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Masters Degrees (Big Data Analyst)

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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Data Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017). Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Data Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

MSc in Data Science aims to equip students with a solid grounding in data science concepts and technologies for extracting information and constructing knowledge from data. Students of the MSc Data Science will study the computational principles, methods, and systems for a variety of real world applications that require mathematical foundations, programming skills, critical thinking, and ingenuity. Development of research skills will be an essential element of the Data Science programme so that students can bring a critical perspective to current data science discipline and apply this to future developments in a rapidly changing technological environment.

Key Features of the MSc Data Science

The MSc Data Science programme focuses on three core technical themes: data mining, machine learning, and visualisation. Data mining is fundamental to data science and the students will learn how to mine both structured data and unstructured data. Students will gain practical data mining experience and will gain a systematic understanding of the fundamental concepts of analysing complex and heterogeneous data. They will be able to manipulate large heterogeneous datasets, from storage to processing, be able to extract information from large datasets, gain experience of data mining algorithms and techniques, and be able to apply them in real world applications. Machine learning has proven to be an effective and exciting technology for data and it is of high value when it comes to employment. Students of the Data Science programme will learn the fundamentals of both conventional and state-of-the-art machine learning techniques, be able to apply the methods and techniques to synthesise solutions using machine learning, and will have the necessary practical skills to apply their understanding to big data problems. We will train students to explore a variety visualisation concepts and techniques for data analysis. Students will be able to apply important concepts in data visualisation, information visualisation, and visual analytics to support data process and knowledge discovery. The students of the Data Science programme also learn important mathematical concepts and methods required by a data scientist. A specifically designed module that is accessible to students with different background will cover the basics of algebra, optimisation techniques, statistics, and so on. More advanced mathematical concepts are integrated in individual modules where necessary.

The MSc Data Science programme delivers the practical components using a number of programming languages and software packages, such as Hadoop, Python, Matlab, C++, OpenGL, OpenCV, and Spark. Students will also be exposed to a range of closely related subject areas, including pattern recognition, high performance computing, GPU processing, computer vision, human computer interaction, and software validation and verification. The delivery of both core and optional modules leverage on the research strength and capacity in the department. The modules are delivered by lecturers who are actively engaged in world leading researches in this field. Students of the Data Science programme will benefit from state-of-the-art materials and contents, and will work on individual degree projects that can be research-led or application driven.

Modules

Modules for the MSc Data Science programme include:

- Visual Analytics

- Data Science Research Methods and Seminars

- Big Data and Data Mining

- Big Data and Machine Learning

- Mathematical Skills for Data Scientists

- Data Visualization

- Human Computer Interaction

- High Performance Computing in C/C++

- Graphics Processor Programming

- Computer Vision and Pattern Recognition

- Modelling and Verification Techniques

- Operating Systems and Architectures

Facilities

The Department of Computer Science is well equipped for teaching, and is continually upgrading its laboratories to ensure equipment is up-to-date – equipment is never more than three years old, and rarely more than two. Currently, our Computer Science students use three fully networked laboratories: one, running Windows; another running Linux; and a project laboratory, containing specialised equipment. These laboratories support a wide range of software, including the programming languages Java, C# and the .net framework, C, C++, Haskell and Prolog among many; integrated programme development environments such as Visual Studio and Netbeans; the widely-used Microsoft Office package; web access tools; and many special purpose software tools including graphical rendering and image manipulation tools; expert system production tools; concurrent system modelling tools; World Wide Web authoring tools; and databases.

As part of the expansion of the Department of Computer Science, we are building the Computational Foundry on our Bay Campus for computer science and mathematical science.

Career Destinations

- Data Analyst

- Data mining Developer

- Machine Learning Developer

- Visual Analytics Developer

- Visualisation Developer

- Visual Computing Software Developer

- Database Developer

- Data Science Researcher

- Computer Vision Developer

- Medical Computing Developer

- Informatics Developer

- Software Engineer



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The opportunity to exploit Big Data is recognised world-wide and some countries include it in their economic strategies. The UK Government identified Big Data as one of the 8 great technologies which will have a strong impact on growth and the Scottish Government highlights it as an emerging opportunity for Scotland. Read more
The opportunity to exploit Big Data is recognised world-wide and some countries include it in their economic strategies. The UK Government identified Big Data as one of the 8 great technologies which will have a strong impact on growth and the Scottish Government highlights it as an emerging opportunity for Scotland.

Our MSc in Data Science aims to produce specialist data scientists with training in industry relevant data acquisition, storage, warehousing, analytics and visualisation tools and techniques and a good understanding of the needs of industry. The course will prepare graduates in technical disciplines for a career in the design and implementation use of computer-analytics and visualisation solutions for industry.

