transferable skills throughout the course. These include project management, critical thinking, organisation and presentation skills. The professional issues and practise unit helps prepare students for the workplace by looking at the wider computing industry and the contexts in which big data can be used most effectively.

Next steps

Think you might enjoy a career in data analysis? With opportunities to work on independent projects, learn advanced computing techniques and build a portfolio of industry relevant skills, Southampton Solent’s postgraduate data analytics engineering course is the ideal way to break into the world of big data. Apply today http://www.solent.ac.uk/courses/2016/postgraduate/data-analytics-engineering-msc/course-details.aspx#tab5

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Are you looking for a distance learning course that gives you the flexibility to combine your existing job, or other commitments, with a Masters-level qualification in the field of data analytics? This course combines core modules in information science with specialised modules in Database Modelling as well as Statistics and Business Intelligence.

Compared to the full-time on-campus version of this course, this Masters is taught via a flexible distance learning mode and it has a slightly extended duration of 16 months. This makes it very suitable for those who are already employed as information professionals, in addition to those looking to break into the sector for the first time.

All of Northumbria’s information science postgraduate courses are accredited by the Chartered Institute of Library and Information Professionals. This accreditation makes our courses stand out and enhances their credibility and currency among employers, and is also crucial for progressing to Chartership status once qualified.

Learn From The Best

Our teaching staff include cutting-edge researchers whose specialisms overlap with the content of this course, helping ensure that teaching is right up-to-date. Specialisms include big data, data mining, decision-making, digital literacy, information behaviour, information retrieval systems, recommender systems, and the link between information science and cognitive psychology.

Our eminent academics have written books that regularly appear on reading lists for information science courses at universities all over the world. They also work as external examiners and reviewers of courses at other UK and non-UK universities.

Our course is delivered through the Northumbria iSchool, which is one of only six iSchools in the UK. A hallmark of an iSchool is an understanding that expertise in all forms of information is required for progress in science, business, education and culture. This expertise must cover the uses and users of information, the nature of information itself, as well as information technologies and their applications.

Information Science at Northumbria was established over 70 years ago and has developed in close collaboration with the profession. That dynamic working relationship has allowed us to not only reflect professional requirements, but also to be instrumental in understanding and shaping those requirements.

Teaching And Assessment

Our teaching is linked to what you want to learn and also to what you need to learn in order to achieve greater success in information science. Our long established relationship with employers ensures that you receive the most relevant and up-to-date knowledge to bring innovation, relevance, ethical sensitivity and currency to all you do. There is an emphasis on learning by doing; coursework will include projects, portfolios of work, reports and presentations as well as essays. All this helps you to make sense of the subject, getting a clear understanding of important concepts and theories.

While some assessments contribute to your final grade, there are other assessments that are provided purely to guide your progress and reinforce your learning. You can expect both your tutors and your peers to provide useful comments and feedback throughout the course.

Learning Environment

As a distance learner you will have full access to our eLearning Portal, ‘Blackboard Learn’, which includes lecture materials, web conferencing, study notes, discussion boards, virtual classrooms and communities. Blackboard Learn brings together all aspects of course management as well as assessment and feedback. Simpler technology is also effective and there’s still the option to reach tutors through a quick telephone call!

You will also have online access to Northumbria’s library, which has half a million electronic books that you can read whenever or wherever you need them. Our library was ranked #2 in the Times Higher Education Student Experience Survey for 2015 and, since 2010, it has been accredited by the UK Government for Customer Service Excellence.

The University has advanced search software and database tools, including NORA Power Search that allows you to use a single search box to get fast results from across a wide and reliable range of academic resources. The use of such software and tools is an important aspect of our information science courses.

Research-Rich Learning

In fast-moving fields like information science it’s particularly important for teaching to take account of the latest research. Northumbria is helping to push out the frontier of knowledge in a range of areas including:
-Digital consumers, behaviours and literacy
-Digital socio-technical design
-Digital libraries, archives and records

As a student, you will be heavily engaged in analysing recent insights from the field of information science. You will undertake a major individual study that will require you to evaluate relevant literature as well as to develop your ideas within the context of existing research. Your study will be tailored to your particular interests but the underlying theme will be the relationships between information, people and technology. Many of our students publish their own research and present at professional and academic conferences, before or soon after graduating.

Give Your Career An Edge

This course is accredited by the Chartered Institute of Library and Information Professionals. This reflects the relevance of the curriculum, which is informed by contact with employers and close professional links.

The topics and activities in the course have a strong emphasis on employability. For example you will develop skills in how to analyse, monitor and evaluate user behaviour. You will also learn how to evaluate and use a range of appropriate technologies for solving problems and supporting decision-making in organisations. Your knowledge and practical skills will help you take a lead on research-informed approaches that give organisations and professionals a valuable advantage.

Your Future

Data analytics is firmly in the spotlight due to transformations in information science and the emergence of big data. As we look to the future, which will be marked by ever greater capabilities for data processing, and a rising expectation that major decisions should be based on data-driven insights, data analytics will become increasingly valued and rewarded.

On graduation, you will be well placed to take advantage of this trend. Employers are looking for information professionals who can develop new insights through mastery of their subject and critical scholarship. With your Masters qualification, you will be equipped to make a difference, advance your practice and make well-balanced judgements. You could work for a wide range of employers in the field of data analytics or you could progress in a career that you have already started. Your Masters qualification can also form the basis for further postgraduate studies at a higher level.

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The Online MSc in Data Analytics covers 18 5-credit modules, two per semester over 9 semesters or 3 years, of which the Online Professional Diploma in Data Analytics covers the first 4. This first year is designed to introduce you to statistical and mathematical concepts in Data Analytics and Data Mining, and to get you started on programming with data. The second year is split between understanding the theory behind statistical and mathematical models for data via predictive analytics, and dealing with data sets at scale using Python and multivariate techniques. The final year covers some advanced methods: Monte Carlo, Bayesian Analysis, Time Series Data, and Complex Stochastic models. A provisional list of topics is as follows:

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