• Diploma issued:
    Master of Science in Data Engineering & Cloud Computing (5/6 years of higher education) - RNCP level 7 - Expert in infrastructure and big data processing
    Input level:
    Bachelor's degree, Master's degree, Doctorate
    Format:
    Initial training or sandwich courses
    Languages:
    English & French
    Next entries:
    November
    Place of re-entry:
    Paris-Villejuif or Nice
  • img
    State-recognized school
    Our program is ranked third among Big Data and Data Science master's, MS, and MBA programs in France

MSc Data Engineering & Cloud Computing program

According to the World Economic Forum's "Future of Jobs Report 2020", data-related professions are at the top of the list of professions that are set to evolve the most over the coming years. Big data analysis is also among the 5 fastest-growing skills since the pandemic crisis, according to a November 2020 LinkedIn study.

Based on a pedagogical approach combining multidisciplinary teaching, projects and feedback, the MSc Data Engineering & Cloud Computing is designed to respond to the opportunities and challenges raised by the massification of data in all sectors of economic activity and all areas of modern society, leading to a major transformation of professions and practices.

Faced with exponential growth in data volumes, companies of all sizes and in all sectors now have no choice but to think about and implement solutions capable of addressing the issues linked to this megadata, described as "fuel" or "black gold" because its value feeds the digital economy. Moreover, the importance of data is not confined to the business world. Public organizations, scientific research, NGOs, local authorities and Smart Cities are also concerned.

The aim of the MSc Data Engineering & Cloud Computing is to train students to become experts in data management and to master the complete data engineering cycle: acquisition, architecture, storage, cleansing, pipelining, securing, structuring, analysis and visualization.

The program is both technical (Cloud Computing, Programming, Database Management) and theoretical (Distributed Algorithms, Machine Learning, Distributed Database Theory). This dual approach means you'll be able to use the most common and effective tools while understanding the fundamentals of their mechanisms, so you can use them under the best conditions and configure them quickly. What's more, learning about different tools and their principles enables students to learn how to adapt to different technologies and quickly become efficient on any such tool.

The first year consolidates mastery of a data-driven project. An MSc DE1 student will be able to set up a project and propose robust architecture for projects handling reasonable volumes of data (use of centralized databases). Some high-volume computing techniques are presented.

The second year specializes in the processing of massive data (known as Big Data). From storage to analysis, this year trains students to become experts in the management of massive data projects. Different types of Clouds are presented (AWS and Azure), as well as NoSQL databases.

The program introduces many of the technologies regularly used in business, and a scientific and technological watch enables us to present cutting-edge tools and approaches in order to get to grips with them. In order to train experts in Data Engineering and Cloud Computing, the course also focuses on the place of these skills in society, through a business approach coupled with the ethics of data manipulation, green computing and the laws governing this field.

Play
pepper robot on campus
 
aivancity: the vision of a pioneering AI school

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

Diplomas

  • Master of Science Data Engineering & Cloud Computing
  • State-registered RNCP (Répertoire National des Certifications professionnelles) level 7 professional certification (equivalent to bac+5/6): "Expert infrastructure and processing of massive data".
  • The following professional certifications: One Professional Certification per year (AWS, Azure, Kafka, MongoDB, ...)
A state-approved professional certification registered with the RNCP (Répertoire National des Certifications professionnelles) under number 37763 (on 19-07-2023) at level 7 (bac+5/6 equivalent): "Expert infrastructure et traitement des données massives". It is possible to validate this certification in blocks of skills: to find out more, please contact us.
Click here to find out more about the program's evaluation and teaching methods.  
Performance indicators 2022 / 2023
Content updated on 19/11/2023
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.