Algorithmic Thinking with Python: Foundations

Introduction

• Importance of algorithmic thinking
• What you should know
• Use GitHub Codespaces with this course

1. Warm Up

• Challenge: 100 doors
• Solution: 100 doors
• FizzBuzz

2. Tools to Help Understand the Flow of Algorithms

• Tracing algorithms using an online visualization tool
• Tracing algorithms using code or a debugger
• Algorithm animations
• Pseudocode
• Using a whiteboard to explore algorithms

3. Brute Force Algorithms

• Introduction to brute force algorithms
• Linear search
• Selection Sort introduction
• Challenge: Selection Sort in Python
• Solution: Selection Sort in Python

4. Analysis of Time-Space Complexity

• Introduction to analysis of time-space complexity
• Challenge: Big-O notation practice
• Solution: Big-O notation practice
• Examples of time complexity with Python
• Memory considerations when implementing algorithms

5. Greedy Algorithms

• Introduction to greedy algorithms
• Introduction to the change making problem
• Solution to the change making problem
• Dijkstra's algorithm
• Challenge: Dijkstra's algorithm
• Solution: Dijkstra's algorithm
• Dijkstra's algorithm: Python implementation

6. Decrease and Conquer

• Ferrying soldiers
• Introduction to decrease and conquer
• Binary search
• Challenge: Binary search
• Solution: Binary search

Conclusion

• Exploring algorithmic thinking with Python