Block 5: Advanced Scientific Python

Introduction

Already comfortable with the basics from block 3 ? In Part 2 (Advanced), we go beyond syntax and focus on writing reliable, reusable code for real research projects.

What you’ll learn in Part 2 (Advanced):

• Organizing code with functions, classes, modules, and simple packaging
• Using virtual environments and dependency management for reproducibility
• Working effectively with Jupyter/Marimo notebooks (structure, pitfalls, exports)
• Data wrangling with pandas/polars and scientific workflows with SciPy
• Plotting at scale and saving publication‑ready figures
• Share devcontainer setups for consistent environments across team members
• Time will be spent answering your specific questions and applying concepts to your research

By the end, you’ll have a robust, efficient workflow that scales from exploratory notebooks to reusable research code.

Program Outline

• Code Organization and Reusability
  • Functions and classes
  • Modules and simple packaging
• Environment and Dependency Management
  • Virtual environments (conda/mamba)
  • Managing dependencies with pip and YAML files.
• Advanced Jupyter/Marimo Usage
  • Notebook structure and best practices
  • Exporting notebooks to scripts and reports
• Data Wrangling with pandas/polars
  • DataFrames and Series
  • Data cleaning and transformation
• Scientific Computing with SciPy
  • Optimization
  • ODE integration
  • … and more
• Plotting and Visualization
  • Plotting with Matplotlib and Seaborn
  • Saving publication‑ready figures
• Collaborative Development with Devcontainers
  • Setting up devcontainers for consistent environments
  • Sharing setups with team members

Prerequisites

See block 3 for details on the prerequisites.