#Circular Singly Linked List

[d8tl55c]

series with links

the above linked lists are doubly-linked lists,

_-[ ]z[ ]z[ ]z[ ]-_

and if you don't link to prev, then it's a singly-linked list.

[ ]->[ ]->[ ]->[ ]-_

there's also circular linked lists which loop around back to front, singly or doubly-linked. these guys have no null pointer.

[ ]->[ ]->[ ]->[ ]-.

^-[ ]<-[ ]<-[ ]<-[ ]

i don't remember a comp sci term for the last one except graph theory from discrete math so you're getting the graph theory.

[mindmapdaily]

Linked List in Python

A linked list is a dynamic data structure used to store elements in a linear order, where each element (called a node) points to the next. Unlike Python lists that use contiguous memory, linked lists offer flexibility in memory allocation, making them ideal for situations where the size of the data isn’t fixed or changes frequently.

In Python, linked lists aren’t built-in but can be implemented using classes. Each node contains two parts: the data and a reference to the next node. The list is managed using a class that tracks the starting node, known as the head.

Node Structure: Contains data and next (pointer to the next node).

Types:

Singly Linked List

Doubly Linked List

Circular Linked List

Operations: Insertion, deletion, and traversal.

Advantages:

Dynamic size

Efficient insertions/deletions

Disadvantages:

Slower access (no random indexing)

Extra memory for pointers

Want to master linked lists and other data structures in Python? PrepInsta has you covered with beginner-friendly explanations and practice problems.

Explore Linked Lists with PrepInsta

[contentsmith]

Linked List in Java

Hey everyone!

If you're learning Java and getting into data structures, one concept you'll definitely come across is the Linked List. At first, it might seem a bit tricky compared to arrays, but once you understand how it works, it becomes a super handy tool in your coding journey.

So, what exactly is a linked list?

Think of it like a chain — each part of the chain is called a node. Every node holds some data (like a number or a word) and also keeps track of the next node in line. This way, all the nodes are connected one after the other.

Unlike arrays, linked lists don’t store elements in a fixed or continuous memory space. That makes them flexible — you can easily add or remove items without having to shift other elements around.

There are a few different types of linked lists:

Singly Linked List – each node points to the next one.

Doubly Linked List – nodes point both to the next and previous ones.

Circular Linked List – the last node connects back to the first.

One of the biggest reasons to use a linked list over an array is because it’s dynamic. You don’t need to define a fixed size at the beginning. It’s great for situations where the number of elements can change often.

If you're curious about how to create a linked list in Java, or want to practice with some examples, I recommend checking out this page: Linked List in Java

It explains the concept in a beginner-friendly way and includes simple Java code examples you can try out on your own.

Give it a go and start experimenting — it's a great step toward understanding how data structures work in real coding scenarios.

[programmingandengineering]

Circular Singly LinkedList

You are to code a CircularSinglyLinkedList with a head reference. A linked list is a collection of nodes, each having a data item and references to other nodes. In a CircularSinglyLinkedList, each node has a reference to the next node. Since it must be circular, the next reference for the last node in this list will point to the head node. As a special case, this means that in a one node list,…

[finarena]

DSA Channel: The Ultimate Destination for Learning Data Structures and Algorithms from Basics to Advanced

DSA mastery stands vital for successful software development and competitive programming in the current digital world that operates at high speeds. People at every skill level from beginner to advanced developer will find their educational destination at the DSA Channel.

Why is DSA Important?

Software development relies on data structures together with algorithms as its essential core components. Code optimization emerges from data structures and algorithms which produces better performance and leads to successful solutions of complex problems. Strategic knowledge of DSA serves essential needs for handling job interviews and coding competitions while enhancing logical thinking abilities. Proper guidance makes basic concepts of DSA both rewarding and enjoyable to study.

What Makes DSA Channel Unique?

The DSA Channel exists to simplify both data structures along algorithms and make them accessible to all users. Here’s why it stands out:

The channel provides step-by-step learning progress which conservatively begins by teaching arrays and linked lists and continues to dynamic programming and graph theory.

Each theoretical concept gets backed through coding examples practically to facilitate easier understanding and application in real-life situations.

Major companies like Google, Microsoft, and Amazon utilize DSA knowledge as part of their job recruiter process. Through their DSA Channel service candidates can perform mock interview preparation along with receiving technical interview problem-solving advice and interview cracking techniques.