Visit the website: http://www.rgu.ac.uk/computing/study-options/postgraduate/masters-in-data-science

Course detail

The course will focus on satisfying industry’s demand for data scientists who have the ability to:

• Apply appropriate data science tools and techniques to industry’s data in order to uncover important, previously unknown information only implicit in the data.
• Relate a company’s key performance indicators to a data science problem area in order to focus a data science task.
• Handle large amounts of real-time, non-persistent, data.
• Contribute to business decision-making by effectively communicating (potentially large volumes of) key data visually.
• Understand, clean up, summarise, interpret and manage data.
• Grasp key knowledge about new problem areas in order to communicate with end-users; understand key business needs and processes and identify added value through data analytics.
• Provide user-centred data analytics at an appropriate level.
• Protect and share data as appropriate.

The course will emphasise Big Data, covering not only traditional data management systems but also systems where data and/or its storage is unstructured.

Format

Throughout the course, content is complemented by practical work, allowing you to support your theoretical knowledge with practical experience in data storage, mining, warehousing, visualisation and analysis as well as transferrable skills. You will be taught through a mixture of lectures, tutorials, labs. You will be invited to attend talks presented by highly-experienced researchers, speakers from industry, and members of the BCS (British Computer Society) on a wide range of industry-related topics. You will also be supported through our online virtual learning environment where you can access a wide variety of resources and other support materials.

The individual project provides an opportunity for applying specialist knowledge together with analytic, problem-solving, managerial and communication skills to a particular area of interest within data science. Working with the full support and guidance of an allocated project supervisor, you will be given the opportunity to propose, plan, specify, develop, evaluate, and present a substantial project.

Placements and accreditation

Students who perform particularly well during their first semester of studies will be invited to apply for a 45-week internship.

Careers

The course prepares you for a career in Data Science. Job openings include: Data Scientist, Data Analyst, Data Visualisation Specialist, Data Manager, Database Designer/Manager, Data Mining Expert and Big Data Scientist.

Aberdeen is home to many multinational oil and gas companies and associated suppliers such as mainstream software houses, IT providers to major oil-related companies, specialist software consultancies, and venture capital start-ups.

The university is involved in a number of commercial collaborations on a local, national and international scale with organisations such as BP, British Geological Survey, Wood Group PSN, Accenture, WIPRO and many Aberdeen-based software development companies.

The course also prepares students for research careers by providing the skills necessary of an effective researcher. Suitable MSc graduates may continue to PhD programmes within the school.

How to apply

To find out how to apply, use the following link: http://www.rgu.ac.uk/applyonline

Funding

For information on funding, including loans, scholarships and Disabled Students Allowance (DSA) please click the following link: http://www.rgu.ac.uk/future-students/finance-and-scholarships/financial-support/uk-students/postgraduate-students/postgraduate-students/

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Train to become a data analyst in a growing industry by studying techniques such as data mining, statistical modelling, business intelligence and data visualisation. Read more
Train to become a data analyst in a growing industry by studying techniques such as data mining, statistical modelling, business intelligence and data visualisation. Study on a course which has been developed with direct input from industry experts who will bring real life business case scenarios to you.

More about this course

This specialist advanced course will equip students with the theoretical, technical and practical data analytics competencies required in an area of economic growth. The course curriculum content has been developed with direct input from industry experts and utilises specialist software tools and techniques. Students’ experience of the course will be enriched with exposure to real life business case scenarios brought to them by skilled professionals in industry.

The specialist nature of the course will allow students to explore and experience advanced techniques in data science. Students will acquire practical skills, often first-hand from an external practitioners, preparing them for employment as data analysts. Students will also be trained in the use of software tools and environments currently used by the industry sector. For example, students on this course will have exposure to R and Python programming, IBM SPSS, SAS®, Tableau, Oracle and Hadoop.

A range of assessment methods are used on the course, including written reports, practical and research assignments, demonstrations, presentations, group work and examinations.

Modular structure

The modules listed below are for the academic year 2016/17 and represent the course modules at this time. Modules and module details (including, but not limited to, location and time) are subject to change over time.

Year 1 modules include:
-Data Analysis and Visualization (core, 20 credits)
-Data Mining for Business Intelligence (core, 20 credits)
-Data Modelling and OLAP Techniques for Data Analytics (core, 20 credits)
-MSc Project (core, 60 credits)
-Programming for Data Analytics (core, 20 credits)
-Statistical Modelling and Forecasting (core, 20 credits)
-Financial Mathematics (option, 20 credits)
-Work Related Learning (option, 20 credits)

After the course

On completion of the course graduates will be well equipped to work in some of the fastest growing sectors of the data science and big data industries. The course offers wide-ranging career opportunities in the commercial industry, public and financial services, especially in areas requiring big data analysis such as consumer, healthcare, scientific, financial, security intelligence, business and social sciences.

Job roles include data scientist, data analyst, digital analyst, big data consultant, statistical analyst and data modeller. Graduates will be eligible to work as data analysts or data scientists in a multitude of areas where skills such as R or Python programming, machine learning and statistical modelling, SAS® and SPSS experience, data visualisation and data-driven decision-making are required.

