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5.00 credits
22.5 h + 30.0 h
Q2
Language
French
Prerequisites
This course is intended for university students with no prerequisites in computer science or mathematics but who are curious about data science, digitalisation, or information analysis.
Prior knowledge of computer science, programming, statistics or data processing is therefore not necessary.
Prior knowledge of computer science, programming, statistics or data processing is therefore not necessary.
Main themes
This course aims to introduce students to programming (in Python) from a data processing perspective. It will enable them to:
• Understand the fundamental principles of programming and read, understand, modify and create simple programmes;
• Manipulate data structures such as lists, dictionaries and sets;
• Design basic algorithms for data processing;
• Process textual, tabular or structured data;
• Interact with simple databases;
• Graphically represent data (histograms, curves, etc.).
• Understand the fundamental principles of programming and read, understand, modify and create simple programmes;
• Manipulate data structures such as lists, dictionaries and sets;
• Design basic algorithms for data processing;
• Process textual, tabular or structured data;
• Interact with simple databases;
• Graphically represent data (histograms, curves, etc.).
Learning outcomes
At the end of this learning unit, the student is able to : | |
| By the end of the course, students should be able to: 1. Understand and write Python programmes using:
3. Write simple data processing algorithms:
6. Use libraries (Python modules) to:
|
|
Teaching methods
• Interactive lectures with live demonstrations.
• Introduction to programming to introduce algorithmic reasoning.
• Supervised practical work in the computer lab.
• Exploration of how to use AI language models to understand, generate, or improve simple Python code.
• Individual exercises on an online platform.
• Introduction to programming to introduce algorithmic reasoning.
• Supervised practical work in the computer lab.
• Exploration of how to use AI language models to understand, generate, or improve simple Python code.
• Individual exercises on an online platform.
Evaluation methods
• Formative assessment:
o Multiple-choice questions or short open-ended questions online on concepts and syntax.
o Weekly exercises on an online learning platform.
o Multiple-choice exam (theoretical and practical) on the concepts covered in class.
• Summative assessment:
o An individual or pair project comprising:
o Creation of a documented programme or notebook.
o Analysis of a dataset (CSV or SQLite database).
o Visualisation of results.
o Presentation and peer review.
o Multiple-choice questions or short open-ended questions online on concepts and syntax.
o Weekly exercises on an online learning platform.
o Multiple-choice exam (theoretical and practical) on the concepts covered in class.
• Summative assessment:
o An individual or pair project comprising:
o Creation of a documented programme or notebook.
o Analysis of a dataset (CSV or SQLite database).
o Visualisation of results.
o Presentation and peer review.
Faculty or entity
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Bachelor in Geography : General