Updates Occur Regularly Because the DSA Channel Matches the Ongoing Transformation in the Technology Industry. The content uses current algorithm field trends and new elements for constant updates.

 DSAC channels will be covering the below key topics

DSA Channel makes certain you have clear ideas that are necessary for everything from the basics of data structures to the most sophisticated methods and use cases. Highlights :

1. Introduction Basic Data Structures

Fundamentals First, You Always Need To Start With the Basics. Some of the DSA Channel topics are:

Memories storing and manipulating elements of Arrays

Linked Lists — learn linked lists: Singly Linked lists Dually linked lists and Circular linked list

Implementing Stacks and Queues — linear data structure with these implementations.

Hash Table: Understanding Hashing and its impact in the retrieval of Data.

2. Advanced Data Structures

If you want to get Intense: the DSA channel has profound lessons:

Graph bases Types- Type of Graph Traversals: BFS, DFS

Heaps — Come to know about Min Heap and Max Heap

Index Tries – How to store and retrieve a string faster than the fastest possible.

3. Algorithms

This is especially true for efficient problem-solving. The DSA Channel discusses in-depth:

Searching Algorithms Binary Search and Linear Search etc.

Dynamic Programming: Optimization of subproblems

Recursion and Backtracking: How to solve a problem by recursion.

Graph Algorithms — Dijkstra, Bellman-Ford and Floyd-Warshall etc

4. Applications of DSA in Real life

So one of the unique things (About the DSA channel) is these real-world applications of his DSA Channel.

Instead of just teaching Theory the channel gives a hands-on to see how it's used in world DSA applications.

Learning about Database Management Systems — Indexing, Query Optimization, Storage Techniques

Operating Systems – study algorithms scheduling, memory management,t, and file systems.

Machine Learning and AI — Learning the usage of algorithms in training models, and optimizing computations.

Finance and Banking — data structures that help us in identifying risk scheme things, fraud detection, transaction processing, etc.

This hands-on approach to working out will ensure that learners not only know how to use these concepts in real-life examples. 

How Arena Fincorp Benefits from DSA?

Arena Fincorp, a leading financial services provider, understands the importance of efficiency and optimization in the fintech sector. The financial solutions offered through Arena Fincorp operate under the same principles as data structures and algorithms which enhance coding operations. Arena Fincorp guarantees perfect financial transactions and data protection through its implementation of sophisticated algorithms. The foundational principles of DSA enable developers to build strong financial technological solutions for contemporary financial complications.

How to Get Started with DSA Channel?

New users of the DSA Channel should follow these instructions to maximize their experience:

The educational process should start with fundamental videos explaining arrays together with linked lists and stacks to establish a basic knowledge base.

The practice of DSA needs regular exercise and time to build comprehension. Devote specific time each day to find solutions for problems.

The platforms LeetCode, CodeChef, and HackerRank provide various DSA problems for daily problem-solving which boosts your skills.

Join community discussions where you can help learners by sharing solutions as well as working with fellow participants.

Students should do Mock Interviews through the DSA Channel to enhance their self-confidence and gain experience in actual interview situations.

The process of learning becomes more successful when people study together in a community. Through the DSA Channel students find an energetic learning community to share knowledge about doubts and project work and they exchange insight among themselves.

Conclusion

Using either data structures or algorithms in tech requires mastery so they have become mandatory in this sector. The DSA Channel delivers the best learning gateway that suits students as well as professionals and competitive programmers. Through their well-organized educational approach, practical experience and active learner network the DSA Channel builds a deep understanding of DSA with effective problem-solving abilities.

The value of data structures and algorithms and their optimized algorithms and efficient coding practices allows companies such as Arena Fincorp to succeed in their industries. New learners should begin their educational journey right now with the DSA Channel to master data structures and algorithms expertise.

[sunbeaminfo]

Enhance Your Coding Skills with Data Structures and Algorithms Classes at Sunbeam Institute, Pune

Elevate your programming expertise by enrolling in the Data Structures and Algorithms course at Sunbeam Institute, Pune. This comprehensive program is designed for students, freshers, and working professionals aiming to deepen their understanding of essential data structures and algorithms using Java.

Course Highlights:

Algorithm Analysis: Learn to evaluate time and space complexity for efficient coding.

Linked Lists: Master various types, including singly, doubly, and circular linked lists.

Stacks and Queues: Understand their implementation using arrays and linked lists, and apply them in expression evaluation and parenthesis balancing.