The course also provides an excellent basis for further study for those wishing to pursue a higher-level research degree or embark on an industry-based research career.

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

What will I study?

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

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

How will I study?

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

How will I be assessed?

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

Who will be teaching me?

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

What are my career prospects?

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

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

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

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

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

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

Program Advantages:

- Introduction of leading tools that convert data to knowledge

- Possibility to obtain business-relevant certificates

- Exposure to both academic and applied industry research

Career Opportunities:

- Digital/Web Analyst

- Customer Analyst

- Data Scientist

- Credit Risk Analyst

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

Program

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

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

Internship -

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

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

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

French language classes -

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

Admission & Fees

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

Admission requirements -

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

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

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

Application process -

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

Rolling admission is offered from October 2017.

Checklist requirements:

- Online application form

- Transcripts and diploma translated into English or French if necessary

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

- CV / Resume

- Copy of passport

- 80€ application fee

Tuition 2017-2018:

- € 15,000 for domestic and international students

- International merit-based scholarships are available

Funding and scholarship-

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

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

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



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IMF Business School, in collaboration with the Camilo José Cela University, launches the Master in Business Analytics and Big Data. Read more

IMF Business School, in collaboration with the Camilo José Cela University, launches the Master in Business Analytics and Big Data. This program aims to provide students with a global view of Big Data technologies and their use, as well as applied and practical training in Business Analytics. IMF is a member of the Association of Computer Technicians (ATI).

This Master's degree is aimed at both new graduates and experienced professionals who wish to focus on the new professions related to data analysis (Data Analyst, Data Scientists, Chief Data Officer, Data Engineer ...). The recommended access profiles are those related to ICTs, careers with a high qualitative component, and careers in business and economics.

The Master's Degree in Big Data of IMF, of an academic year of duration, is taught in online mode supported by an advanced technological platform that allows the student to access the study, regardless of geographical location or time availability.

With the IMF Student Centered methodology, the student is placed at the center of all training services and guides the institution towards academic and professional success. The student will be able to know his progress at all times, be attended when he needs to, access to all resources with total freedom and have a coaching service, headhunting and job placement.

All students who successfully complete this program will obtain a double Master's degree from the University Camilo José Cela and Master by MFI Business School. They will have at their disposal all the advantages of MFI:

  • Program Scholarships and Study Grants
  • Recruiters with over 12,000 vacancies posted in the last year
  • Unlimited tutoring
  • Live online classes
  • Masterclasses and networking sessions
  • Financing up to 12 months without interest or bank intervention
  • Virtual library with over 30,000 references
  • Virtual library with access to any master classes
  • Access to the VIP Club with discounts on leisure, travel, restaurants ...

TEMARY:

  • MODULE I - Technological Foundations for Data Processing
  • MODULE II - Models and Statistical Learning
  • MODULE III - Applied Automatic Learning
  • MODULE IV - Text Mining and Natural Language Processing (NLP)
  • MODULE V - Business Intelligence and Visualization
  • MODULE VI - Big Data Infrastructure
  • MODULE VII - Storage and Data Integration
  • MODULE VIII - Value and Context of Big Data Analytics
  • MODULE IX - Analytical Applications
  • MODULE X - Final Master's Work
  • COURSE I - English Course


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Centennial College's Marketing Research and Analytics program positions you at the forefront of a cutting edge job market in which organizations have oceans of data available to them but struggle to make sense of it as marketing becomes increasingly data-driven. Read more
Centennial College's Marketing Research and Analytics program positions you at the forefront of a cutting edge job market in which organizations have oceans of data available to them but struggle to make sense of it as marketing becomes increasingly data-driven. As a result, there is a large and growing demand for trained researchers who can harness the power of big data using the latest tools and analytical techniques to uncover new insights and drive businesses forward.

This Marketing Research and Analytics program combines advanced courses in marketing research and big data analytics with training on leading commercial technologies and platforms and the opportunity to gain in-demand industry certifications.

This program equips you with knowledge, skills and training in leading business intelligence and marketing research technologies and tools used in the field. Among them are SAS Enterprise Guide and SAS Enterprise Miner, Environics Analytics Envision (used to develop comprehensive profiles of selected target markets), SPSS, Tableau (the leading data visualization software), Excel, XL Miner, Dell Factiva and NVIVO (qualitative research and text analysis software).

Upon graduation, you receive an Ontario Graduate Certificate from Centennial College, plus certificates of recognition from SAS and Environics Canada. In addition, you are put on an accelerated track to earning the Certified Marketing Research Professional (CMRP) designation, the premier credential in Canadian marketing research from the Marketing Research and Intelligence Association (MRIA).

