MSc Data Engineering & Cloud Computing

Data professions are the top of the list professions that will evolve the most in the coming years, according to the World Economic Forum’s « Future of Jobs Report 2020 » to a LinkedIn studyin November 2020.

Based on a pedagogical approach combining multi-disciplinary teaching, projects and feedback, aivancity’s MSc Data is a response to the opportunities and challenges raised by the massification of data in all sectors of economic activity and all areas of our modern society, leading to a significant shift in businesses 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 and Cloud Computing is to train students to become experts in data management and to master the complete data engineering cycle: acquisition, architecture, storage, cleansing, pipeline, security, 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 provides expertise in the management of massive data projects. Different types of Clouds are presented (AWS and Azure), as well as NoSQL databases.

DEGREES

• a Master of Science in "Data Engineering & Cloud Computing".
• Level 7 (equivalent to Master level) State professional certification registered with the RNCP (Répertoire National des Certifications professionnelles): «Expert infrastructure et traitement des données massives» (Expert in infrastructure and massive data processing).
• The following professional certifications: One Professional Certification per year (AWS, Azure, Kafka, MongoDB, ...)

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 training by blocks of skills contact us.

Learning Goals

• Add value to a team, company or organization with a combination of technical and business skills, knowledge about legal and ethical issues related to data engineering, and the ability to effectively communicate with professionals from different disciplines.
• Master the Cloud ecosystem
• Identify patterns and gather insights from large and complex data sets
• Gain an in-depth understanding of Big Data foundations, methods and technologies
• 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.
• Manage 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 questions of privacy, responsibility or human autonomy in decision-making

Assesment & teaching methods

The assessment of student achievements in this program will be carried out through a variety of methods, allowing for a comprehensive and accurate evaluation of their skills, in accordance with the requirements of the diploma or professional certification awarded

Click here to learn about the evaluation methods and educational approach of the program.