Master Data Structures And Algorithms In Python

From Basic to Advanced Concepts, Practical Implementation and Problem Solving, Mastery of Data Structures and Algorithm

What you'll learn

Advance Data Structure and Algorithms in Python : arrays, linked lists, stacks, queues, trees, and graphs

Building a strong foundation in computer science fundamentals for efficient problem-solving

Analyzing time and space complexity of algorithms for efficiency

Algorithm design techniques: divide and conquer, dynamic programming, and greedy algorithms

Using algorithmic paradigms such as brute force, backtracking, and heuristics to solve problems efficiently.

Requirements

Basic Programming Knowledge

Description

Python is a powerful and versatile programming language, known for its simplicity and readability. This course will cover the fundamental concepts and techniques for organizing, storing, and manipulating data efficiently using Python.Use coupon code for maximum discount: E074C6A29AA74C582006The course will start with an introduction to basic data structures such as arrays, linked lists, stacks, and queues, and then move on to more complex data structures such as trees and graphs. We will explore how to implement these data structures in Python, as well as how to use them to solve real-world problems.The course will also cover various algorithms such as sorting, searching, and graph traversal, and we will analyze the time and space complexity of these algorithms to determine their efficiency. We will explore algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms, and we will apply these techniques to solve real-world problems.In addition to the core data structures and algorithms, we will also cover topics such as data abstraction, complexity theory, and algorithmic paradigms such as brute force, backtracking, and heuristics. We will explore how to choose the appropriate paradigm for a given problem and how to use it to solve problems efficiently.How data structure and algorithm course help to get placed in top tech companies?A strong foundation in data structures and algorithms is essential for success in top tech companies, as they form the building blocks for software development. Here are some ways in which a data structure and algorithm course can help individuals get placed in top tech companies:Problem-Solving Skills: Data structure and algorithm courses teach problem-solving skills that are essential for success in top tech companies. They provide a framework for approaching complex problems and breaking them down into smaller, more manageable tasks.Efficiency: Top tech companies are always looking for ways to improve the efficiency of their software. Knowledge of data structures and algorithms helps individuals develop efficient programs that can handle large amounts of data quickly and reliably.Competitive Edge: Many top tech companies look for candidates who have a strong foundation in computer science fundamentals. A data structure and algorithm course can provide individuals with a competitive edge when applying for jobs at these companies.Technical Interviews: Technical interviews at top tech companies often focus on data structures and algorithms. A data structure and algorithm course can help individuals prepare for these interviews by giving them the necessary knowledge and practice to succeed.Industry-Relevant Skills: A data structure and algorithm course can provide individuals with industry-relevant skills that are in high demand in top tech companies. These skills can be leveraged to stand out from other candidates and secure a position at a top tech company.Overall, a data structure and algorithm course can help individuals develop the skills and knowledge necessary to succeed in top tech companies. It provides a strong foundation in computer science fundamentals and teaches problem-solving skills that are essential for success in the industry.

Overview

Section 1: Array problems solving techniques with examples

Lecture 1 Time Complexity and Space Complexity Introduction

Lecture 2 Searching Algorithms Introduction & Implementation

Lecture 3 Segregation logic to Sort an array of 0's, 1's and 2's

Lecture 4 Merge Sort Implementation

Lecture 5 Maximum Value in an array of Increasing and Decreasing using Binary Search

Lecture 6 Digit rearrangement method to find next greater number with same set of digits

Lecture 7 Greedy Techniques to find minimum number of platforms

Lecture 8 Techniques to print matrix in spiral order without any extra space

Lecture 9 Count frequencies of array elements in O(n) time complexity

Lecture 10 Linear time approach to solve Stock Buy Sell Problem

Lecture 11 Merge sort method to Count inversion in an array

Lecture 12 Binary search method to find Median of two sorted Array

Lecture 13 Minimum Window Substring

Lecture 14 Search an element in a sorted and rotated array

Lecture 15 Segregation logic to Sort an array of 0's, 1's and 2's (Assigment)

Lecture 16 Techniques to print matrix in spiral order without any extra space (Assignment)

Lecture 17 Count frequencies of array elements in O(n) time complexity (Assignment)

Lecture 18 Remove Duplicate From String (Assignment)

Section 2: Binary Tree

Lecture 19 Binary Tree Traversal Implementation

Lecture 20 Binary Tree to Doubly Linked List Conversion

Lecture 21 Print all the boundary nodes of Binary Tree

Lecture 22 Diameter of Binary tree

Lecture 23 Print nodes at k distance from root

Lecture 24 Find All Nodes Distance K in Binary Tree

Lecture 25 Bottom View of Binary Tree

Lecture 26 Construct Tree from PostOrder

Lecture 27 Spiral Order of Binary Tree

Lecture 28 Print Left View of Binary Tree

Lecture 29 Binary Tree Reverse Level Order Traversal

Lecture 30 Serialize and Deserialize Binary Tree

Section 3: Linked List

Lecture 31 Add Number to Linked List

Lecture 32 Linked List Even and Odd List

Lecture 33 Flattering of LinkedList

Lecture 34 Linked List Palindrome

Lecture 35 Merge Sort for Linked Lists

Lecture 36 Rearrange Linked List

Lecture 37 Reverse K Linked List

Section 4: Heap Sort/Hashing

Lecture 38 Min/Max Heap Implementation

Lecture 39 Heapify operation implementation

Lecture 40 Four Sum Problem

Lecture 41 Median of running data streams problem

Lecture 42 Group Anagrams Together

Lecture 43 Design and implement LRU

Section 5: Recursion & Backtracking Concept and Implementation with Multiple Example

Lecture 44 Knight Walk Problem

Lecture 45 N Queen Problem

Lecture 46 Print all Permutations of a given String

Lecture 47 Print all possible words from phone digits

Lecture 48 Recursion & Backtracking Concept and Implementation with Multiple Example

Lecture 49 Implement pow(x, n)

Lecture 50 Rat Maze Problem

Lecture 51 Sudoku solving Problem - 2

Section 6: Graph

Lecture 52 Alien Dictionary

Lecture 53 Cycle Graph

Lecture 54 Package Dependency Problem Using Topological Sorting

Lecture 55 Breadth first search algorithm to find Number of IsLand in matrix

Lecture 56 Breadth first search algorithm to solve Rotten Orange Problem

Lecture 57 Breadth first search algorithm to solve snake ladder problem

Lecture 58 All Paths From Source to Target

Lecture 59 Topological sorting concepts and implementation

Lecture 60 Trie data Structure implementation

Lecture 61 Trie data Structure implementation

Essential for computer science candidate to gain in-depth knowledge about data structures and algorithms,Useful for software developers to improve skills in data storage, retrieval, and processing,Beneficial for IT professionals to learn new skills or update their knowledge about data structures and algorithms,Suitable for anyone with an interest in computer science and problem-solving,Intended for individuals who want to develop a strong foundation in computer science fundamentals.