Algorithms and Data Structures in Python (INTERVIEW QA)

Algorithms and Data Structures in Python (INTERVIEW Q&A), available at $99.99, has an average rating of 4.62, with 213 lectures, 44 quizzes, based on 4278 reviews, and has 28849 subscribers.

You will learn about 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 Dijkstras approach or Bellman-Ford method Understand minimum spanning trees (Primss 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 ideal for individuals who are Beginner Python developers curious about graphs, algorithms and data structures It is particularly useful for Beginner Python developers curious about graphs, algorithms and data structures.

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

Summary

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

Price: $99.99

Average Rating: 4.62

Number of Lectures: 213

Number of Quizzes: 44

Number of Published Lectures: 209

Number of Published Quizzes: 44

Number of Curriculum Items: 257

Number of Published Curriculum Objects: 253

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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 Dijkstras approach or Bellman-Ford method

Understand minimum spanning trees (Primss 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

Who Should Attend

Beginner Python developers curious about graphs, algorithms and data structures

Target Audiences

Beginner Python developers curious about graphs, algorithms and data structures

This course is about data structures, algorithms and graphs. We are going to implement the problems in Python programminglanguage. 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Complexity theory basics

Chapter 2: Installation and Environment Setup

Lecture 1: Installing Python

Lecture 2: Installing PyCharm

Chapter 3: ###?DATA STRUCTURES ###

Lecture 1: Why do we need data structures?

Lecture 2: Data structures and abstract data types

Chapter 4: Data Structures – Arrays

Lecture 1: What are array data structures?

Lecture 2: What are lists in Python?

Lecture 3: Arrays introduction – operations

Lecture 4: Lists in Python

Lecture 5: Lists in Python – advanced operations

Lecture 6: Lists in Python – list comprehension

Chapter 5: Interview Questions – (Arrays)

Lecture 1: Reversing an array in-place solution

Lecture 2: Palindrome problem solution

Lecture 3: Integer reversion problem solution

Lecture 4: Anagram problem solution

Chapter 6: Data Structures – Linked Lists

Lecture 1: What are linked lists?

Lecture 2: Linked list introduction – operations

Lecture 3: Pros and cons of linked lists

Lecture 4: Linked list implementation I

Lecture 5: Linked list implementation II

Lecture 6: Linked list implementation III

Lecture 7: Revisiting remove operation

Lecture 8: Comparing linked lists and arrays

Lecture 9: Practical (real-world) applications of linked lists

Chapter 7: Data Structures – Doubly Linked Lists

Lecture 1: What are doubly linked lists?

Lecture 2: Doubly linked list implementation

Lecture 3: Running time comparison: linked lists and arrays

Chapter 8: Interview Questions (Linked Lists)

Lecture 1: Finding the middle node in a linked list solution

Lecture 2: Reverse a linked list in-place solution

Chapter 9: Data Structures – Stacks

Lecture 1: What are stacks?

Lecture 2: Stacks in memory management (stacks and heaps)

Lecture 3: Stack memory visualization

Lecture 4: Stack implementation

Lecture 5: Practical (real-world) applications of stacks

Chapter 10: Data Structures – Queues

Lecture 1: What are queues?

Lecture 2: Queue implementation

Chapter 11: Interview Questions (Stacks and Queues)

Lecture 1: Max in a stack problem overview

Lecture 2: Max in a stack problem solution

Lecture 3: Queue with stack problem

Lecture 4: Queue with stack problem solution

Lecture 5: Queue with stack problem solution – recursion

Chapter 12: Data Structures – Binary Search Trees

Lecture 1: What are binary search trees?

Lecture 2: Binary search trees theory – search, insert

Lecture 3: Binary search trees theory – delete

Lecture 4: Binary search trees theory – in-order traversal

Lecture 5: Pros and cons of binary search trees

Lecture 6: Binary search tree implementation I

Lecture 7: Binary search tree implementation II

Lecture 8: Stack memory visualization – finding max (min) items

Lecture 9: Stack memory visualization – tree traversal

Lecture 10: Binary search tree implementation III – remove operation

Lecture 11: Practical (real-world) applications of trees

Chapter 13: Interview Questions (Binary Search Trees)

Lecture 1: Compare binary trees solution

Chapter 14: Data Structures – AVL Trees

Lecture 1: Motivation behind balanced binary search trees

Lecture 2: What are AVL trees?

Lecture 3: AVL trees introduction – height

Lecture 4: AVL trees introduction – rotations

Lecture 5: AVL trees introduction – illustration

Lecture 6: AVL tree implementation I

Lecture 7: AVL tree implementation II

Lecture 8: AVL tree implementation III

Lecture 9: AVL tree implementation IV

Lecture 10: AVL tree implementation V

Lecture 11: Practical (real-world) applications of balanced binary search trees

Chapter 15: Data Structures – Red-Black Trees

Lecture 1: What are red-black trees?

Lecture 2: The logic behind red-black trees

Lecture 3: Red-black trees – recoloring and rotation cases

Lecture 4: Red-black tree illustrations

Lecture 5: Red-black tree implementation I

Instructors

Holczer Balazs
Software Engineer

Rating Distribution

1 stars: 92 votes

2 stars: 150 votes

3 stars: 577 votes

4 stars: 1506 votes

5 stars: 1952 votes

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