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Masters Degrees (Data Analytics)

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

Why this course?

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.

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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In our social media and mobile-driven world, data and digital marketing have never been more vital. Enroll in our . Digital Marketing and Data Analytics online master's program. Read more

In our social media and mobile-driven world, data and digital marketing have never been more vital. Enroll in our Digital Marketing and Data Analytics online master's program to gain critical, in-demand skills and advance your marketing career. With courses in digital campaigns, branding, web and predictive analytics, social and mobile marketing, customer segmentation, and more, graduates of this program develop the digital and analytic skills you need to compete in today's insight­-driven market.

In this program, you will learn how to:

  • Design mobile-friendly marketing programs focused on user experience
  • Implement digital storytelling and content marketing strategies that connect consumers with brands
  • Use web and social media analytic tools to evaluate online interactions to generate consumer leads
  • Incorporate best practices for digital campaign testing and measurement

The 32-credit program can be completed in one year for students choosing to take the accelerated course schedule, or in a 16-month or longer period. The program is designed to be flexible, meeting the needs of working professionals.

Ready to advance your career in digital marketing and analytics? Apply to our online graduate program in Digital Marketing and Data Analytics today.

The MA in Digital Marketing and Data Analytics can also be completed as individual certificate programs. Student have the opportunity to develop critical skills through our 16-credit graduate certificates in Digital Marketing or Data Analytics.

Program Details

Our expertly designed online curriculum empowers working professionals to advance their careers in the areas of digital marketing and data analytics. According to the U.S. Bureau of Labor Statistics, the demand for advertising and marketing managers will grow by 9% over a 10-year span. With a balanced curriculum of digital-centric marketing and omni-channel customer analytics courses, graduates of the program develop digital and analytic skills that are necessary to compete in today's dynamic insight-driven marketing environment. 

You can complete our 32 credit program entirely online. The program curriculum is made up of four classes (16 credits) for Digital Marketing and four classes (16 credits) for Data Analytics. The online environment provides the flexibility to meet the needs of busy working professionals. Students can choose to take between 1-3 classes a semester and can complete the program in as little as 1-year with our accelerated option.

The program allows students who are eager to gain critical, in-demand digital marketing or data analytics skills to choose one of our 16 credit certificate options. Upon completion of a certificate, students have the option to apply to continue and complete the full degree program. The certificate program is made up of the 4 Digital Marketing or Data Analytics courses.

Learning Outcomes

The student learning outcomes of the Digital Marketing and Data Analytics program balance the priorities of both digital marketing and data analytics. Students will be able to:

Digital Marketing

  • Develop targeted, customer-centric digital marketing campaigns across a range of digital interfaces
  • Design marketing programs that account for the unique user experience needs of mobile consumers
  • Implement digital storytelling and content marketing strategies that connect consumers with brands across all major social media platforms
  • Use their knowledge of digital consumer behaviors and trends to design marketing programs that motivate consumers to engage and remain loyal to a brand

Data Analytics

  • Develop consumer personas and segments that provide the framework to deploy targeted and personalized marketing treatments
  • Build predictive models that forecast individual consumer behaviors and enable proactive marketing communications
  • Use web and social media analytic tools to evaluate online interactions and identify new opportunities to generate consumer leads and build even stronger customer relationships
  • Incorporate best practice digital campaign testing and measurement approaches that accurately assess the ROI of marketing investments

Emerson Advantage

Drawing upon Emerson's longstanding expertise in strategic communication, the Department of Marketing Communication has created a unique online master's program that equips working professionals with the cross-functional skills required to be successful in today's insight-driven marketing environment.

Our online learning and collaboration platform offers a student-centric learning experience. Enrollment in each course is capped at 20 to maintain a low student-to-faculty ratio. Smaller class sizes promote interactive learning and stimulating discussions with peers and faculty.

Students in this program are exposed to industry leading analytics software such as SAS Studio, SAS Enterprise Miner, Google Analytics, and social analytics platforms to acquire the skills to turn data into valuable insights that support better decision-making across myriad business applications.



