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Advanced Algorithmic Thinking with Python

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The need for competent problem solvers has never been greater, and Python has become an important programming language. Because of its clarity and expressiveness, Python is an ideal tool to explore algorithmic thinking. In this course, Robin Andrews explains algorithmic thinking and guides you through puzzles, problems, and theories to help you build and challenge your skills. After a warmup problem, Robin shows you how to use the divide and conquer problem solving technique and the Quicksort algorithm, with puzzles to practice each. He dives into the transform and conquer technique that applies preprocessing to the data for a problem before implementing a solution, with additional puzzles for practice. Robin goes over dynamic programming, both top-down and bottom-up, and gives you problems to practice both theory and implementation. Plus, he introduces and explains hash tables and how you can use them to solve problems in Python.


01 - Introduction
  • 01 - Exploration of algorithmic thinking
  • 02 - Use GitHub Codespaces with this course

  • 02 - 1. Warmup
  • 01 - The two-sum interview problem
  • 02 - The two-sum interview problem solution
  • 03 - Number placement puzzle

  • 03 - 2. Divide and Conquer
  • 01 - Triominoes puzzle
  • 02 - Triominoes puzzle solution
  • 03 - Introduction to divide and conquer
  • 04 - Quicksort introduction
  • 05 - Implementing Quicksort in Python
  • 06 - Challenge Implementing Fibonacci function in Python
  • 07 - Solution Implementing Fibonacci function in Python

  • 04 - 3. Transform and Conquer
  • 01 - Coins on a star puzzle
  • 02 - Coins on a star puzzle solution
  • 03 - Introduction to transform and conquer
  • 04 - Presort for mode finding
  • 05 - Number placement puzzle revisited
  • 06 - Challenge Implement number puzzle solution in Python
  • 07 - Solution Implement number puzzle solution in Python

  • 05 - 4. Dynamic Programming
  • 01 - Introduction to dynamic programming
  • 02 - Top-down dynamic programming example
  • 03 - Bottom-up dynamic programming example
  • 04 - The knapsack problem Theory
  • 05 - The knapsack problem Python implementation
  • 06 - Challenge The knapsack problem
  • 07 - Solution The knapsack problem

  • 06 - 5. Hash Tables
  • 01 - What are hash tables
  • 02 - Python code for hash tables
  • 03 - Python dictionaries
  • 04 - Two-sum problem revisited
  • 05 - Challenge Ransom note
  • 06 - Solution Ransom note

  • 07 - Conclusion
  • 01 - Next steps
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