Programming Data Structures and Algorithms using Python - Introduction and Jupyter Notebooks

Overview

• Welcome to the course on programming data structures and algorithms using Python.
• Refreshing on Python basics and introducing the programming environment: Jupyter Notebooks.

Traditional Environments for Running Python Code

Text Editor and Console

• Text Editor and Console: Edit code in a text editor, then load and run it in a console.
  • Pros: Simple to use.
  • Cons: Tedious cycle of editing and running, having to reload after every change.

Integrated Development Environment (IDE)

• IDE (e.g., Repl.it): Combine code editing and running in one interface.
  • Pros: Easier to see changes and outputs immediately, includes debuggers, test case preparation.
  • Cons: Still limited in terms of collaboration and documentation.

Introduction to Jupyter Notebooks

Need for Another Tool

• Enhances collaboration, sharing of code and results, and documentation integration.

Structure of Jupyter Notebooks

• Notebook Interface: Combines code and text cells.
  • Code Cells: Type and run code.
  • Text Cells: Format text using Markdown.
  • Output Section: Shows the output of the executed code.
• Benefits:
  • Interleaving documentation with code for better readability.
  • Flexibility to update and rerun code without losing previous versions.
  • Ability to preserve and share outputs for evaluation and collaboration.

Dynamic Editing and Rerunning

• Comparable to a spreadsheet: Dynamic content changes, recomputes values based on updated cells.
• Markdown: Simple formatting for text cells, enhancing the documentation quality.

The Jupyter Project and Its Languages

• Origin of Name: Jupyter is derived from Julia, Python, and R.
• Multi-language Support: Works with various programming languages, not limited to Python.

Popularity and Usage in Machine Learning

• Extensive Use in ML:
  • Popular for machine learning projects and competitions (e.g., Kaggle).
  • Code and documentation sharing for collaborative work.
• Achievements: Won the ACM Software Systems Award in 2017.

Using Google Colab

Overview

• Google Colab: A publicly available, free, cloud-based Jupyter notebook environment.
  • Pros: Pre-loaded with machine learning packages (e.g., scikit-learn, TensorFlow), access to GPUs for heavy computations.
  • Cons: Different look and feel compared to standalone Jupyter notebooks but functionally similar.

Benefits for this Course

• Convenient for developing, saving, and sharing Python code.
• Enhances the ability to collaborate and distribute class materials efficiently.
• Configured for machine learning with access to advanced computing resources.

Summary

• Jupyter Notebooks: Chosen for its convenient, collaborative, and documentation-friendly environment.
• Google Colab: The platform we'll be using due to its free access and machine learning configurations.
• Overall benefit: Easier editing, saving, sharing, collaboration, and enhanced learning experience.

[Music fades]