Module 1 - Foundations of Python Coding¶

This module introduces the essential concepts of Python programming and the foundational practices needed for scientific software development. Participants learn how to set up a working environment, write clear Python code, use notebooks and scripts, process data, and organize small projects in a way that can grow later.

Learning outcomes¶

By the end of this module, participants will be able to:

• Set up a working Python environment using Miniforge3 and VS Code.
• Understand and use Python syntax, data structures, and control flow.
• Write and execute simple scripts and notebooks.
• Use core scientific libraries such as NumPy, pandas, and matplotlib.
• Follow basic best practices for readability, reproducibility, and structure.
• Apply introductory object-oriented programming concepts.
• Manage dependencies in virtual environments with conda, venv, and pip.

Study pages¶

Use the pages linked below as the student-facing handbook for this module:

• Module 1 study guide
• Getting ready: Python environment and tools
• Python basics
• Control flow and data structures
• Working with files and data
• Code quality and good practices
• Introduction to object-oriented programming
• Project organization for small Python projects

Sections and timing¶

1. Quick welcoming to the course and module (15min)
2. Setting up the Python and Development Environment (1h-1h15min)
   • Installing VS Code, Python via Miniforge3, and checking GitHub accounts
   • Configuring VS Code for Python development
   • Creating and managing virtual environments (conda, venv)
   • Jupyter Notebook introduction
   • (Optional) Quiz
3. Introduction to Python (45min)
   • What is Python?
   • The interactive Python terminal
   • Variables and naming conventions
   • Basic data types: string, integer, float, boolean, complex
   • Expressions and operators
   • (Optional) Quiz
4. Core syntax and control flow (1h30min)
   • Functions
   • Conditional statements (if / elif / else)
   • Data structures: lists, tuples, dictionaries, sets
   • Iteration (for and while loops)
   • List comprehensions
   • Recursion
   • Error and exception handling
   • (Optional) Quiz
5. Working with Files and Data (1h30min)
   • Basic input/output (I/O) file operations: text, CSV, and JSON
   • Numerical arrays and vectorized operations (numpy)
   • Visualizing data (matplotlib)
   • Structured data handling (pandas)
   • Managing file paths (os, pathlib)
   • (Optional) Numerical methods in Python (scipy)
   • (Optional) Quiz
6. Code Quality (45min)
   • Reproducible research code
   • Python style conventions (PEP 8)
   • Writing clear docstrings and comments
   • Introduction to Python typing and style checks (mypy, ruff)
   • Basic logging and informative error messages
   • Introduction to testing
   • (Optional) Quiz
7. Introduction to Object-Oriented Programming (OOP) (1h30min)
   • Classes, attributes, and methods
   • Abstraction
   • Naming conventions and design
   • SOLID principles vs. functional programming
   • (Optional) Quiz
8. Teaser to Project Organization (30min)
   • Recommended folder structure
   • Introduction to templates
   • Naming and modularization
   • (Optional) Quiz

Participants' projects¶

After the last session, we will organize an additional 2-hour session for all participants. The goal is to give everyone uninterrupted time to work on their own projects with support from experts and peers.