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Algorithms and Data Structures in Python

A guide to implement the most up to date algorithms from scratch: arrays, linked lists, graph algorithms and sorting
Holczer Balazs
17,043 students enrolled
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Understand arrays and linked lists
Understand stacks and queues
Understand the differences between data structures and abstract data types
Understand tree like data structures (binary search trees)
Understand balances trees (AVL trees and red-black trees)
Understand graph traversing (BFS and DFS)
Understand shortest path algorithms such as Dijkstra's approach
Understand minimum spanning trees
Be able to develop your own algorithms
Have a good grasp of algorithmic thinking
Be able to detect and correct inefficient code snippets

This course is about data structures, algorithms and graphs. We are going to implement the problems in Python programming language. I highly recommend typing out these data structures and algorithms several times on your own in order to get a good grasp of it.

So what are you going to learn in this course?

Section 1:

  • setting up the environment

  • differences between data structures and abstract data types

Section 2:

  • what is an array data structure

  • arrays related interview questions

  • linked list data structure and its implementation

Section 3:

  • stacks and queues

  • stacks and queues related interview questions

Section 4:

  • what are binary search trees

  • practical applications of binary search trees

Section 5:

  • problems with binary trees

  • balanced trees: AVL trees and red-black trees

Section 6:

  • what are heaps

  • heapsort algorithm overview

Section 7:

  • associative arrays and dictionaries

  • how to achieve O(1) constant running time with hashing

  • ternary search trees as associative arrays

Section 8:

  • basic graph algorithms

  • breadth-first and depth-first search

Section 9:

  • shortest path algorithms

  • Dijkstra’s algroithm

  • Bellman-Ford algorithm

Section 10:

  • what are spanning trees

  • what is the union-find data structure and how to use it

  • Kruskal’s algorithm theory and implementation as well

Section 11:

  • sorting algorithms

  • bubble sort, selection sort and insertion sort

  • quicksort and merge sort

  • non-comparison based sorting algorithms

  • counting sort and radix sort

In the first part of the course we are going to learn about basic data structures such as linked lists, stacks, queues, binary search trees, heaps and some advanced ones such as AVL trees and red-black trees.. The second part will be about graph algorithms such as spanning trees, shortest path algorithms and graph traversing. We will try to optimize each data structure as much as possible.

In each chapter I am going to talk about the theoretical background of each algorithm or data structure, then we are going to write the code step by step in Python.

Most of the advanced algorithms relies heavily on these topics so it is definitely worth understanding the basics. These principles can be used in several fields: in investment banking, artificial intelligence or electronic trading algorithms on the stock market. Research institutes use Python as a programming language in the main: there are a lot of library available for the public from machine learning to complex networks.

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

You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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14 hours on-demand video
21 articles
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