Career Opportunities

Program Highlights
-The Marketing – Research and Analytics program combines marketing research principles and skills with cutting edge "big data" analytics techniques to equip you with the training required to deliver insights and strategies to help organizations make smarter and more impactful business decisions.
-Employed is an extensive use of learner-centered approaches such as case studies, simulations and project-based learning, with a focus on developing project management, teamwork, analytical thinking, and report writing and presentation skills.
-Hands-on learning covers areas such as questionnaire design, data manipulation, quality control, statistical output and program development.
-There is a strong focus on applying marketing research and analytics to strategic marketing decision-making.
-In the second semester, you develop and implement a capstone project that will integrate and apply your learning.
-In addition to market research technologies, you also have access to the full suite of Microsoft products, including Microsoft Excel, XL Miner, Access and PowerPoint.
-Once you graduate, you have the option to take the Comprehensive Marketing Research Exam (CMRE) on campus at Centennial College, which leads to the Certified Marketing Research Professional (CMRP) designation.

Articulation Agreements
Start with a graduate certificate, and continue to a master of business administration through our degree completion partnership. Successful graduates of this Marketing – Research and Analytics program may choose to continue with courses leading to a graduate degree.

Career Outlook
-Marketing research specialist or analyst
-Research analyst
-Marketing research and intelligence coordinator
-Market intelligence specialist or analyst
-Customer insights analyst
-Consumer research manager
-Business intelligence analyst
-Market research analytics manager
-Web marketing analyst
-Customer experience analyst
-CRM analyst
-Direct response analyst
-Digital marketing analyst
-Social media analyst
-Data and analytics specialist
-Business analytics specialist
-Loyalty program analyst
-Sales analyst
-Marketing strategy analyst

Program Outcomes
-Optimize the financial results produced by interactive marketing programs through the application of marketing analytics
-Contribute to the design of a marketing analytics team project (develop charter, business case financials, technical requirements, design, test plan, test results, approval to proceed) and the management of the resulting project
-Create, manage and mine, and apply modelling and decision making functions to a database
-Utilize data auditing techniques and quality control processes that are consistent with current marketing research codes of conduct and Canadian privacy principles to ensure the integrity of the data collection, storage, analysis and presentation processes
-Compare and contrast, evaluate and select appropriate data sources to meet specific marketing objectives
-Conduct industry, competitor and customer analyses using a wide variety of secondary research sources
-Produce reliable and analyzable data through the application of sound questionnaire design principles to marketing research projects
-Design marketing research projects and interactive marketing programs that are founded in sound sampling techniques, hypothesis testing and research design
-Solve business and marketing problems by identifying, selecting and applying effective, current and relevant techniques such as descriptive and inferential analysis
-Prepare provisional output of analyses including cross-tabulations and pivot tables that address the needs of analysts and prepare final output, including research reports, presentation sides and visual representations of data that address the needs of management
-Develop actionable recommendations based on situation analyses and research findings

Areas of Employment
-Retail corporations
-Organizations with in-house analytical and research functions
-Marketing research firms

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Summary. Data Science is a rapidly developing field of study within both academia and industry. Its interdisciplinary nature ensures its wide application domain. Read more

Summary

Data Science is a rapidly developing field of study within both academia and industry. Its interdisciplinary nature ensures its wide application domain. This MSc Data Science aims to prepare students for a successful career as a data scientist or business analyst working in any profession where large amounts of data is collected, hence there is a need for skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation. These type of skills are typically in high demand in IT business, security and health sectors, intelligent transport, energy efficiency and the creative industries.

More generally data and analytics capabilities have developed rapidly in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. Most companies, however, are not capturing the full potential value from data and analytics because they do not have the required expertise. Consequently, the MSc Data Science aims to address these challenges by providing a firm grounding in the core disciplines of data analytics and information processing, partnered with a broad appreciation of aspects of other disciplines where data science can form natural synergistic relationships.

Modules

  • Business intelligence
  • Machine learning and data modelling
  • Data science foundations
  • Big data and infrastructure
  • Statistical modelling and data mining
  • Masters project (research)

Academic profile

Ulster University academics are actively involved in both research and teaching and this ensures that the developments accrued through research can feed into the teaching of students. A high percentage of staff are members of the Higher Education Academy, and all staff are expected to have a Postgraduate Certificate in University Teaching or equivalent. All Computing courses are subject to periodic Faculty Review and University Revalidation.

Career options

The key message from employability and work-related learning initiatives is that enhancing opportunities to develop work-related learning and employability enhances the learning of the subject being studied. We understand the importance of including real industrial and commercial contexts to our student's experience, so this MSc Data Science will pursue opportunities for industrially linked teaching material and student project work. In this regard, we will utilise our business and industry links to facilitate an industrially relevant student project. Such projects create valuable experiences for the student, and additionally, they can also help to build new and ongoing collaborations with departments and companies, with the potential to tap into funding streams designed for industry-academic research and development.

A recent statement from Ulster University’s Careers Office indicates that Data analysts are in high demand across all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education. Data analysts can work in large companies such as the ‘big four’ consultancies or financial services firms, or consumer retail firms, small and medium sized businesses such as marketing agencies’ or the public sector.



