#algorithms and datastructures

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thinking about her(GreedyStackDestroyer)

[mindfulengineer]

7 Growth Functions in Data Structures: Behind asymptotic notations

Top coders use these to calculate time complexity and space complexity of algorithms.

https://medium.com/competitive-programming-concepts/7-growth-functions-in-data-structures-behind-asymptotic-notations-0fe44330daef

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"Introduction to the Theory of Computation" by Michael Sipser is a foundational textbook that explores the mathematical underpinnings of computer science. By reading this book, you will gain a deep understanding of computation, its limits, and its capabilities. Below is a step-by-step breakdown of the outcomes you can expect from studying this book:

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What is the concept of DSA?

Meaning of DSA

In today's technology-driven world, understanding what is the concept of DSA (Data Structures and Algorithms) is crucial for anyone in computer science and software development. Whether you're a beginner or an experienced programmer, mastering DSA helps in writing efficient and optimized programs. At TCCI-Tririd Computer Coaching Institute, our experts guide students in grasping DSA concepts and enhancing their problem-solving skills.

Understand DSA: The Programming Foundation

Data Structures and Algorithms can be considered the pillar of programming. It organizes and manages data so that searching, sorting, or manipulating it becomes more accessible and more rapid.

Why is DSA Needed?

Optimized Coding: Well-structured algorithms make a program run faster.

Interview Preparation: Tech companies focus a lot on DSA in their job interviews.

Problem-Solving Efficiency: DSA improves logical and coding skills.

Competitive Programming: Good DSA knowledge helps in solving very complicated coding problems quickly.

Components of DSA

Data Structures: These are ways in which data can be organized and stored. Some critical would include:

Arrays – Data is stored in a fixed-size.

Linked Lists – Flexible storage through dynamic memory allocation.

Stacks & Queues – LIFO & FIFO data handling techniques.

Trees & Graphs – Grouping data hierarchically or based on a network.

Hashtables – Using a key-value pair for fast access to data.

Algorithms: It is a stepwise procedure for solving problems. They can be of several types, including:

Sorting Algorithms (Bubble Sort, Merge Sort, Quick Sort)

Searching Algorithms (Binary Search, Linear Search)

Graph Algorithms (Dijkstra's Algorithm, BFS, DFS)

Dynamic Programming (Fibonacci Series, Knapsack Problem)

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[fortunatelycoldengineer]

Test Your Knowledge: Quiz Challenge!!! 📝🧠

What is the output of the below code?🤔

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For the explanation of the right answer, you can check Q.No. 7 of the above link. 📖

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LI-MAT's Data Structures and Algorithms Course is not merely an educational program but a transformative 6-month journey. Beyond imparting knowledge, the course equips students with practical skills, installs confidence, and validates their expertise with a prestigious certification. Read More - https://www.limatsoftsolutions.co.in/data-structures-and-algorithms-course Visit Us - https://www.limatsoftsolutions.co.in/

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[tutort-academy]

4 Actionable Steps to Master Data Structures and Algorithms (No Magic Required!)

Are you ready to embark on a journey to master Data Structures and Algorithms (DSA)? Whether you are a beginner or an experienced programmer, DSA is an essential skill set that can take your coding abilities to the next level. In this comprehensive guide, we will walk you through the 4 key steps to learn and master DSA, providing you with the roadmap and resources you need to succeed.

Step 1: Master a Programming Language

The first step in your DSA journey is to choose and master a programming language. While there are numerous programming languages to choose from, it is important to select one that you are comfortable with or interested in learning. Popular options include Java, C++, Python, and JavaScript.

Learning a programming language involves understanding its syntax, data types, control structures, and other fundamental concepts such as OOP (object oriented programming). To get started, you can explore online tutorials, and interactive coding platforms, or even enroll in a programming course. 

Step 2: Dive into Time and Space Complexities

Once you have a strong grasp of a programming language, it's time to delve into time and space complexities. Time complexity measures the amount of time required to execute a program, while space complexity refers to the amount of memory space needed for the program to run successfully. 

To determine the efficiency of an algorithm, you need to understand its time and space complexities. Asymptotic notation, such as Big O, Omega, and Theta, is commonly used to represent the time complexity of algorithms. These notations allow you to analyze the rate of growth of an algorithm as the input size increases. 