Sorting and Searching: Gain proficiency in algorithms like Quick Sort, Merge Sort, Heap Sort, Linear Search, Binary Search, and Hashing.

Trees and Graphs: Explore tree traversals, Binary Search Trees (BST), and graph algorithms such as Prim’s MST, Kruskal’s MST, Dijkstra's, and A* search.

Course Details:

Duration: 60 hours

Schedule: Weekdays (Monday to Saturday), 5:00 PM to 8:00 PM

Upcoming Batch: January 27, 2025, to February 18, 2025

Fees: ₹7,500 (including 18% GST)

Prerequisites:

Basic knowledge of Java programming, including classes, objects, generics, and Java collections (e.g., ArrayList).

Why Choose Sunbeam Institute?

Sunbeam Institute is renowned for its effective IT training programs in Pune, offering a blend of theoretical knowledge and practical application to ensure a thorough understanding of complex concepts.

Enroll Now: Secure your spot in this sought-after course to advance your programming skills and enhance your career prospects. For registration and more information, visit:

[nocodehackathon]

Essential Algorithms and Data Structures for Competitive Programming

Competitive programming is a thrilling and intellectually stimulating field that challenges participants to solve complex problems efficiently and effectively. At its core, competitive programming revolves around algorithms and data structures—tools that help you tackle problems with precision and speed. If you're preparing for a competitive programming contest or just want to enhance your problem-solving skills, understanding essential algorithms and data structures is crucial. In this blog, we’ll walk through some of the most important ones you should be familiar with.

1. Arrays and Strings

Arrays are fundamental data structures that store elements in a contiguous block of memory. They allow for efficient access to elements via indexing and are often the first data structure you encounter in competitive programming.

Operations: Basic operations include traversal, insertion, deletion, and searching. Understanding how to manipulate arrays efficiently can help solve a wide range of problems.

Strings are arrays of characters and are often used to solve problems involving text processing. Basic string operations like concatenation, substring search, and pattern matching are essential.

2. Linked Lists

A linked list is a data structure where elements (nodes) are stored in separate memory locations and linked together using pointers. There are several types of linked lists:

Singly Linked List: Each node points to the next node.

Doubly Linked List: Each node points to both the next and previous nodes.

Circular Linked List: The last node points back to the first node.

Linked lists are useful when you need to frequently insert or delete elements as they allow for efficient manipulation of the data.

3. Stacks and Queues

Stacks and queues are abstract data types that operate on a last-in-first-out (LIFO) and first-in-first-out (FIFO) principle, respectively.

Stacks: Useful for problems involving backtracking or nested structures (e.g., parsing expressions).

Queues: Useful for problems involving scheduling or buffering (e.g., breadth-first search).

Both can be implemented using arrays or linked lists and are foundational for many algorithms.

4. Hashing

Hashing involves using a hash function to convert keys into indices in a hash table. This allows for efficient data retrieval and insertion.

Hash Tables: Hash tables provide average-case constant time complexity for search, insert, and delete operations.

Collisions: Handling collisions (when two keys hash to the same index) using techniques like chaining or open addressing is crucial for effective hashing.

5. Trees

Trees are hierarchical data structures with a root node and child nodes. They are used to represent hierarchical relationships and are key to many algorithms.

Binary Trees: Each node has at most two children. They are used in various applications such as binary search trees (BSTs), where the left child is less than the parent, and the right child is greater.

Binary Search Trees (BSTs): Useful for dynamic sets where elements need to be ordered. Operations like insertion, deletion, and search have an average-case time complexity of O(log n).

Balanced Trees: Trees like AVL trees and Red-Black trees maintain balance to ensure O(log n) time complexity for operations.

6. Heaps

A heap is a specialized tree-based data structure that satisfies the heap property:

Max-Heap: The value of each node is greater than or equal to the values of its children.

Min-Heap: The value of each node is less than or equal to the values of its children.

Heaps are used in algorithms like heap sort and are also crucial for implementing priority queues.

7. Graphs

Graphs represent relationships between entities using nodes (vertices) and edges. They are essential for solving problems involving networks, paths, and connectivity.

Graph Traversal: Algorithms like Breadth-First Search (BFS) and Depth-First Search (DFS) are used to explore nodes and edges in graphs.

Shortest Path: Algorithms such as Dijkstra’s and Floyd-Warshall help find the shortest path between nodes.