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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. Read more

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.

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.

What does this course lead to?

Graduates from this course would be well-suited for a range of data analytics and data systems design roles. If you are interested in research, the course offers opportunities to continue on to PhD study.

Who is this course for?

This conversion master’s course is ideally suited to students from a number of academic backgrounds who have a strong interest in learning to code and transform data.

The course is also suited to those with extensive industry experience in this area, and who wish to gain an academic qualification.

What you will study

Core units with CATS points:

  • Research Methods (15)
  • Professional Issues and Practice (15)
  • Pilot Project (15)
  • Research Project (45)
  • Computer Fundamentals (15)
  • Data Management (15)
  • Web Technologies (15)
  • Databases (15)
  • Data visualisation (15)
  • Data Analysis (15)

Facilities

Students have access to high-spec computer labs and make use of the latest design and development programs.

Students will test applications in our new device laboratory - a special test area integrated within one of our existing software development spaces. This arrangement allows you to test your website designs and apps on real equipment, ensuring they perform as expected on the target platforms.

Your future

Suitable roles for graduates include:

  • Business Analyst
  • Project manager
  • Database manager
  • Digital marketer
  • Software developer
  • Web Developer
  • Application development
  • Project manager
  • Senior database analyst
  • Senior user experience analyst
  • Software architect
  • Network deployment specialist.

Industry links

Course content is developed with input from a variety of sources including an industrial liaison panel. This ensures that your studies include the latest technologies and working practices.

You’ll also have the chance to work directly with real-world companies on live briefs, events and projects, while regular BCS meetings hosted at the University help you to build professional connections and secure valuable work experience opportunities.



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This degree equips you with advanced knowledge and skills for a range of careers from data analyst to computer scientist or IT consultant. Read more
This degree equips you with advanced knowledge and skills for a range of careers from data analyst to computer scientist or IT consultant.

Cloud Computing and Big Data remain hot topics in the media and there is strong demand for graduates with technical skills in this area. Kent’s Advanced Computer Science (Cloud Computing & Big Data) MSc equips you with the advanced knowledge and skills for a wide range of careers from data analysts to computer scientists and IT consultants.

The programme combines a wide choice of advanced topics in computer science with specialist modules relating to cloud computing and big data. These include Google App Engine, Apache Spark, Software-as-a-Service, Data Centers Galaxy, Mobile Cloud, Hadoop, Bitcoin and MapReduce.

This programme is available with an optional industrial placement which provides an opportunity to work in real-world, technical and business roles, enhancing your study experience and having a dramatic impact on your choices after graduation. We have strong links with industry including Cisco, IBM, Microsoft and Oracle and are among the top ten in the UK for graduate employment prospects.

The Advanced Computer Science (Cloud Computing & Big Data) MSc is aimed at graduates considering a career in research and development. It would also provide an excellent foundation for PhD study.

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This postgraduate Masters course is uniquely designed for both computing and non-computing graduates wishing to pursue a career in data science community. Read more
This postgraduate Masters course is uniquely designed for both computing and non-computing graduates wishing to pursue a career in data science community.

Data science is at the intersection of the fields of computer science, statistics, and design. In a rapidly evolving world, new types of big data are emerging from mobile devices, sensors, instruments, transactional systems, web logs, social media, the cloud and other sources. Businesses are accumulating big data at a rate that often exceeds their capacity to extract value from it.

MSc Data Science is ideally placed to provide you with technical knowledge and employer-focused skills required in a data scientist role. The course will develop your specialist knowledge of data acquisition, data cleansing, data analysis, information extraction, prediction, visualization, story-telling and explanation.

You'll gain hands-on experience using industry standard tools for data science, business intelligence and analytics including SAS, Tableau MS SQL Server, Oracle Database, and R Programming.

Modules

Research methods and professional issues
Future internet technologies
Statistical analysis and modelling
Business intelligence
Data management
Machine learning
Data mining and analysis
MSc thesis

All modules are assessed by a mix of coursework and examinations.

Teaching and learning

You'll make use of our e-learning suite and learn in a combination of lectures, seminars, workshops and private study. You'll have access to specialist software including Microsoft SQL Server 2012, Netbeans 7.x with Java 7, Oracle, Python, SAS and Visual Paradigm.

Placements

You are encouraged to actively seek placements, work experience and voluntary work during your studies to improve your CV and to give you the opportunity to put theory into practice. Many opportunities are offered through the University's central Employability team, who can support you in finding a placement.

Accreditation

In order to ensure the course runs in accordance with industry recognised standards we are seeking accreditation for both Chartered Engineer (CEng) status as well as Chartered IT Professional (CITP) accreditation by the British Computer Society, the Chartered Institute for Information Technology.

We are also seeking Chartered Engineer (CEng) status with both the Institute of Engineering and Technology and the Engineering Council.

