Taught Modules 2025/26

Applied Statistics, Data Science & AI

FR-CCN-OI1-GB1S1 — Computing tools

The objective is to acquire digital literacy and a mastery of the main computing tools, enabling students to work optimally within a professional context.

  • Format:

    • Ressources

    FR-CCN-OI-WS1

    application/pdf270,5 KB

    FR-CCN-ST1-GB1S1 — Statistical and Computing Tools 1

  • Format:

    • Ressources

    GB1S1-STA1-WS1

    application/pdf308,6 KB

    GB1S1-DATA-1.1

    text/csv22,8 MB

    GB1S1-STA1-WS2

    application/pdf258,8 KB

    GB1S1-DATA-2.1

    text/csv16,7 MB

    FR-CCN-ST4-GB3S1 — Statistical and Computing Tools 4

    The objective is to observe how, based on simple experimental measurements, the mechanisms of natural variability and progressive drift can be highlighted within a real-world agri-food production context.

  • Format:

    • Ressources

    soluce

    application/pdf2,2 KB

    GB3S1-WS-1

    application/pdf305 B

    GB3S1-WS-2

    application/pdf291,0 KB

    GB3S1-DATA-1

    text/csv819,3 KB

    GB3S1-DATA-2

    text/csv262,7 KB

    GB3S1-WS-TP1

    application/pdf281,3 KB

    FR-CCN-L3GMS1 — Digital Culture and Skills (PIX Certification)

    This course prepares Economics/Management students for data analysis using Python (pandas, matplotlib) within the Google Colab environment. It covers the reading, exploration, and visualisation of economic datasets (INSEE, Eurostat). The sessions also address the processing of unstructured data. A portion of the course is dedicated to Prompt Engineering and the use of generative AI to produce and validate economic analyses. The final objective is the professional structuring of economic reports and sectoral analysis based on concrete case studies.

  • Format:

    • Ressources

    L3GMS1-WS-5

    application/pdf286,1 KB

    L3GMS1-DATA-5

    text/csv3 B