Minimum Spanning Tree: Algorithms like Kruskal’s and Prim’s are used to find the minimum spanning tree in a graph.

8. Dynamic Programming

Dynamic Programming (DP) is a method for solving problems by breaking them down into simpler subproblems and storing the results of these subproblems to avoid redundant computations.

Memoization: Storing results of subproblems to avoid recomputation.

Tabulation: Building a table of results iteratively, bottom-up.

DP is especially useful for optimization problems, such as finding the shortest path, longest common subsequence, or knapsack problem.

9. Greedy Algorithms

Greedy Algorithms make a series of choices, each of which looks best at the moment, with the hope that these local choices will lead to a global optimum.

Applications: Commonly used for problems like activity selection, Huffman coding, and coin change.

10. Graph Algorithms

Understanding graph algorithms is crucial for competitive programming:

Shortest Path Algorithms: Dijkstra’s Algorithm, Bellman-Ford Algorithm.

Minimum Spanning Tree Algorithms: Kruskal’s Algorithm, Prim’s Algorithm.

Network Flow Algorithms: Ford-Fulkerson Algorithm, Edmonds-Karp Algorithm.

Preparing for Competitive Programming: Summer Internship Program

If you're eager to dive deeper into these algorithms and data structures, participating in a summer internship program focused on Data Structures and Algorithms (DSA) can be incredibly beneficial. At our Summer Internship Program, we provide hands-on experience and mentorship to help you master these crucial skills. This program is designed for aspiring programmers who want to enhance their competitive programming abilities and prepare for real-world challenges.

What to Expect:

Hands-On Projects: Work on real-world problems and implement algorithms and data structures.

Mentorship: Receive guidance from experienced professionals in the field.

Workshops and Seminars: Participate in workshops that cover advanced topics and techniques.

Networking Opportunities: Connect with peers and industry experts to expand your professional network.

By participating in our DSA Internship, you’ll gain practical experience and insights that will significantly boost your competitive programming skills and prepare you for success in contests and future career opportunities.

In conclusion, mastering essential algorithms and data structures is key to excelling in competitive programming. By understanding and practicing these concepts, you can tackle complex problems with confidence and efficiency. Whether you’re just starting out or looking to sharpen your skills, focusing on these fundamentals will set you on the path to success.

Ready to take your skills to the next level? Join our Summer Internship Program and dive into the world of algorithms and data structures with expert guidance and hands-on experience. Your journey to becoming a competitive programming expert starts here!

[myprogrammingsolver]

Circular Singly LinkedList

You are to code a CircularSinglyLinkedList with a head reference. A linked list is a collection of nodes, each having a data item and references to other nodes. In a CircularSinglyLinkedList, each node has a reference to the next node. Since it must be circular, the next reference for the last node in this list will point to the head node. As a special case, this means that in a one node list,…

[tutort-academy]

How to Ace Your DSA Interview, Even If You're a Newbie

Are you aiming to crack DSA interviews and land your dream job as a software engineer or developer? Look no further! This comprehensive guide will provide you with all the necessary tips and insights to ace your DSA interviews. We'll explore the important DSA topics to study, share valuable preparation tips, and even introduce you to Tutort Academy DSA courses to help you get started on your journey. So let's dive in!

Why is DSA Important?

Before we delve into the specifics of DSA interviews, let's first understand why data structures and algorithms are crucial for software development. DSA plays a vital role in optimizing software components, enabling efficient data storage and processing.

From logging into your Facebook account to finding the shortest route on Google Maps, DSA is at work in various applications we use every day. Mastering DSA allows you to solve complex problems, optimize code performance, and design efficient software systems.

Important DSA Topics to Study

To excel in DSA interviews, it's essential to have a strong foundation in key topics. Here are some important DSA topics you should study:

1. Arrays and Strings

Arrays and strings are fundamental data structures in programming. Understanding array manipulation, string operations, and common algorithms like sorting and searching is crucial for solving coding problems.

2. Linked Lists

Linked lists are linear data structures that consist of nodes linked together. It's important to understand concepts like singly linked lists, doubly linked lists, and circular linked lists, as well as operations like insertion, deletion, and traversal.

3. Stacks and Queues

Stacks and queues are abstract data types that follow specific orderings. Mastering concepts like LIFO (Last In, First Out) for stacks and FIFO (First In, First Out) for queues is essential. Additionally, learn about their applications in real-life scenarios.

