Advanced Algorithms and Data Structures in Python


Fenwick trees, Caches, Splay Trees, Prefix Trees (Tries), Substring-Search Algorithms and Travelling Salesman Problem

What you’ll learn

  • Have a good grasp of algorithmic thinking
  • Be able to develop your own algorithms
  • Be able to detect and correct inefficient code snippets
  • Understand Fenwick trees
  • Understand caches (LRU caches and Splay Trees)
  • Understand tries and ternary search trees
  • Understand substring search algorithms (Rabin-Karp method, KMP algorithm and Z algorithm)
  • Understand the Hamiltonian cycle problem (and travelling salesman problem)
  • Understand Eulerian cycle problem


  • Python basics
  • Some theoretical background (big O notation )


This course is for those who are interested in computer science and want to implement the algorithms and given data structures in Python. In every chapter you will learn about the theory of a given data structure or algorithm and then you will implement them from scratch.

Chapter 1: Binary Indexed Trees (Fenwick Trees)

  • theory behind the binary indexed tree or Fenwick tree data structure
  • how to use this data structure in computer vision and artificial intelligence
  • implementation in Python

Chapter 2: LRU Caches

  • what are caches and why are they so important
  • how to use doubly linked lists to implement caches
  • theory behind LRU caches
  • implementation in Python

Chapter 3: Splay Trees

  • what are splay trees
  • how to achieve caches with splay trees

Chapter 4: B-Trees

  • external memory and internal memory (RAM)
  • data structures for the external memory
  • trees with multiple children and multiple keys
  • what are B-tree data structures?

Chapter 5: Prefix Trees (Tries)

  • what are tries or prefix trees
  • real world applications of tries
  • autocomplete feature of tries
  • sorting with tries
  • IP routing

Chapter 6: Ternary Search Trees

  • what are ternary search trees
  • boggle game with tries

Chapter 7: Substring Search Algorithms

  • what are substring search algorithms and why are they important in real world softwares
  • brute-force substring search algorithm
  • hashing and Rabin-Karp method
  • Knuth-Morris-Pratt substring search algorithm
  • Z substring search algorithm (Z algorithm)
  • implementations in Python

Chapter 8: Topological Ordering

  • what is topological ordering (topological sort)?
  • topological ordering implementation with depth-first search

Chapter 9: Cycle Detection

  • how to detect cycles in graphs?

Chapter 10: Strongly Connected Components (Tarjan’s Algorithm)

  • what are strongly connected components?
  • Tarjan’s algorithm with depth-first search

Chapter 11: Hamiltonian cycles (Travelling Salesman Problem)

  • Hamiltonian cycles in graphs
  • what is the travelling salesman problem?
  • how to use backtracking to solve the problem
  • meta-heuristic approaches to boost algorithms

Chapter 12: Eulerian Cycles (Chinese Postman Problem)

  • Eulerian cycles in graphs
  • what is the chinese postman problem?

Thanks for joining my course, let’s get started!

Who this course is for:

  • This course is suited for anyone who has some basic knowledge in Python and interested in algorithms and data structures

Created by Holczer Balazs
Last updated 1/2022
English [Auto]

Size: 2.10 GB

Google Drive Links

Download Part 1 | Download Part 2 | Download Part 3

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1 Comment
  1. daniel says

    pls seed

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