Employability

Career opportunities range from IT services to business consultancy, and this course will prepare you for a career in the business intelligence community working actively with a range of resources including tools from major commercial vendors such as Microsoft and SAS.

As a graduate of this course you should to be able to work within the areas of business intelligence or business and data analytics, in roles such as a business intelligence specialist, data or business analyst or business intelligence developer.

LSBU Employability Services

LSBU is committed to supporting you develop your employability and succeed in getting a job after you have graduated. Your qualification will certainly help, but in a competitive market you also need to work on your employability, and on your career search. Our Employability Service will support you in developing your skills, finding a job, interview techniques, work experience or an internship, and will help you assess what you need to do to get the job you want at the end of your course. LSBU offers a comprehensive Employability Service, with a range of initiatives to complement your studies, including:

• Direct engagement from employers who come in to interview and talk to students
• Job Shop and on-campus recruitment agencies to help your job search
• Mentoring and work shadowing schemes.

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Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. Read more

Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. This MSc will equip students with the analytical tools to design sophisticated technical solutions using modern computational methods and with an emphasis on rigorous statistical thinking.

About this degree

The programme combines training in core statistical and machine learning methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical computing and modelling. Students choosing the statistics specialisation will take one compulsory module and up to two additional modules in computer science, with the remaining modules (including the research project) taken mainly from within UCL Statistical Science.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research dissertation/report (60 credits).

Core modules

  • Introduction to Statistical Data Science
  • Introduction to Machine Learning
  • Statistical Design of Investigations
  • Statistical Computing

Optional modules

At least two from a choice of Statistical Science modules including:

  • Applied Bayesian Methods
  • Decision & Risk
  • Factorial Experimentation
  • Forecasting
  • Quantitative Modelling of Operational Risk and Insurance Analytics
  • Selected Topics in Statistics
  • Stochastic Methods in Finance I
  • Stochastic Methods in Finance II
  • Stochastic Systems

Up to two from a choice of Computer Science modules including:

  • Affective Computing and Human-Robot Interaction
  • Graphical Models
  • Statistical Natural Language Processing
  • Information Retrieval & Data Mining

Dissertation/report

All students undertake an independent research project, culminating in a dissertation usually of 10,000-12,000 words. Workshops running during the teaching terms provide preparation for this project and cover the communication of statistics.

Teaching and learning

The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.

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

Careers

Graduates from UCL Statistical Science typically enter professional employment across a broad range of industry sectors or pursue further academic study. 

The Data Science MSc is a new programme with the first cohort of students due to graduate in 2017. Recent career destinations for graduates of the related Statistics MSc include:

  • Management Associate, HSBC
  • Statistical Analyst, Nielsen
  • PhD in Statistics, UCL
  • Mortgage Specialist, Citibank
  • Research Assistant Statistician, Cambridge Institute of Public Health

Employability

Data science professionals are likely to be increasingly sought after as the integration of statistical and computational analytical tools becomes essential in all kinds of organisations and enterprises. A thorough understanding of the fundamentals is to be expected from the best practitioners. For instance, in applications in marketing, the healthcare industry and banking, computational skills should should be accompanied by statistical expertise at graduate level. Data scientists need a broad background knowledge so that they will be able to adapt to rapidly evolving challenges. Recent graduates from the related Statistics MSc have been offered positions as research analysts or consultants, and job opportunities in these areas are increasing.

Why study this degree at UCL?

UCL Statistical Science has a broad range of research interests, but has particular strengths in the area of computational statistics and in the interface between statistics and computer science.

UCL's Centre for Computational Statistics and Machine Learning, in which many members of the department are active, has a programme of seminars, masterclasses and other events. UCL's Centre for Data Science and Big Data Institute are newer developments, again with strong involvement of the department, where emphasis is on research into big data problems.

UCL is one of the founding members of the Alan Turing Institute, and both UCL Statistical Science and UCL Computer Science will be playing major roles in this exciting new development which will make London a major focus for big data research.

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.

The following REF score was awarded to the department: Statistical Science

82% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

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



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Data is becoming an ever increasing part of modern life, yet the talent to extract information and value from complex data is scarce. Read more

Data is becoming an ever increasing part of modern life, yet the talent to extract information and value from complex data is scarce. This Masters will provide you with a thorough grounding in state-of-the art methods for learning from data, both in terms of statistical modelling and computation. You will also gain practical hands-on experience in carrying out various data-driven analytical projects. Previous study of Statistics or Computing Science is not required.

Why this programme?

  • The University of Glasgow’s School of Mathematics and Statistics is ranked 3rd in Scotland and 11th in the UK (Complete University Guide 2017).
  • The Statistics Group at Glasgow is the largest Statistics group in Scotland and internationally renowned for its research excellence.

