Algorithms and Data Structures in Python (INTERVIEW Q&A)

Algorithms and Data Structures in Python (INTERVIEW Q&A) | Udemy
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Genre: eLearning

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

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 – 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

Section 13 – 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

Section 14 – 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

Section 15 – Algorithms Analysis

how to measure the running time of algorithms

running time analysis with big O (ordo), big Ω (omega) and big θ (theta) notations

complexity classes

polynomial (P) and non-deterministic polynomial (NP) algorithms

O(1), O(logN), O(N) and several other running time complexities

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