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).
- Introduction of leading tools that convert data to knowledge
- Possibility to obtain business-relevant certificates
- Exposure to both academic and applied industry research
- 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.
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.
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.
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.
- 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
- € 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.
This Postgraduate Certificate provides you with a practical understanding of big data analytics and emerging technologies. You will gain the essential skills and confidence required to apply knowledge and understanding of issues surrounding big data analytics in a range of contexts. There is the opportunity to develop a critical understanding of visualisation concepts and emerging technologies, as well as to develop and evaluate new or advanced bespoke solutions for making sense of big and/or complex data. The programme enables you concentrate on a specific practical area within computer science and is suitable whether you are a recent graduate or already working in the IT industry and looking to change career paths.
Gaining knowledge of big data applications and visualization, along with a critical understanding of new technologies, you will develop a systematic and critical understanding of the algorithms and programming techniques required for processing, storing, analysing, visualising and interpreting data.
The course is delivered through a combination of seminars and tutorials in evening classes. Sessions will frequently be highly interactive with a focus on the practical application of concepts and the use of case studies drawn from real life. You will apply your knowledge in a practical environment using the state of the art facilities in the Tech Hub. An emphasis on small group sizes ensures that you will have plenty of opportunities for individual discussions with your tutors. Typically, you will study for approximately nine hours a week.
Your vocational capability, academic critical thinking and intellectual development will be assessed throughout the course. This is achieved through a combination of coursework, portfolios, case studies and problem-solving exercises. You may be assessed individually or as part of a group.
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.
Big data is the description used to encompass the huge amounts of data that is common to many businesses. It has been described as the next frontier for innovation, competition and productivity in business. It is essential for companies to embrace so that they can understand their customers better, develop new products and cut operational costs.
This course has been developed to create graduates who can become data scientists capable of working with the massive amounts of data now common to many businesses. It is aimed at people who want to move into this rapidly expanding and exciting area.
The modules on this course help you develop the core skills and expertise needed by the data scientist. The course can be split into three main areas, statistics, computing and management.
In the statistics section you study modules on data mining and data modelling. These modules cover the three main data areas, which are ensuring that data is reliable and of a high quality, searching the data to discover new information and presenting interpretations of that data to the end user.
The computing section covers areas related to data integration, massive datasets stored in the cloud, how data is stored and utilised within the distributed systems of an enterprise and how organisations can utilise data to change and improve business processes.
The management modules are focused on developing your core skills around professionalism and research. All of which are valuable skills during your university studies and in your career.
Our partnerships with business inform the course design, ensuring the content is relevant, up to date and meets the needs of industry. These partnerships also enable the inclusion of some leading edge software such as SAS, SAP Hana, and Hadroop within the course. You may be able to study abroad as part of the Erasmus programme.
Key areas of study
Key areas of study include • data quality and analysis • technologies to store and mine data • professionalism and research
This course includes the SAP Business Intelligence with SAP BW 7.3 and SAP BI 4.0 e-academy (UB130e). You also have the opportunity to sit the SAP certification exam and the SAS 9 base certification exam.
Full time – September start – typically 12 or 18 months
Part time – September start – typically 36 months
Choose one from :
Many jobs for data scientists, data analysts and data mining analysts are available with salaries ranging from £35,000 to £80,000.
Jobs typically list the skills to be in areas such as statistical analysis and machine learning techniques, database and programming technologies, and expertise in statistical theory, which are all areas you cover on this course.
You also gain skills and knowledge in HaDoop, MapReduce, Java, SAS, MSQL which are some of the common technologies used in data scientist roles.
Our Big Data in Culture & Society MA recognises the growing importance of Big Data in contemporary society and addresses the theory and practice of Big Data from an arts and humanities perspective.
What is Big Data? Beyond the unprecedentedly large data sets that can be analysed to reveal patterns, trends, and associations, it is increasingly about our everyday lives. In short, it is about how the data we generate is transforming social, cultural, political and economic processes as well as the generation of knowledge.
This course is likely to appeal to a broad range of students across the Arts and Humanities from Sociology to Political Science to English to Business and beyond. It will attract forward-thinking students interested in emerging trends who recognise that data scientists and analysts require collaborators with domain specialisation and critical insights.
This Big Data in Culture & Society MA offers you the opportunity to develop your knowledge and understanding of the role of Big Data in culture and society. It will enable you to analyse Big Data across social, political and economic areas. In addition to the required content we cover, you will have the opportunity to pursue your own academic interests through our optional modules and to undertake an internship and a group project module.
By bringing together domain knowledge and technical skills and approaching these from an Arts and Humanities perspective, the course will help you develop highly valued employment skills and expertise for careers in Big Data.
The course will provide you with:
The MA Big Data in Culture and Society offers students the opportunity to develop their knowledge and understanding of the role of Big Data in culture and society. It enables them to analyse Big Data across social, political and economic areas and provides them with a background for pursuing careers in Big Data by bringing together domain knowledge and technical skills.
If you are a full-time student, we will provide you with 120 to 180 hours of teaching through lectures and seminars across the year. We expect you to undertake around 1,674 hours of independent study.
