Description

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 – Arrays:

   what is an array data structure
   arrays related interview questions

Section 3 – Linked Lists:

   linked list data structure and its implementation
   doubly linked lists
   linked lists related interview questions

Section 4 – Stacks and Queues:

   stacks and queues
   stack memory and heap memory
   how the stack memory works exactly?
   stacks and queues related interview questions

Section 5 – Binary Search Trees:

   what are binary search trees
   practical applications of binary search trees
   problems with binary trees

Section 6 – Balanced Binary Trees (AVL Trees and Red-Black Trees):

   why to use balanced binary search trees
   AVL trees
   red-black trees

Section 7 – Priority Queues and Heaps:

   what are priority queues
   what are heaps
   heapsort algorithm overview

Section 8 – Hashing and Dictionaries:

   associative arrays and dictionaries
   how to achieve O(1) constant running time with hashing

Section 9 – Graph Traversal:

   basic graph algorithms
   breadth-first
   depth-first search
   stack memory visualization for DFS

Section 10 – Shortest Path problems (Dijkstra’s and Bellman-Ford Algorithms):

   shortest path algorithms
   Dijkstra’s algorithm
   Bellman-Ford algorithm
   how to detect arbitrage opportunities on the FOREX?

Section 11 – Spanning Trees (Kruskal’s and Prim’s Approaches):

   what are spanning trees
   what is the union-find data structure and how to use it
   Kruskal’s algorithm theory and implementation as well
   Prim’s algorithm

Section 12 – Sorting Algorithms

   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!
Who this course is for:

   Beginner Python developers curious about graphs, algorithms and data structures

Requirements

   Python basics
   Some theoretical background ( big O notation )

Last Updated 7/2021