Computer Literacy and Scientific Computing for Science Students
These courses introduce science students to computing as a practical intellectual tool: understanding digital environments, using software responsibly, and applying Python to scientific calculations.
Course Context
This family includes introductory computing courses for biology and earth-science students, together with the Physics L2 course on applied scientific computing and programming in Python. The objective is not to turn every learner into a software engineer, but to give science students enough conceptual and practical fluency to use computing responsibly in their scientific training.
| Years | Role | Institution | Track / level | Courses documented |
|---|---|---|---|---|
| 2022-2026 | Course lecturer / main instructor | University of Dschang, Faculty of Science | Biochemistry, Animal Biology, Plant Biology and Earth Sciences; mainly L1 | Informatique / Introduction to Computer Science, including BIO162 and STE152 variants |
| 2022-2025 | Course lecturer / main instructor | University of Dschang, Faculty of Science | Physics, L2 | Introduction to Scientific Computing and Programming in Python |
| Representative labels | Teaching focus |
|---|---|
| Introduction to Computer Science | Digital environments, data, files, software tools, and basic computational thinking. |
| Computer Science for Biology / Earth Sciences | Use of computing concepts and tools in scientific contexts. |
| Applied Scientific Computing in Python | Numerical calculations, scientific formulas, simple numerical methods, and Python scripts for physics-oriented problem solving. |
Main Notions
- Basic computer architecture, operating systems, file organization, and digital work habits.
- Representation of information: text, numbers, tables, files, and structured data.
- Introductory algorithmic reasoning through everyday and scientific problems.
- Python programming for scientific calculations: variables, expressions, conditions, loops, functions, arrays or lists, and reusable scripts.
- Elementary numerical methods and scientific-computing examples such as approximation, iteration, root finding, and data preparation.
How the Course Runs
The course usually alternates short conceptual explanations with guided exercises. Students are progressively moved from tool usage to reasoning: they learn not only which button to click or which command to write, but why a representation, a procedure, or a computation is appropriate.
- Guided practice: students reproduce small operations before adapting them to new examples.
- Scientific examples: calculations, tables, measurements, and basic data processing are used to keep the course close to their disciplinary context.
- Progressive abstraction: learners move from concrete software actions to algorithmic descriptions and simple programs.
Competencies Developed
- Work confidently in a digital environment and manage files, documents, and data responsibly.
- Translate a simple scientific or administrative task into a clear sequence of operations.
- Read and write elementary algorithms or programs.
- Use computing as a support for scientific reasoning, not only as an office tool.
- Develop the autonomy needed to continue learning digital tools beyond the classroom.