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Free download — Algorithms and Data Structures in Python (INTERVIEW Q&A)
A guide to implement data structures, graph algorithms and sorting algorithms from scratch with interview questions!
What you’ll learn
Understand arrays and linked lists
Understand stacks and queues
Understand tree like data structures (binary search trees)
Understand balances trees (AVL trees and red-black trees)
Understand heap data structures
Understand hashing, hash tables and dictionaries
Understand the differences between data structures and abstract data types
Understand graph traversing (BFS and DFS)
Understand shortest path algorithms such as Dijkstra’s approach or Bellman-Ford method
Understand minimum spanning trees (Prims’s algorithm)
Understand sorting algorithms
Be able to develop your own algorithms
Have a good grasp of algorithmic thinking
Be able to detect and correct inefficient code snippets
Some theoretical background ( big O notation )
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?
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
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
stack memory visualization for DFS
Section 10 – Shortest Path problems (Dijkstra’s and Bellman-Ford Algorithms):
shortest path algorithms
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
Section 12 – 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!
About: Algorithms and Data Structures in Python (INTERVIEW Q&A)
|File Name:||Algorithms and Data Structures in Python (INTERVIEW Q&A)|
|Genre / Category:||Free Course|
|File Size :||4.1GB|
|Updated and Published:||OCTOBER 18, 2021|
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