MSc Data Analytics for Business

Program Overview

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MSc, Master of Science Data Analytics for Business

Presentation

This Master of Science programme prepares students to become professionals with the ability to bridge the gap between data specialists and processing techniques with various business sectors. Graduates emerge as experts in data analytics and the digital transformation of companies.

Curriculum

The programme covers a wide range of subjects to ensure a comprehensive approach to the field. Students are exposed to programming languages, data mining, data organisation, data visualisation techniques and information systems management given them a rare opportunity to become experts with a comprehensive vision, bridging the gap between cutting-edge techniques and business operations within organisations.

• Programming language (Python, SQL)
• Machine learning algorithms
• Data mining
• Data organisation and management
• Data visualisation
• Business intelligence
• Business analytics
• Information systems management

Careers

The programme was created to bridge the gap between two distinct populations (technical and managerial experts), facilitating the alignment of business objectives with data science competencies to execute successful data science projects to drive business decision-making. The professional demand for this dual expertise is high while the supply is low making graduates highly employable. The U.S Bureau of Labour predicts the number of jobs will increase by 28% through 2026. This is roughly 11.5 million new jobs in the field.

Admission and Funding

Taught entirely in English at our Bordeaux campus, the programme is designed for applicants holding a four-year degree, demonstrating proficiency in English and possessing a reasonable comfort level with mathematics and statistics.

For more information on the course requirements, tuition fees and how to apply, please visit the dedicated admission and funding page.

Testimonials

Discover KEDGE Master of Science in Data Analytics for Business

campus of Bordeaux

Olivier DUPOUET

THE OBJECTIVES OF THIS TRAINING IN DATA ANALYTICS

Artificial intelligence (AI), machine learning, and business analytics techniques are increasingly crucial for companies, enabling them to innovate and establish new, value-driven ventures.

However, as these technical specialties evolve, a critical gap has emerged: the need for professionals capable of bridging the divide between data specialists and other business sectors.

The Master of Science (MSc) in Data Analytics for Business is founded on three fundamental pillars essential for the successful digital transformation of companies:

• Modelling, based on machine learning and business analytics techniques, empowering companies to enhance operational excellence and develop new business models.
• Understanding information systems, which serve as the infrastructure for deploying these models.
• Profound knowledge of the company's businesses and major functions, guiding project initiation and application utilisation.

Programme's Labels

Highlights for the MSc in Data Analytics for Business

A THOROUGH MANAGERIAL TRAINING

To equip students with both technical expertise and advanced managerial skills, the curriculum encompasses a comprehensive range of management topics, including digital corporate strategy, ethical considerations, legal aspects, project management, and design thinking.

Real-life case studies hold particular significance in domains such as finance, marketing, and logistics, preparing future managers to address diverse challenges. Additionally, the programme emphasises the development of soft skills, including public speaking, leadership, and team management.

A GLOBAL APPROACH TO DATA

The programme covers a wide range of subjects to ensure a comprehensive approach to the field. Students are exposed to programming languages, data mining, data organisation, data visualisation techniques and information systems management given them a rare opportunity to become experts with a comprehensive vision, bridging the gap between cutting-edge techniques and business operations within organisations.

EXPERIENTIAL LEARNING

Students engage in a collaborative experience with the HACKATHON whereby students look to introduce new techniques, standards, or personal programming styles to progress on projects and develop solutions. This year, it is organised for L’Oreal using the azure tools (Microsoft). Students also engage in fields visit to companies such as IBM, Bloomberg, and Microsoft.

HIGH-PROFILE PARTNERS

Graduates of the MSc Data Analytics for Business will have access to a solid network of graduates and partner companies throughout their studies and beyond. Among those involved, Microsoft, a leader in its field, is a major sponsor of the programme. Last year, they welcomed the students during their first lecture at KEDGE. They also provided them with certification in their core cloud services, Azure, and have committed to inviting them to their job market event in June.

Corporate partners are part of the programme’s development committee ensuring the teaching is in line with recruiter’s expectations.

CLOSE COLLABORATION WITH THE RESEARCH CENTRE IN SUPPLY CHAIN

Under the guidance of Professor Frédéric Babonneau, the Research Centre in Supply Chain is committed to addressing the economic, environmental, and societal challenges inherent in the supply chain. We collaborate with companies, communities, institutions, and academia to co-develop innovative concepts, methods, and tools essential for organising and transforming supply chains. Our initiatives influence academic research, the corporate landscape, societal dynamics, and educational paradigms.

Skills developed during the MSc programme in Data Analytics for Business

TECHNICAL KNOWLEDGE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

While graduates may not need to attain expertise in these fields, it is crucial that they grasp the fundamental principles and logic that drive the work of data scientists:

• Understanding what data is, what the main sources are and what forms it can take.
• Mastery of the basic concepts around the most popular algorithms.
• Understanding of what can be done with these tools.
• Master the techniques for evaluating the quality of algorithms and identifying potential sources of problems.

MASTERING THE MANAGEMENT TECHNIQUES ASSOCIATED WITH THIS MEDIATION ROLE IN THE SPECIFIC CONTEXT OF ARTIFICIAL INTELLIGENCE

• Learning the techniques of agile project management
• Understanding of the strategic and organisational issues associated with the introduction of artificial intelligence tools into the company's processes.
• Understanding of the ethical issues associated with the use of these techniques.

Why study for an MSc in Data Analytics for Business?

The programme was created to bridge the gap between two distinct populations (technical and managerial experts), facilitating the alignment of business objectives with data science competencies to execute successful data science projects to drive business decision-making. The professional demand for this dual expertise is high while the supply is low making graduates highly employable. The U.S Bureau of Labour predicts the number of jobs will increase by 28% through 2026. This is roughly 11.5 million new jobs in the field.

Taught entirely in English at our Bordeaux campus, the programme is designed for applicants holding a four-year degree, demonstrating proficiency in English and possessing a reasonable comfort level with mathematics and statistics.

For more information on the course requirements, tuition fees and how to apply, please visit the dedicated admission and funding page. Start your journey now and become a leader in the new disciplines linked to data and artificial intelligence as business accelerators!

Key figures

• 38 000€: Project Owner average starting salary
• 34 000€: Business Engineer average starting salary
• 40 000€: Data Analyst average annual salary

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• KEDGE Business School is one of only 1% of Business Schools in the world to hold the Triple Crown (AMBA, EQUIS & AACSB).

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