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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. Read more
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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Health Data Analytics is the activity of extracting insights from health data, either to shape national policy, manage local organisations or inform the care of an individual. Read more

Health Data Analytics is the activity of extracting insights from health data, either to shape national policy, manage local organisations or inform the care of an individual. As more and more data becomes available electronically, the demand for skilled and trained individuals to take advantage of it becomes increasingly urgent.

About this degree

Students on the Health Data Analytics programme will learn about mathematical and statistical approaches to understanding health data, including operational research, machine learning and health economics. They will learn the fundamentals of how health data is collected, represented, stored and processed as well as how to analyse it effectively and how best to present analyses to have an impact on decisions.

Students undertake modules to the value of 180 credits.

The programme consists of three core modules (45 credits), five optional modules (75 credits) and a research project (60 credits).

A Postgraduate Diploma (120 credits, flexible study 2-5 years) is offered.

A Postgraduate Certificate (60 credits, flexible study over a period of two years) is offered.

Core modules

  • Principles of Health Data Analytics
  • Research Methods in Healthcare
  • Statistical Methods for Health Data Analytics

Optional modules

Students choose five of the following:

  • Key Principles of Health Economics
  • Public Health Data Science
  • Learning Health Systems
  • Information Law & Governance in Clinical Practice
  • Economic Evaluation of Health Care
  • Essentials of Informatics for Healthcare Systems
  • Machine Learning in Health Care
  • Clinical Decision Support Systems
  • Patient Safety and Clinical Risk

Please note that the optional modules listed here may be subject to change.

Dissertation/report

All MSc students undertake an independent research project, normally based at their place of work, which culminates in a piece of work written in the style of a journal article.

Teaching and learning

The programme is taught by 'blended learning', and therefore includes interactive online teaching and face-to-face lectures, seminars and workshops including substantial use of examples of real clinical systems. Assessment is through examination, critical evaluations, technical tasks, coursework and project reports, compulsory programming and database assignments, and the dissertation.

Further information on modules and degree structure is available on the department website: Health Data Analytics MSc

Careers

Health data analysts are employed by NHS England in a variety of roles, notably within NHS Improvement, assessing policy proposals and evaluating the economic or financial suitability of initatives. They are employed in acute trusts and in public health, mental health and other community-focused organisations to assist in the planning of services and the assessment of demand and to identify improvements in the organisation and management of services. Consultancy organisations providing services to the health sector also employ analysts as do data and IT organisations.

Employability

Our graduates will be skilled in the use of mathematical and statistical techniques for the manipulation and analysis of data. They will be familiar with state-of-the-art statistical packages but also have detailed practical experience of working with health data and the specific challenges and responsibilities that it entails. They will understand the processes by which data is collected and have insights into how that impacts its significance. These experiences will equip them to work in the NHS and also in a range of commercial and other organisations dealing with healthcare data.

Why study this degree at UCL?

Health data analysts are employed in interesting and challenging roles in healthcare organisations, government agencies and commercial organisations, including IT suppliers, consultancy organisations and pharmaceutical companies. The demand for skilled analysts is growing and graduates with the right skills and training can choose from a range of exciting and rewarding opportunities.

This programme has been designed in conjunction with the NHS to meet an identified shortage in skilled analysts. The aim is to provide a unique educational experience which not only prepares students for technical roles in analysis but equips them to take on senior roles in NHS organisations. The NHS needs not only more analytics staff, but also managers and decision makers who understand the importance of data and the role that analytics should be playing in shaping policy.

Our programme is delivered by a unique team including mathematicians, computer scientists and statisticians with expertise in the analysis of health data in a variety of forms and for a variety of purposes. The team are highly experienced not just in teaching and research but in the practical application of data analytics to the problems of health and healthcare organisations. We work closely with the NHS and with other commercial organisations to ensure our work is relevant and up-to-date.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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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. Read more

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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The Energy Systems and Data Analytics MSc provides an academically leading and industrially relevant study of energy systems through the lens of data analytics. Read more

The Energy Systems and Data Analytics MSc provides an academically leading and industrially relevant study of energy systems through the lens of data analytics. Advanced analytics, fuelled by big data and massive computational power, has the potential to transform how energy systems are designed, operated and maintained. You will gain the skills and knowledge to unlock the transformative potential of big energy data, and understand how it can reshape the energy sector.

