Taught Modules 2025/26

Applied Statistics, Data Science & AI

R1.10-Compter Tools

Student workload: 6 hours of practical sessions. Classes cover: scientific writing (basic and advanced), spreadsheet use and analysis, pivot tables and charts, CSV import and data cleaning, and the Pix certification pathway.

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    • L3GM15-Digital Literacy and Skills (Pix Certification)

    Student workload: 24 hours of practical sessions. Classes cover: this course develops students’ digital literacy and data skills in an economic context, aligned with the Pix certification framework. Through practical sessions, students learn to use tools such as Google Colab, Markdown, and Python for data acquisition, cleaning, analysis, and visualisation. The programme covers structured, semi-structured, and unstructured data, the use of generative AI for prompt design and automated reporting, and the production of professional-quality economic reports. Emphasis is placed on accuracy, clarity, and the critical validation of results.

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    • R5-SAB.09-Statistical and Computing Tools

    This course equips students with statistical and computing skills tailored to biological and environmental sciences, with a particular focus on applications in food and biomolecule production. It introduces methods for collecting, organising, and analysing experimental data, combining descriptive and inferential statistics with appropriate software tools. Practical work includes the use of spreadsheets, statistical packages, and programming environments to manage datasets, perform analyses, and produce clear visual representations. Special emphasis is placed on the implementation of Statistical Process Control (SPC) to monitor and optimise production processes, ensuring quality and compliance. The focus is on selecting suitable methods, ensuring accuracy, and interpreting results in an applied scientific and industrial context.

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    • R1.9-Statistics

    Student workload: 12 hours of practical sessions. Classes cover: this course introduces fundamental concepts and methods in statistics for application in science and technology. Students learn how to collect, organise, and analyse data, using both descriptive and inferential statistical techniques. The teaching combines theoretical foundations with practical exercises, enabling students to interpret results critically and apply statistical tools to real-world datasets.

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