Udemy – Algorithms and Data Structures in Python (INTERVIEW Q&A) 2022-11 – Downloadly

Description

Algorithms and Data Structures in Python Course (INTERVIEW Q&A). This course is about data structure, algorithms and graphs. We aim to implement the problems in Python programming language. I highly recommend you to enter these data structures and algorithms by yourself several times to get a good understanding of them. In the first part of the course, we will be introduced to basic data structures like linked lists, stacks, queues, binary search trees, heaps and some advanced structures like AVL trees and red-black trees. About graph algorithms like spanning trees, shortest path algorithms and graph traversal. We will try to optimize each data structure as much as possible. In each chapter, I will talk about the theoretical background of each algorithm or data structure, then we will write the code step by step in Python. Many advanced algorithms rely heavily on these topics, so it is definitely worth understanding the basics. These principles can be used in several areas: in investment banking, in artificial intelligence or in e-trading algorithms on the stock exchange. Research institutions mainly use Python as a programming language: many libraries are publicly available, from machine learning to complex networks.

What you will learn in the course Algorithms and Data Structures in Python (INTERVIEW Q&A).

Understand arrays and linked lists
Understand stacks and queues
Understanding tree-like data structures (binary search trees)
Understanding equilibrium trees (AVL trees and red-black trees)
Understanding heap data structures
Understand hashing, hash tables and dictionaries
Understand the difference between data structures and abstract data types
Basics of graph traversal (BFS and DFS)
Understand shortest path algorithms such as the Dijkstra approach or the Bellman-Ford method
Basics of Minimum Spanning Trees (Prims algorithm)
Understanding sorting algorithms
Be able to develop your own algorithms
You have a good understanding of algorithmic thinking
Be able to identify and correct inefficient code fragments