About this degree

You will gain a broad understanding of energy systems as a whole, covering supply and demand, the interconnectedness and dependencies between different sectors and a multi-vector multi-sector approach to analysis. You will learn about the theory and practice of data analysis and will gain practical experience of the challenges of working with different data sets relating to energy throughout the programme and modules. 

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

Core modules

  • Energy Systems
  • Energy Data Analytics
  • Statistics for Energy Analysis
  • Energy Analytics in the Built Environment
  • Energy and Transport Analytics

Optional modules

  • Spatial Analysis of Energy Data
  • Introduction to Systems Dynamics Modelling in the Built Environment
  • Econometrics for Energy and the Environment
  • Energy, Technology and Innovation
  • UK Energy and Environment Policy and Law
  • Smart Energy Systems: Theory, Practice and Implementation
  • Eco-innovation and Sustainable Entrepreneurship

The list of optional modules is correct for the 2018/19 academic year. Enrolment on modules is subject to availability.

Dissertation/report

All students undertake an independent research project whch culminates in a 10,000-word dissertation.

Teaching and learning

The programme is delivered through a combination of lectures, seminars, tutorials, problem-based learning and project work. Assessment is through a combination of methods including problem sets, individual assignments and coursework, group based design tasks with a report and presentation, unseen examinations and a dissertation.

Further information on modules and degree structure is available on the department website: Energy Systems and Data Analytics MSc

Careers

Graduates of the ESDA MSc will be ideally placed to gain employment as energy analysts/ data scientists in consultancies, utilities, innovative start-ups and government institutions which value expertise in energy systems and have a need for data literate analysts.

Employability

There is a strong emphasis placed on innovation throughout the programme. Based on our market research and the trends in the industry (which is increasingly driven by data) there will be a healthy demand for our graduates.

Students will also benefit from a skill set in data analytics that will be highly transferable and applicable across a range of industries and domains.

The programme has been developed with input from industry leaders. You will gain exposure to real life energy and sustainability challenges.

Why study this degree at UCL?

The MSc in Energy Systems and Data Analytics is the first programme in the UK to combine the study of energy systems with data science. The MSc is delivered by leading researchers in the UCL Energy Institute and UCL Institute for Sustainable Resources. You will benefit from their specific expertise, research communities and industry contacts (including guest lecturers drawn from the energy industry), as well as our multidisciplinary and cross-domain approach.

The UCL Energy Institute has consulted across industry to identify key skills gaps for the energy analysts that will be required by utilities, consultancies and small and medium enterprises. There is a growing need in industry for graduates who combine an understanding of energy systems with the skills and abilities to extract insights from data through the use of advanced analytics.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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Statistics is the study of the collection, analysis, interpretation, presentation and organisation of data. Read more

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. Read more

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

Course description

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

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

Core Modules

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

Application and Admission

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

FAQs

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

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About the course. Data Analytics MSc has been developed in collaboration with SAS; a world leader in data analytics. Due to this strong partnership you will gain substantial SAS software skills as part of your study. Read more

About the course

Data Analytics MSc has been developed in collaboration with SAS; a world leader in data analytics. Due to this strong partnership you will gain substantial SAS software skills as part of your study. This course is designed specifically to equip you with the skills and abilities to address the skills shortage in industry. 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. Upon graduating you will be prepared to undertake business intelligence and data mining roles within any target industry

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

• Statistics

• Fundamentals of Business Intelligence Systems

• Research Methods

• Data Warehouse Design and OLAP

Second semester

• Analytics Programming

• Business Intelligence Systems

Application and Development

• Big Data Analytics

• Data Mining

Third semester

• 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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Gain the skills and knowledge to truly capitalise on the potential of big data and analytics. Boost your ability to integrate and deploy data-driven solutions that help build competitive advantage. Read more

Gain the skills and knowledge to truly capitalise on the potential of big data and analytics. Boost your ability to integrate and deploy data-driven solutions that help build competitive advantage. Develop your confidence in the practical application of the latest big data analytics tools, and use our innovative learning environment to study online from anywhere in the world.