Programme Structure

Core courses

  • Bayesian Statistics
  • Big Data Analytics
  • Data Management and Analytics using SAS
  • Generalised Linear Models
  • Introduction to R Programming
  • Preliminary Mathematics for Statisticians 1
  • Probability 2
  • Regression Models2
  • Statistical Inference2

One Course is optional for students with sufficient background in Linear Algebra and Calculus.

Two students who have already completed an equivalent course can substitute this course by any other optional course, including optional courses offered as part of the MRes in Advanced Statistics (see the website for details).

In your project (60 credits) you will model data collected from research in environmental science, assessed by a dissertation.

Optional courses

Students choose at least two courses from group 1 and at least one course from group 2.

Group 1

  • Artificial Intelligence
  • Information Retrieval
  • Machine Learning
  • Programming

Group 2

  • Data Analysis
  • Professional Skills

Group 3

  • Biostatistics
  • Design of Experiments
  • Environmental Statistics
  • Financial Statistics
  • Functional Data Analysis
  • Multivariate Methods
  • Spatial Statistics
  • Stochastic Processes
  • Statistical Genetics
  • Time Series

 In your project (60 credits) you will tackle a complex data analytical problem or develop novel approaches to solving data analytical challenges. 

Career prospects

There is a massive shortage of data-analytical skills in the workforce. Statistician is projected to be one of the fastest-growing occupations. There is a massive shortage of data-analytical skills in the workforce. Statistician is projected to be one of the fastest-growing occupations. Statistical Analysis and Data Mining was listed by LinkedIn as the hottest skill in 2014 and came second in 2015.

Our graduates have an excellent track record of gaining employment in many sectors including medical research, the pharmaceutical industry, finance and government statistical services, while others have continued to a PhD. Our recent graduates have taken up positions as Statisticians with the Scottish Government, as Advanced Analytics Analyst at Deloitte Ireland, as Consultant at the World Bank and as Research Officer at Kenya Medical Research Institute (KEMRI).



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Understanding data is becoming increasingly important for us all. This is especially true for the intelligence analyst working for a police intelligence unit or business analytics department. Read more

Understanding data is becoming increasingly important for us all. This is especially true for the intelligence analyst working for a police intelligence unit or business analytics department. The MSc Crime Intelligence and Data Analytics (with Advanced Practice) course helps you develop the necessary skills to work in these sectors.

Course details

The work boundaries of the traditional police intelligence analyst and digital forensic investigator are becoming blurred – today’s analysts need to be cyber aware, understanding how communication records and web search histories can be extracted and analysed.

This course covers these areas as well as theories that provide a better sense of the causes of crime and the prevention measures that can be put in place to stabilise and reverse these trends. Analysts shouldn’t be phased by data simply because of its size, complexity or format. This course provides you with the skills to work effectively with large datasets, allowing you to make more informed decisions in relation to criminal investigations. Key features include writing code to quickly clean up data and packaging it so it’s suitable for analysis and visualisation. You will discover that the world constantly presents data in data frames or spreadsheets – our daily activities are invariably logged by a time, date, geolocation. You develop these skills along with your confidence in applying them to make more sense of the data – analysing Twitter downloads, searched words and images, geolocation points or big data. This course also explores strategies employed in forensic investigation. It gives you the space and opportunity to develop your own area of interest in a 60-credit research project where your supervisor enables you to maximise your skillsets from academic writing to data analytics.The two-year MSc Crime Intelligence and Data Analytics (with Advanced Practice) is an opportunity to enhance your qualification by spending one year completing an internship, research or study abroad experience. Although we can’t guarantee an internship, we can provide you with practical support and advice on how to find and secure your own internship position. A vocational internship is a great way to gain work experience and give your CV a competitive edge. Alternatively, a research internship develops your research and academic skills as you work as part of a research team in an academic setting – ideal if you are interested in a career in research or academia. A third option is to study abroad in an academic exchange with one of our partner universities. This option does incur additional costs such as travel and accommodation. You must also take responsibility for ensuring you have the appropriate visa to study outside the UK, where relevant.

What you study

For the MSc award you must successfully complete 120 credits of taught modules and a 60-credit master's research project.

Course structure

Core modules

  • Coding for Intelligence Analysts
  • Crime Science: Theories, Principles and Intelligence Sources
  • Cyber Security and Digital Investigation
  • Forensic Investigative Strategy
  • Legal Issues and Evidence Reporting
  • Research Methods and Proposal

Advanced Practice options

  • Research Internship
  • Study Abroad
  • Vocational Internship

Modules offered may vary.

Teaching

How you learn

You learn through a range of lectures, seminars, tutorials and IT laboratories, using a variety of software. Simulated problems and scenarios are posed in much the same way that analysts would face in the real world. You have the opportunity to use software that is found in real-world intelligence analysis and digital forensic units and data science. Engaging and learning from your peers will help you to achieve solutions. Much of the software you use in class can be downloaded for home use.

How you are assessed

You are assessed through a formal exam as well as through structured coursework.