Step 3: Learn Data Structures and Algorithms

With a solid foundation in programming and an understanding of time and space complexities, you are now ready to dive into the heart of DSA - data structures and algorithms. Data structures are the building blocks that organize and store data, while algorithms are the step-by-step procedures used to solve problems and manipulate data.

To learn data structures and algorithms effectively, it is important to follow a structured approach. Start with the basics, such as arrays, linked lists, stacks, and queues. Understand their properties, operations, and use cases. Tutort Academy offers comprehensive courses on each of these topics, providing you with video lectures, practice problems, quizzes, and contests to enhance your understanding.

As you progress, you can explore more advanced topics like trees, graphs, sorting algorithms, searching algorithms, and dynamic programming.

Step 4: Practice, Practice, and Practice More

Practice is the key to mastering DSA. As the saying goes, "Practice makes perfect." The more you practice solving problems using different data structures and algorithms, the more comfortable and proficient you will become.

There are numerous resources available for practicing DSA problems. Online coding platforms, such as LeetCode, HackerRank, and CodeSignal, offer a wide range of coding challenges that test your problem-solving skills.

Now that you have a roadmap to learning DSA, it's time to take action and start your journey. Tutort Academy's Data Structures and Algorithms courses provide a comprehensive and structured approach to mastering DSA. With their expertly designed curriculum, you will gain a deep understanding of data structures, algorithms, time and space complexities, and problem-solving techniques.

Remember, learning DSA is not a one-time endeavor. It requires continuous practice, exploration, and the willingness to solve challenging problems.

Happy coding!

[asadmukhtarr]

"Introduction to Algorithms" by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein (often referred to as CLRS) is one of the most comprehensive and widely used textbooks on algorithms. It provides a deep dive into the design, analysis, and implementation of algorithms, making it an essential resource for students, programmers, and computer science enthusiasts. Below is a step-by-step breakdown of the outcomes you can expect from studying this book:

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The Complete Data Structures and Algorithms in Python Udemy Free Course

Welcome to the Complete Data Structures and Algorithms in Python Bootcamp, the most modern, and the most complete Data Structures and Algorithms in Python course on the internet. At 40+ hours, this is the most comprehensive course online to help you ace your coding interviews and learn about Data Structures and Algorithms in Python. You will see 100+ Interview Questions done at the top technology companies such as Apple,Amazon, Google and Microsoft and how to face Interviews with comprehensive visual explanatory video materials which will bring you closer towards landing the tech job of your dreams! Learning Python is one of the fastest ways to improve your career prospects as it is one of the most in demand tech skills! This course will help you in better understanding every detail of Data Structures and how algorithms are implemented in high level programming language.

Data Structures and Algorithms in Python

We’ll take you step-by-step through engaging video tutorials and teach you everything you need to succeed as a professional programmer. After finishing this course, you will be able to: Learn basic algorithmic techniques such as greedy algorithms, binary search, sorting and dynamic programming to solve programming challenges. Learn the strengths and weaknesses of a variety of data structures, so you can choose the best data structure for your data and applications Learn many of the algorithms commonly used to sort data, so your applications will perform efficiently when sorting large datasets Learn how to apply graph and string algorithms to solve real-world challenges: finding shortest paths on huge maps and assembling genomes from millions of pieces.

