Degree RNCP Level 7 qualification — Bac+5/6
Entry Level Bac+3, Bac+4 or Bac+5
Format Full-time & Work-study
Languages French & English
Campuses Paris-Villejuif
Intake November
RNCP
Niv. 7
RNCP Level 7 qualification
Large-scale Data Infrastructure and Processing Expert
 
Eduniversal 5 Palmes of Excellence
Ranked #3 Master's in Big Data & Data Science in France
2026
  • Degree Awarded:
    Master of Science in Data Engineering & Cloud Computing (5 or 6 years of higher education) – RNCP Level 7 – Expert in infrastructure and big data processing
    Entry Level:
    Bac+3, Bac+4, Bac+5
    Format:
    Initial training
    Language:
    English & French
    Location:
    Paris-Villejuif
  • Our programme is ranked 3rd amongst Master’s, MS & MBA programmes in Big Data & Data Science in France

MSc Programme in Data Engineering & Cloud Computing

Data-related roles are at the top of the list of jobs set to evolve the most in the coming years, according to the World Economic Forum’s “Future of Jobs Report 2020”. Big data analysis is also among the top five skills that have grown most rapidly since the pandemic, according to a LinkedIn study published in November 2020.

Based on an educational approach that combines multidisciplinary teaching, projects and feedback, the MSc in Data Engineering & Cloud Computing enables students to address the opportunities and challenges arising from the proliferation of data across all sectors of the economy and all areas of modern society, which is leading to a significant transformation of professions and practices.

Faced with exponential growth in data volumes, businesses of all sizes and across all sectors have no choice but to consider and implement solutions capable of tackling the challenges associated with this big data, often described as ‘fuel’ or ‘black gold’ because its value drives the digital economy. Furthermore, the importance of data is not limited to the business world. Public organisations, scientific research, NGOs, local authorities and smart cities are also affected.

The aim of the MSc in Data Engineering & Cloud Computing is to train students to become experts in data management and to master the entire data engineering lifecycle: acquisition, architecture, storage, cleaning, pipelining, security, structuring, analysis and visualisation.

The programme covers both technical subjects (cloud computing, programming, database management) and theoretical subjects (distributed algorithms, machine learning, theory of distributed databases). This dual approach enables students to learn how to use the most common and effective tools whilst understanding the fundamentals of how they work, so that they can use them to their full potential and configure them quickly. Furthermore, learning about different tools and their principles enables students to adapt to various technologies and quickly become proficient with any similar tool.

The first year builds on students’ mastery of data-centred projects. An MSc DE1 student will be able to set up a project and propose robust architectures for projects handling reasonable volumes of data (using centralised databases). Certain techniques for processing large volumes of data are introduced.

The second year specialises in the processing of big data. From storage to analysis, this year provides training in big data project management. Various types of cloud platforms are covered (AWS and Azure), as well as NoSQL databases.

This programme covers a wide range of technologies commonly used in business, whilst a focus on technological and scientific developments enables students to learn about and master the latest tools and approaches. With the aim of training experts in Data Engineering and Cloud Computing, the course also emphasises the role of these skills in society through a business-oriented approach combined with the ethics of data handling, green computing and the legislation governing this field.

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What makes this program unique?

  • Hybridization of technological, business and social aspects
  • One of the most interdisciplinary teaching teams in Europe
  • A diploma with a lifetime update guarantee© including 5 years free of charge
  • Relationships with technology partners and companies, enabling students to work on real-life problems in a wide range of sectors, including finance, software, healthcare, industry, commerce, marketing, etc.
  • Preparation for industry-leading professional certifications from Microsoft and Amazon Web Services

Qualifications

  • A Master of Science in Data Engineering & Cloud Computing
  • A state-recognised professional qualification registered with the RNCP (National Register of Professional Qualifications) at Level 7 (equivalent to a bac+5/6): “ Infrastructure and Big Data Processing Expert » (verified by: aivancity)
  • The following professional certifications: One professional certification per year (AWS, Azure, Kafka, MongoDB, etc.)
A state-recognised professional qualification registered with the RNCP (National Register of Professional Qualifications) under number 37763 (19 July 2023) at Level 7 (equivalent to a bac+5/6) – awarding body: aivancity: ‘Expert in Infrastructure and Big Data Processing’. It is possible to validate this certification in blocks of skills: for further information, please do not hesitate to contact us.
Click here to find out about the programme’s assessment and teaching methods
Performance Indicators for 2024–2025
 
97%
Satisfaction rate
100%
Graduation rate
100%
Graduation rate

Learning objectives

  • Add value to a team, company or organization through a combination of technical and managerial skills, knowledge of legal and ethical issues related to data engineering, and the ability to communicate effectively with professionals from different disciplines.
  • Mastering the Cloud ecosystem
  • Identify patterns and gather information from large, complex data sets.
  • Gain an in-depth understanding of the foundations, methods and technologies of Big Data.
  • Design and maintain a platform for processing large volumes of data.
  • Design a data warehouse and processing infrastructure.
  • Know how to use various analysis and programming tools: Python, R, Scala, Java, etc.
  • Analyze, process and model massive data using statistical models to make them intelligible and exploitable
  • Managing a data engineering project
  • Familiarize yourself with the laws and regulations that are relevant to data science and governance.
  • Integrate ethical and societal issues, such as privacy, responsibility and human autonomy, into decision-making.