Module 2: Introduction to low-code Python programming
Lesson 2.1: AI-aided and low-code programming
AI-aided content analysis of sustainability communication
Open Science Methods
- Emphasizes reproducibility and transparency in research.
- Encourages collaboration among global research communities.
- Relies on sharing data, code, and methodologies openly.
- Supported by open-source tools and platforms.
- Enhances scientific integrity and innovation.
Python Environments: Local, Cloud, Notebooks
- Local environments require Python installation and configuration.
- Cloud environments like Google Colab eliminate setup hurdles.
- Notebooks provide interactive code execution and documentation.
- Cloud environments offer scalability and resource management.
- Notebooks support real-time code execution alongside markdown.
The Concept of Jupyter Notebooks
- Enables interactive, step-wise execution of code.
- Integrates code, text, and visualizations within one document.
- Ideal for iterative development, debugging, and documentation.
- Supports educational and research purposes effectively.
- Contrasts with traditional script-based development environments.
Google Colab Notebook Interface
- Combines a cloud-hosted Jupyter notebook with GPU access.
- Features cells for executing code and writing text.
- Auto-saves progress and links to Google Drive for file storage.
- Supports collaboration and sharing with other users.
- Requires no local setup, ideal for rapid project development.
What Is Programming and Python Programming
- Programming involves writing instructions for computers to execute.
- Python is a high-level, versatile language known for readability.
- Widely used in web development, data science, and automation.
- Python’s extensive libraries simplify complex tasks.
- Popular for both beginner learning and professional development.
Python Use Cases: Low vs. High-Code
- Low-code leverages Python’s simplicity for fast data analysis.
- High-code applications involve deeper customization and algorithms.
- Social sciences benefit from low-code data manipulation and visualization.
- Computer science projects often require complex, high-code solutions.
- Python accommodates both low-code and high-code workflows.