Data Structures and Algorithms in Python

Why this course is so special and different from any other resource available online? The Complete Data Structures and Algorithms Course in Python will take you from very beginning to a very complex and advanced topics in understanding Data Structures and Algorithms! You will get video lectures explaining concepts clearly with comprehensive visual explanations throughout the course. You will also see Interview Questions done at the top technology companies such as Apple,Amazon, Google and Microsoft. I cover everything you need to know about technical interview process! So whether you are interested in learning the top programming language in the world in-depth And interested in learning the fundamental Algorithms, Data Structures and performance analysis that make up the core foundational skillset of every accomplished programmer/designer or software architect and is excited to ace your next technical interview this is the course for you! And this is what you get by signing up today: Lifetime access to 40+ hours of HD quality videos. No monthly subscription. Learn at your own pace, whenever you want Friendly and fast support in the course Q&A whenever you have questions or get stuck FULL money back guarantee for 30 days! Who is this course for? Self-taught programmers who have a basic knowledge in Python and want to be professional in Data Structures and Algorithms and begin interviewing in tech positions! As well as students currently studying computer science and want supplementary material on Data Structures and Algorithms and interview preparation for after graduation! As well as professional programmers who need practice for upcoming coding interviews. And finally anybody interested in learning more about data structures and algorithms or the technical interview process! This course is designed to help you to achieve your career goals. Whether you are looking to get more into Data Structures and Algorithms , increase your earning potential or just want a job with more freedom, this is the right course for you! The topics that are covered in this course. Section 1 – Introduction - What are Data Structures? - What is an algorithm? - Why are Data Structures and Algorithms important? - Types of Data Structures - Types of Algorithms Section 2 – Recursion - What is Recursion? - Why do we need recursion? - How Recursion works? - Recursive vs Iterative Solutions - When to use/avoid Recursion? - How to write Recursion in 3 steps? - How to find Fibonacci numbers using Recursion? Section 3 – Cracking Recursion Interview Questions - Question 1 – Sum of Digits - Question 2 – Power - Question 3 – Greatest Common Divisor - Question 4 – Decimal To Binary Section 4 – Bonus CHALLENGING Recursion Problems (Exercises) - power - factorial - productofArray - recursiveRange - fib - reverse - isPalindrome - someRecursive - flatten - captalizeFirst - nestedEvenSum - capitalizeWords - stringifyNumbers - collectStrings Section 5 – Big O Notation - Analogy and Time Complexity - Big O, Big Theta and Big Omega - Time complexity examples - Space Complexity - Drop the Constants and the non dominant terms - Add vs Multiply - How to measure the codes using Big O? - How to find time complexity for Recursive calls? - How to measure Recursive Algorithms that make multiple calls? Section 6 – Top 10 Big O Interview Questions (Amazon, Facebook, Apple and Microsoft) - Product and Sum - Print Pairs - Print Unordered Pairs - Print Unordered Pairs 2 Arrays - Print Unordered Pairs 2 Arrays 100000 Units - Reverse - O(N)  Equivalents - Factorial Complexity - Fibonacci Complexity - Powers of 2 Section 7 – Arrays - What is an Array? - Types of Array - Arrays in Memory - Create an Array - Insertion Operation - Traversal Operation - Accessing an element of Array - Searching for an element in Array - Deleting an element from Array - Time and Space complexity of One Dimensional Array - One Dimensional Array Practice - Create Two Dimensional Array - Insertion – Two Dimensional Array - Accessing an element of Two Dimensional Array - Traversal – Two Dimensional Array - Searching for an element in Two Dimensional Array - Deletion – Two Dimensional Array - Time and Space complexity of Two Dimensional Array - When to use/avoid array Section 8 – Python Lists - What is a List? How to create it? - Accessing/Traversing a list - Update/Insert a List - Slice/ from a List - Searching for an element in a List - List Operations/Functions - Lists and strings - Common List pitfalls and ways to avoid them - Lists vs Arrays - Time and Space Complexity of List - List Interview Questions Section 9 – Cracking Array/List Interview Questions (Amazon, Facebook, Apple and Microsoft) - Question 1 – Missing Number - Question 2 – Pairs - Question 3 – Finding a number in an Array - Question 4 – Max product of two int - Question 5 – Is Unique - Question 6 – Permutation - Question 7 – Rotate Matrix Section 10 – CHALLENGING Array/List Problems (Exercises) - Middle Function - 2D Lists - Best Score - Missing Number - Duplicate Number - Pairs Section 11 – Dictionaries - What is a Dictionary? - Create a Dictionary - Dictionaries in memory - Insert /Update an element in a Dictionary - Traverse through a Dictionary - Search for an element in a Dictionary - Delete / Remove an element from a Dictionary - Dictionary Methods - Dictionary operations/ built in functions - Dictionary vs List - Time and Space Complexity of a Dictionary - Dictionary Interview Questions Section 12 – Tuples - What is a Tuple? How to create it? - Tuples in Memory / Accessing an element of Tuple - Traversing a Tuple - Search for an element in Tuple - Tuple Operations/Functions - Tuple vs List - Time and Space complexity of Tuples - Tuple Questions Section 13 – Linked List - What is a Linked List? - Linked List vs Arrays - Types of Linked List - Linked List in the Memory - Creation of Singly Linked List - Insertion in Singly Linked List in Memory - Insertion in Singly Linked List Algorithm - Insertion Method in Singly Linked List - Traversal of Singly Linked List - Search for a value in Single Linked List - Deletion of node from Singly Linked List - Deletion Method in Singly Linked List - Deletion of entire Singly Linked List - Time and Space Complexity of Singly Linked List Section 14 – Circular Singly Linked List - Creation of Circular Singly Linked List - Insertion in Circular Singly Linked List - Insertion Algorithm in Circular Singly Linked List - Insertion method in Circular Singly Linked List - Traversal of Circular Singly Linked List - Searching a node in Circular Singly Linked List - Deletion of a node from Circular Singly Linked List - Deletion Algorithm in Circular Singly Linked List - Method in Circular Singly Linked List - Deletion of entire Circular Singly Linked List - Time and Space Complexity of Circular Singly Linked List Section 15 – Doubly Linked List - Creation of Doubly Linked List - Insertion in Doubly Linked List - Insertion Algorithm in Doubly Linked List - Insertion Method in Doubly Linked List - Traversal of Doubly Linked List - Reverse Traversal of Doubly Linked List - Searching for a node in Doubly Linked List - Deletion of a node in Doubly Linked List - Deletion Algorithm in Doubly Linked List - Deletion Method in Doubly Linked List - Deletion of entire Doubly Linked List - Time and Space Complexity of Doubly Linked List Section 16 – Circular Doubly Linked List - Creation of Circular Doubly Linked List - Insertion in Circular Doubly Linked List - Insertion Algorithm in Circular Doubly Linked List - Insertion Method in Circular Doubly Linked List - Traversal of Circular Doubly Linked List - Reverse Traversal of Circular Doubly Linked List - Search for a node in Circular Doubly Linked List - Delete a node from Circular Doubly Linked List - Deletion Algorithm in Circular Doubly Linked List - Deletion Method in Circular Doubly Linked List - Entire Circular Doubly Linked List - Time and Space Complexity of Circular Doubly Linked List - Time Complexity of Linked List vs Arrays Section 17 – Cracking Linked List Interview Questions (Amazon, Facebook, Apple and Microsoft) - Linked List Class - Question 1 – Remove Dups - Question 2 – Return Kth to Last - Question 3 – Partition - Question 4 – Sum Linked Lists - Question 5 – Intersection Section 18 – Stack - What is a Stack? - Stack Operations - Create Stack using List without size limit - Operations on Stack using List (push, pop, peek, isEmpty, ) - Create Stack with limit (pop, push, peek, isFull, isEmpty, ) - Create Stack using Linked List - Operation on Stack using Linked List (pop, push, peek, isEmpty, ) - Time and Space Complexity of Stack using Linked List - When to use/avoid Stack - Stack Quiz Section 19 – Queue - What is Queue? - Queue using Python List – no size limit - Queue using Python List – no size limit , operations (enqueue, dequeue, peek) - Circular Queue – Python List - Circular Queue – Python List, Operations (enqueue, dequeue, peek, ) - Queue – Linked List - Queue – Linked List, Operations (Create, Enqueue) - Queue – Linked List, Operations (Dequeue(), isEmpty, Peek) - Time and Space complexity of Queue using Linked List - List vs Linked List Implementation - Collections Module - Queue Module - Multiprocessing module Section 20 – Cracking Stack and Queue Interview Questions (Amazon,Facebook, Apple, Microsoft) - Question 1 – Three in One - Question 2 – Stack Minimum - Question 3 – Stack of Plates - Question 4 – Queue via Stacks - Question 5 – Animal Shelter Section 21 – Tree / Binary Tree - What is a Tree? - Why Tree? - Tree Terminology - How to create a basic tree in