“The best part of online study with the University of Liverpool was the teamwork with people from around the world.”

George Bagropoulos (Greece) IT graduate

Unlock the power of big data to drive business strategy

This 100% online master’s programme gives you the opportunity to:

  • Acquire a practical understanding of big data analytics and how it can empower organisations to become more effective, efficient and competitive.
  • Advance your potential career potential by acquiring a comprehensive and demonstrable understanding of big data and analytics tools and techniques.
  • Get hands-on experience of big data management frameworks and the ecosystems that can be used to support advanced data analytics.
  • Equip yourself with the tools and methods used in data mining, including data pre-processing, to generate a systematic understanding of the end-to-end process.
  • Create data warehouses using data from multiple sources and use state-of-the-art data visualisation technology to ‘tell a story’.
  • Analyse and understand the practical challenges of integrating and deploying big data management systems.
  • Demonstrate your skills in big data analytics and data-driven decision making to current or future employers via an e-portfolio of IT artefacts.

Grow with one of the world’s leading universities

The University of Liverpool is ranked in the top 1% of universities worldwide1 and is a member of the prestigious Russell Group of research-led British universities.

The 2014 Research Excellence Framework rated 97% of the research produced by the University’s Department of Computer Science as world-leading or internationally excellent – among the highest ratings of computer science department in the UK.

The University has developed an innovative, cloud-based server platform to allow online IT students to develop practical skills in an environment that mirrors real-world IT workspaces.

Careers

Study a master’s programme that puts you at the forefront of new, in-demand technologies. Position yourself to move into senior data or analytics roles2 such as:

  • Director of Analytics
  • Director of Business Analytics
  • Manager of Business Analytics
  • Director of Business Intelligence
  • Analytics Manager
  • Senior Big Data Engineer

1 As listed in the International Handbook of Universities, published by the International Association of Universities (2014).

2 Career options may require additional experience, training or other factors beyond the successful completion of this degree programme.



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Developed to meet the demand for data science professionals, our postgraduate Data Analytics course enables you to effectively structure, analyse and gain insight from a wide range of complex data across different industries. Read more

Developed to meet the demand for data science professionals, our postgraduate Data Analytics course enables you to effectively structure, analyse and gain insight from a wide range of complex data across different industries.

Designed in close consultation with industry partners including the NHS Business Services Authority, Teradata, BT, SAS, the Pensions Regulator and local Brighton companies, your learning is informed by current business developments through case studies looking at real-world data sets, research questions and scenarios. You have the opportunity to collaborate on projects with our industry partners, and can also use your own data, project ideas and industry links.

Guest lecturers will share their knowledge and expertise with you, such as Tom Khabaza who is a founding chairman of the Society of Data Miners, author of 9 Laws of Data Mining and was involved in designing the course.

You will develop a skill set in specialist data analytics and associated software, quantitative methods and techniques, and business intelligence. Our staff are experts in their field and you have the chance to develop your knowledge in specialist areas where we have ongoing research and expertise, such as sequential forecasting, natural language processing and image processing.

Whether you are a recent graduate or an experienced professional wanting to gain data analysis skills, this course is available on a full or part-time basis to help you manage your studies around other commitments. 

Course structure

The course covers three main areas:

  • data management – structuring and manipulating data for analysis purposes
  • data interpretation – statistical analysis using advanced features of industry-standard software such as SAS, SPSS and R
  • project management – the business-specific and strategic aspects of analytics.

You will learn how to assess project viability, propose sound business cases and strategies for analysis, perform and oversee analysis and manage large data projects successfully as well as developing your critical appraisal and presenting techniques. 

Based at our Moulsecoomb campus, you will have access to computer and research labs equipped with specialist, sophisticated software including SAS, SPSS Statistics and SPSS Modeller. Affordable student licences for home use are also available. 

With a flexible timetable to suit full-time or part-time students and commuters, and lecturers available to support you in your module choices, there are different study routes available to you.

Syllabus

You will study five core modules. One of these involves a major project, potentially in collaboration with industry. You will also choose option modules, subject to availability, allowing you to focus on particular areas of interest.