Employability

You can expect to apply for an intelligence researcher and intelligence analyst role in a wide variety of career opportunities ranging from security, policing and business.



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The digital revolution has led to an unprecedented volume of information about consumers, which progressive organisations are eager to understand and use. Read more

The digital revolution has led to an unprecedented volume of information about consumers, which progressive organisations are eager to understand and use. This innovative masters degree will give you the practical skills to analyse consumer data and provide insights for successful marketing strategies.

Taught by leading academics from Leeds University Business School and School of Geography, you’ll explore a range of analytical techniques including applied Geographic Information Systems (GIS) and retail modelling, consumer and predictive analytics and data visualisation. You’ll also develop the softer skills to use the results of these analyses to inform decisions about marketing strategy.

Thanks to our connections with businesses worldwide, you’ll have access to emerging trends in topics such as consumer behaviour, decision science and digital and interactive marketing. You’ll further develop your practical skills with the opportunity to work on a live data project provided by a company.

Academic excellence

This courseoffers you a rare combination of teaching expertise; the Business School’s academic excellence in Marketing alongside world-class teaching from the School of Geography, which draws on the knowledge of the Centre for Spatial Analysis and Policy.

The University of Leeds is a major centre for big data analytics and you’ll benefit from affiliation with the UK’s Consumer Data Research Centre. The centre aims to make data that are routinely collected by businesses and organisations accessible for academic purposes. Coordinating and analysing this large and complex data has the potential to increase productivity and innovation in business, as well as to inform public policy and drive development.

Read an interview with the academic team to learn more about our expertise and the growing importance of this emerging subject area.

Course content

Core modules will introduce you to a range of analytical methods, ensuring you develop a solid foundation in the essential skills for consumer analytics and marketing strategy.

You’ll learn how to analyse geographic data using GIS software and understand the application of this in retail modelling, to evaluate new markets and locations. You’ll study predictive analytics, big data and consumer analytics, business analytics and decision science, and learn how to communicate results through data visualisations.

Alongside this, you’ll learn how to deploy data to inform decisions about marketing strategy. Marketing modules include marketing strategy, consumer behavior and direct, digital and interactive marketing. You’ll also deliver your own data-driven marketing research project for a company.

Optional modules allow you to further your knowledge in a related area of interest, either corporate social responsibility, internal communications and managing change, or applied population and demographic analysis.

By the end of the course, you’ll submit an independent project. You can either research a topic in-depth and submit a dissertation, or gain practical experience through a consultancy project working with an external organisation.

Course structure

Compulsory modules

You’ll take the nine compulsory modules below, plus your dissertation, which can be a choice of either a research dissertation or marketing consultancy project.

  • Geographic Data Visualisation & Analysis 15 credits
  • Big Data and Consumer Analytics 15 credits
  • Predictive Analytics 15 credits
  • Applied GIS and Retail Modelling 15 credits
  • Business Analytics and Decision Science 15 credits
  • Consumer Behaviour 15 credits
  • Marketing Research Consultancy Project 15 credits
  • Direct, Digital and Interactive Marketing 15 credits
  • Marketing Strategy 15 credits
  • Dissertation OR Marketing Consultancy Project 30 credits

Optional modules

You'll take one further optional module.

  • Applied Population and Demographic Analysis 15 credits
  • Corporate Social Responsibility and Sustainability 15 credits
  • Internal Communications and Change Management 15 credits

For more information on typical modules, read Consumer Analytics and Marketing Strategy MSc in the course catalogue

Learning and teaching

We use a range of teaching methods so you can benefit from the expertise of our academics, including lectures, workshops, seminars, simulations and tutorials. Company case studies provide an opportunity to put your learning into practice.

Independent study is also vital for this course, allowing you to prepare for taught classes and sharpen your own research and critical skills.

Assessment

Assessment methods emphasise not just knowledge, but essential skills development too. You’ll be assessed using a range of techniques including exams, group projects, written assignments and essays, in-course assessment, group and individual presentations and reports.

Career opportunities

As a graduate of this course you will be equipped with advanced skills in consumer analytics and marketing strategy, ideal for those wishing to pursue a career in consumer data analytics, marketing and/or management.

Due to the digital revolution, companies from around the world and in many industrial sectors have access to greater amounts of data.

The most progressive companies in the world are particularly interested in marketing graduates with strong analytical skills, and typical roles could include marketing or consumer data analyst, direct marketing manager, marketing manager, retail manager, or marketing or management consultant.

Careers support

As a masters student you will be able to access careers and professional development support, which will help you develop key skills including networking and negotiating, and put you in touch with potential employers.

Our dedicated Professional Development Tutor provides tailored academic and careers support to marketing students. They work in partnership with our academics to help you translate theory into practice and develop your interpersonal and professional business skills.

You can expect support and guidance on career choices, help in identifying and applying for jobs, as well as one-to-one coaching on interpersonal and communication skills.

Read more about careers support at the Business School.



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