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Engineer Cycle – Major in Data & AI

  • Collect, organize, and analyze massive amounts of data to extract strategic insights.

  • Design, train, and deploy machine learning and deep learning models in real-world conditions.

  • Master Big Data architectures, data pipelines, and cloud platforms dedicated to AI.

  • Address the ethical and regulatory issues related to data use and the deployment of AI.

  • Communicate the results of complex analyses to non-technical decision-makers.

  • Manage end-to-end data projects, from defining business requirements to deployment.

    Content

    The courses presented below are given as examples. Their content evolves each year to stay in line with industry advancements and the needs of companies.

    60 ECTS credits/year

    Mathematics and Statistics for Data

    • Linear Algebra and Optimization for Machine Learning
    • Probability and Statistics Applied to Data Science
    • Multivariate Data Analysis

    Data Engineering and Databases

    • Advanced SQL and relational databases
    • NoSQL databases: MongoDB, Cassandra
    • Data pipelines and ETL: collection, transformation, loading
    • Introduction to Big Data: Hadoop, Spark

    Machine Learning and Deep Learning

    • Fundamentals of supervised and unsupervised machine learning
    • Introduction to neural networks and deep learning
    • Python for data science: NumPy, Pandas, Scikit-learn
    • Data visualization: Matplotlib, Seaborn, Tableau

    Applied AI and Business Use Cases

    • Introduction to NLP (Natural Language Processing)
    • Computer vision: image processing and object detection
    • Generative AI: LLMs and business applications
    • Ethics of AI and data regulation

    Management and Project

    • Data project management using agile methods
    • Results communication and data storytelling
    • Technical English for data and AI
    • Multidisciplinary Project in Teams (PPE)

    Technician Internship of at least 4 months (or alternating throughout the year)

    90 ECTS credits/year

    Advanced AI and Model Architectures

    • Advanced deep learning architectures: Transformers, GANs, Diffusion Models
    • MLOps: deployment, monitoring and maintenance of models in production
    • Embedded AI and optimization of models for constrained systems
    • Federated Learning and distributed AI

    Big Data and Advanced Data Engineering

    • Advanced Big Data Architectures: Lambda, Kappa
    • Real-time Data Streaming: Apache Kafka, Flink
    • Cloud Computing and AI Platforms: Azure, AWS, GCP
    • Data Mesh and Data Governance at Scale

    AI for Strategic Sectors

    • AI for Finance, Healthcare, and Industry 4.0
    • Recommendation and Personalization through Algorithms
    • Predictive Analytics and Forecasting Models
    • Advanced Computer Vision for Industry

    Compliance, Ethics, and Governance

    • GDPR, AI Act, and European Regulatory Frameworks
    • Data Governance and Data Quality Management
    • Security and Data Protection in AI Pipelines

    Innovation and Minor

    • Elective Minor: Cross-disciplinary in Digital Technologies
    • Deepening Option: Growing Sector or Cutting-edge Expertise
    • Valorization of Student Projects (VPE) – IDEFI Program
    • Final Year Project (PFE)

    Engineering internship – minimum 6 months  (or work-study)

    Mandatory international academic stay (if not completed in the first year of the Engineering program)

    Evaluation Methods

    • End-to-end data projects, from business framing to deployment in production.
    • Practical work on real datasets: training models, analysis, and visualization.
    • Multidisciplinary Project in Team (PPE) and Final Year Project (PFE).
    • A minimum 6-month engineering internship, or a work-study program, with a report and defense before a jury.

    Certified by the State

    Validated skills

    Prepares for employment

    • Engineering internship of at least 4 months (initial schedule) or a work-study program during the 2nd year of the engineering program

    • A 6-month internship or a work-study program during the third year of an engineering program, culminating in a final engineering project.

    • Work on real-world cases through the Intelligence Lab, France's first hub dedicated to generative AI (Source: ECE – https://www.ece.fr/intelligence-lab/).

    • Work on real-world cases through the Intelligence Lab, France's first hub dedicated to generative AI (Source: ECE – https://www.ece.fr/intelligence-lab/).

      Étudiante du programme Ingénieurs de l’école d’ingénieurs ECE se tenant fièrement face caméra, habillée en tenue professionnelle
      • Portrait photo of Apolline Chartier-Kastler

        The ECE program allowed me to build a solid foundation in engineering while specializing in a field I’m passionate about: data and AI. There, I developed skills in data analysis and artificial intelligence that I use today in my career. The projects I worked on during my time at the ECE were also very beneficial, with a hands-on approach that prepared me well for the professional world.

        Apolline Chartier-KastlerData Specialist
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          Your engineering school in Paris

          Discover our engineering school in Paris: a campus in the heart of the capital, training in AI and Data for a unique student life.

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          Your engineering school in Marseille

          Brand new and fully designed for new educational practices, our campus welcomes you in an ideal setting for your studies.

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          Your engineering school in Rennes

          Just a stone's throw from the TGV train station, our 3,000 m² campus offers you a warm and functional setting designed for studying in the best conditions.

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        • 07/10

          Soirée Portes Ouvertes – ECE – Marseille – mercredi 7 octobre 2026

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          • From 18h00 to 0h00
        • 19/10

          Immersion in Marseille

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          • From 10h00 to 17h00
        • 28/11

          Matinée Portes Ouvertes – ECE – Marseille – samedi 28 novembre 2026

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          Soirée Portes Ouvertes – ECE – Marseille – mercredi 16 décembre 2026

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