Core modules

  • Data Management – provides an understanding of contemporary database management systems. Explores a methodology for database design and development, and develops skills in searching, reporting and analysing the data. Topics covered include database implementation and administration, data modelling and business intelligence.
  • Programming for Analytics – provides competencies in computer programming and algorithm design with emphasis on statistical programming and data analysis. The module covers both general issues of algorithm design and data structures and implementation issues in R and SAS.
  • Data Visualisation and Analysis – covers principles of data visualisation and specialised tools for data visualisation and analysis such as SAS Visual Analytics and Qlikview. The module also explores the mathematical and statistical theory behind data analysis.
  • Business Analytics Strategy and Practice – develops analytics-specific project planning concepts within this context, enabling students to design and manage analytics projects and present the business case to senior management.
  • Industry project – substantial, independent project undertaken with the supervision of a member of the teaching team. Projects are normally industry-based using real data sets.

Option modules*

  • Multivariate Analysis and Statistical Modelling – design statistical experiments, analyse multivariate data and apply classical and modern statistical modelling techniques. Enhances skills in the use of specialist software such as R, SPSS or SAS.
  • Data Mining and Knowledge Discovery in Data – find useful and relevant patterns, trends and anomalies in data sets, and summarise them in a form which may be used to support enterprise decisions – one of the great challenges of the information age. Emphasis is on the big, real-world picture rather than inside-the-box systems design engineering details. 
  • Stochastic Methods and Forecasting – an understanding of stochastic models and their applications in a business context. The module also covers forecasting methods with the emphasis on selecting the best forecasting method for a business problem and correct application of that method.
  • Risk Analysis and Retail Finance – introduction to the statistical methods used to estimate risk and reward in retail credit. The focus is on retail finance especially the provision of credit and lending services.
  • Medical Statistics – introduction to the methods originally designed for clinical trials and now being used in other contexts including sociology and marketing research. Topics include assessment of risk factors, comparing treatments and assessing survival data.

*Option modules are indicative and may change, depending on timetabling and staff availability.  

Employability

A wide variety of organisations draw upon data analytics specialists to help produce valuable information for decision-making, for example commodity price forecasting, customer intelligence, clinical trials, R&D and many other areas utilising large amounts of data.

Graduates are able to choose from a range of private, governmental and academic roles, depending on their personal interests. Some of our full-time students find a full-time job and switch to part-time study in the middle of the course.

Graduate destinations include:

  • government bodies such as the Pensions Regulator and local councils
  • transnational corporations such as Capgemini
  • local companies such as iCrossing.


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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 MSc covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and is an ideal preparation for roles with some of the best internet-related industries and 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 is an ideal preparation for roles with some of the best internet-related industries and areas requiring big data analytical skills.

About this degree

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 three core modules (45 credits), five optional modules (75 credits), and the research dissertation (60 credits).

Core modules

  • Complex Networks and Web (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Web Economics (15 credits)

Optional modules

Students must choose a minimum of 45 and a maximum of 75 credits of optional modules. Up to two electives (30 credits) may also be chosen instead of two of the optional modules.

  • Affective Computing and Human-Robot Interaction (15 credits)
  • Applied Machine Learning (15 credits)
  • Birkbeck College: Cloud Computing (15 credits)
  • Computer Graphics (15 credits)
  • Entrepreneurship: Theory and Practice (15 credits)
  • Graphical Models (15 credits)
  • Interaction Design (15 credits)
  • Machine Vision (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)
  • Statistical Natural Language Processing (15 credits)
  • Supervised Learning (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

A list of acceptable elective modules is available on the Departmental page.

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 after 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.

Recent career destinations for this degree

  • CEO (Chief Executive Officer), Hoxton Analytics
  • Software Engineer, China Mobile
  • Computer Science Lecturer, Singapore Polytechnic
  • Software Developer, Barclays
  • Software Engineer, UCL

Employability

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.

UCL received the highest percentage (96%) for quality of research in Computer Science and Informatics in the UK's most recent Research Excellence Framework (REF2014).

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 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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