Python? - Binary Tree in The Complete Data Structures and Algorithms Course in Python - Types of Binary Tree - Binary Tree Representation - Create Binary Tree (Linked List) - PreOrder Traversal Binary Tree (Linked List) - InOrder Traversal Binary Tree (Linked List) - PostOrder Traversal Binary Tree (Linked List) - LevelOrder Traversal Binary Tree (Linked List) - Searching for a node in Binary Tree (Linked List) - Inserting a node in Binary Tree (Linked List) - Delete a node from Binary Tree (Linked List) - Delete entire Binary Tree (Linked List) - Create Binary Tree (Python List) - Insert a value Binary Tree (Python List) - Search for a node in Binary Tree (Python List) - PreOrder Traversal Binary Tree (Python List) - InOrder Traversal Binary Tree (Python List) - PostOrder Traversal Binary Tree (Python List) - Level Order Traversal Binary Tree (Python List) - Delete a node from Binary Tree (Python List) - Entire Binary Tree (Python List) - Linked List vs Python List Binary Tree Section 22 – Binary Search Tree - What is a Binary Search Tree? Why do we need it? - Create a Binary Search Tree - Insert a node to BST - Traverse BST - Search in BST - Delete a node from BST - Delete entire BST - Time and Space complexity of BST Section 23 – AVL Tree - What is an AVL Tree? - Why AVL Tree? - Common Operations on AVL Trees - Insert a node in AVL (Left Left Condition) - Insert a node in AVL (Left Right Condition) - Insert a node in AVL (Right Right Condition) - Insert a node in AVL (Right Left Condition) - Insert a node in AVL (all together) - Insert a node in AVL (method) - Delete a node from AVL (LL, LR, RR, RL) - Delete a node from AVL (all together) - Delete a node from AVL (method) - Delete entire AVL - Time and Space complexity of AVL Tree Section 24 – Binary Heap - What is Binary Heap? Why do we need it? - Common operations (Creation, Peek, sizeofheap) on Binary Heap - Insert a node in Binary Heap - Extract a node from Binary Heap - Delete entire Binary Heap - Time and space complexity of Binary Heap Section 25 – Trie - What is a Trie? Why do we need it? - Common Operations on Trie (Creation) - Insert a string in Trie - Search for a string in Trie - Delete a string from Trie - Practical use of Trie Section 26 – Hashing - What is Hashing? Why do we need it? - Hashing Terminology - Hash Functions - Types of Collision Resolution Techniques - Hash Table is Full - Pros and Cons of Resolution Techniques - Practical Use of Hashing - Hashing vs Other Data structures Section 27 – Sort Algorithms - What is Sorting? - Types of Sorting - Sorting Terminologies - Bubble Sort - Selection Sort - Insertion Sort - Bucket Sort - Merge Sort - Quick Sort - Heap Sort - Comparison of Sorting Algorithms Section 28 – Searching Algorithms - Introduction to Searching Algorithms - Linear Search - Linear Search in Python - Binary Search - Binary Search in Python - Time Complexity of Binary Search Section 29 – Graph Algorithms - What is a Graph? Why Graph? - Graph Terminology - Types of Graph - Graph Representation - Create a graph using Python - Graph traversal – BFS - BFS Traversal in Python - Graph Traversal – DFS - DFS Traversal in Python - BFS Traversal vs DFS Traversal - Topological Sort in The Complete Data Structures and Algorithms Course in Python - Topological Sort Algorithm - Topological Sort in Python - Single Source Shortest Path Problem (SSSPP) - BFS for Single Source Shortest Path Problem (SSSPP) - BFS for Single Source Shortest Path Problem (SSSPP) in Python - Why does BFS not work with weighted Graphs? - Why does DFS not work for SSSP? - Dijkstra’s Algorithm for SSSP - Dijkstra’s Algorithm in Python - Dijkstra Algorithm with negative cycle - Bellman Ford Algorithm - Bellman Ford Algorithm with negative cycle - Why does Bellman Ford run V-1 times? - Bellman Ford in Python - BFS vs Dijkstra vs Bellman Ford - All pairs shortest path problem - Dry run for All pair shortest path - Floyd Warshall Algorithm - Why Floyd Warshall? - Floyd Warshall with negative cycle, - Floyd Warshall in Python, - BFS vs Dijkstra vs Bellman Ford vs Floyd Warshall, - Minimum Spanning Tree, - Disjoint Set, - Disjoint Set in Python, - Kruskal Algorithm, - Kruskal Algorithm in Python, - Prim’s Algorithm, - Prim’s Algorithm in Python, - Prim’s vs Kruskal Section 30 – Greedy Algorithms - What is Greedy Algorithm? - Well known Greedy Algorithms - Activity Selection Problem - Activity Selection Problem in Python - Coin Change Problem - Coin Change Problem in Python - Fractional Knapsack Problem - Fractional Knapsack Problem in Python Section 31 – Divide and Conquer Algorithms - What is a Divide and Conquer Algorithm? - Common Divide and Conquer algorithms - How to solve Fibonacci series using Divide and Conquer approach? Read the full article

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