If you are a part-time student, we’ll provide you with 90 hours of teaching through lectures and seminars in your first year, and 50 hours in your second. We’ll expect you to undertake 720 hours of independent study in your first year and 954 hours in your second.
Typically, one credit equates to 10 hours of work.
We assess our modules entirely through coursework. This will comprise a mixture of essays, project work, and workshop reports, depending on the modules you choose.
King’s College London is regulated by the Higher Education Funding Council for England.
Our graduates will follow a broad range of career paths. The skills you develop are likely to be particularly transferable to work in social media management, analytics & website management, CRM management, digital advertising, metrics management, market research, marketing and across cultural industries.
Embark on a career in a leading-edge field and master the exciting and challenging world of big data!
Big data techniques are revolutionising how organisations and industries acquire and analyse data, offering valuable insights into how people live, work, play, travel, shop and exercise. These insights are valuable to marketers, researchers, designers, city planners, app developers, educators and many more.
GCU's MSc in Big Data Technologies helps students build the fundamental knowledge and practical skills for success in this fast-growing field. You'll develop competence in a range of emerging technologies: big data, cloud computing and the internet of things. You'll learn from the experts; GCU is internationally recognised for the strength of its research in these exciting subjects, driving 21st century innovation.
With both full-time and part-time study available, the programme is ideal for someone with a background in computer science, software engineering, web technologies or computer engineering who wants to enhance or update their skills. Those with backgrounds in mathematics and electronics are also well suited.
The up-to-date curriculum keeps a career-focused approach, so you'll gain valuable skills you can immediately put to work in the industry.
Your expertise in big data will enable you to provide new insights into human behaviour and psychology, which can help us build stronger and happier societies across the globe. Your work could shape smart, sustainable cities; remove barriers to education; help people make healthier choices day-to-day; improve public health… and so much more. All meaningful ways of contributing to the common good.
Full-time students complete six taught modules; three in trimester A and three in trimester B and an MSc dissertation project in trimester C. Part-time students complete six taught modules; three in Year 1, three in Year 2and an MSc project in Year 3.
Cloud Computing and Web Services
This module provides analytical and practical coverage of cloud computing and web services. It focuses on the technology, frameworks and associated standards: cloud models, cloud platforms and scalability. It also provides coverage of current web service technology and data transport representations, and integrated cloud and web service application development. Current examples from industry technology are used throughout.
Big Data Landscape
This module covers the process of managing Big Data throughout its lifecycle, from requirements through retirement. The lifecycle crosses different application systems, databases and storage media. Students will gain an understanding of the full Big Data value chain. They will be able to analyse the challenges and opportunities associated with the different stages that Big Data passes through.
This module covers the basic concepts of statistics needed to understand the critical concepts of data mining, machine learning and predictive analytics used in the visualisation and analysis of data, particularly Big data. Students will gain an understanding of data preparation, the process models used in analytics, the algorithms and their requirements, the implementation of these algorithms using current technologies, and their applicability to different types of scenario. They will also gain advanced practical skills in the design, implementation and evaluation of analytical solutions to problems involving Big Data.
Big Data Platforms
This module covers the platforms that support data storage, processing and analytics in Big Data scenarios. It focuses on highly scalable platforms that provide operational capabilities for real-time, interactive processing and on platforms that provide analytical capabilities for retrospective, complex analysis. Students will gain an advanced understanding of the principles on which these platforms are based, and their strengths, weaknesses and applicability to different types of scenario. They will also gain advanced practical skills in the design and implementation of scalable Big Data platform solutions.
Internet of Things
This module provides fundamental and practical coverage of the set of converging technologies known as the Internet of Things (IoT). It focuses on representative IoT applications, technologies, frameworks and associated standards that support and underpin IoT applications, such as sensor networks, messaging protocols, security, data storage, analytics, services and human interaction. The module provides in-depth practical coverage of representative IoT implementation frameworks including cloud-based service delivery models.
IT Professional Issues and Project Methods
This module seeks to develop understanding and practical skills in advanced project methods which are inline with industry regulations, standards and practices and are applicable to complex IT projects. Study is undertaken in an integrated fashion to ensure that the professional frameworks within which such projects are developed, deployed and managed are fully understood.
Students will investigate a topical or emerging theme in Cloud Computing or related technologies. The dissertation acts as a vehicle for extending the knowledge and understanding of the student and the technical community in some specialist technical area. It serves through its length, complexity and rigour as a suitable vehicle for extending students' range of personal, interpersonal and communication skills. In addition it serves to develop and extend a range of high-level thinking skills, including analysing and synthesising skills and affords the opportunity for the student to demonstrate initiative and creativity in a major piece of technical work.
Students will be made aware of placement opportunities provided by our industry partners. Typically a placement would be undertaken after graduating from the MSc programme but opportunities can sometimes be made available for students to undertake their MSc Dissertation in partnership with a commercial company.
The methods of assessment include class tests, coursework assignments, practical tests and technical reports. Practical implementation and evaluation form a significant part of the assessment for the taught modules and for the work of the MSc dissertation.
When you graduate, you'll be a competitive candidate for roles as a systems developer, architect or administrator in data and analytics. You'll find opportunities in a diverse range of industries: engineering, pharmaceuticals, finance, healthcare, retail, security, smart environments and more.