4. Trees and Binary Trees

Trees are hierarchical data structures with nodes connected by edges. Understanding binary trees, binary search trees, and traversal algorithms like preorder, inorder, and postorder is crucial. Additionally, explore advanced concepts like AVL trees and red-black trees.

5. Graphs

Graphs are non-linear data structures consisting of nodes (vertices) and edges. Familiarize yourself with graph representations, traversal algorithms like BFS (Breadth-First Search) and DFS (Depth-First Search), and graph algorithms such as Dijkstra's algorithm and Kruskal's algorithm.

6. Sorting and Searching Algorithms

Understanding various sorting algorithms like bubble sort, selection sort, insertion sort, merge sort, and quicksort is essential. Additionally, familiarize yourself with searching algorithms like linear search, binary search, and hash-based searching.

7. Dynamic Programming

Dynamic programming involves breaking down a complex problem into smaller overlapping subproblems and solving them individually. Mastering this technique allows you to solve optimization problems efficiently.

These are just a few of the important DSA topics to study. It's crucial to have a solid understanding of these concepts and their applications to perform well in DSA interviews.

Tips to Follow While Preparing for DSA Interviews

Preparing for DSA interviews can be challenging, but with the right approach, you can maximize your chances of success. Here are some tips to keep in mind:

1. Understand the Fundamentals

Before diving into complex algorithms, ensure you have a strong grasp of the fundamentals. Familiarize yourself with basic data structures, common algorithms, and time and space complexities.

2. Practice Regularly

Consistent practice is key to mastering DSA. Solve a wide range of coding problems, participate in coding challenges, and implement algorithms from scratch. Leverage online coding platforms like LeetCode, HackerRank to practice and improve your problem-solving skills.

3. Analyze and Optimize

After solving a problem, analyze your solution and look for areas of improvement. Optimize your code for better time and space complexities. This demonstrates your ability to write efficient and scalable code.

4. Collaborate and Learn from Others

Engage with the coding community, join study groups, and participate in online forums. Collaborating with others allows you to learn different approaches, gain insights, and improve your problem-solving skills.

5. Mock Interviews and Feedback

Simulate real interview scenarios by participating in mock interviews. Seek feedback from experienced professionals or mentors who can provide valuable insights into your strengths and areas for improvement.

Following these tips will help you build a solid foundation in DSA and boost your confidence for interviews.

Conclusion

Mastering DSA is crucial for acing coding interviews and securing your dream job as a software engineer or developer. By studying important DSA topics, following effective preparation tips, and leveraging Tutort Academy's DSA courses, you'll be well-equipped to tackle DSA interviews with confidence. Remember to practice regularly, seek feedback, and stay curious.

Good luck on your DSA journey!

[allcprogrammingandalgorithm]

#include<stdio.h> void main (){    printf("\n\n\t\t\t*******************************");    printf("\n\t\t\t*                               *");    printf("\n\t\t\t*         hello World           *");        printf("\n\t\t\t*                               *");    printf("\n\t\t\t*********************************\n");} output:- Hello World

[courseforfree]

Data Structures and Algorithms from Zero to Hero and Crack Top Companies 100+ Interview questions (Java Coding)

What you’ll learn

Java Data Structures and Algorithms Masterclass

Learn, implement, and use different Data Structures

Learn, implement and use different Algorithms

Become a better developer by mastering computer science fundamentals

Learn everything you need to ace difficult coding interviews

Cracking the Coding Interview with 100+ questions with explanations

Time and Space Complexity of Data Structures and Algorithms

Recursion

Big O

Dynamic Programming

Divide and Conquer Algorithms

Graph Algorithms

Greedy Algorithms

Requirements

Basic Java Programming skills

Description

Welcome to the Java Data Structures and Algorithms Masterclass, the most modern, and the most complete Data Structures and Algorithms in Java course on the internet.

At 44+ hours, this is the most comprehensive course online to help you ace your coding interviews and learn about Data Structures and Algorithms in Java. 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 to landing the tech job of your dreams!

Learning Java 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 languages.

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.

Why this course is so special and different from any other resource available online?

This course will take you from the very beginning to 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 the 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 44+ 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!

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 does Recursion work?

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

products array

recursiveRange

fib

reverse

palindrome

some recursive

flatten

capitalize first

nestedEvenSum

capitalize words

stringifyNumbers

collects things

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 nondominant 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 – Cracking Array 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 9 – CHALLENGING Array Problems (Exercises)

Middle Function

2D Lists

Best Score

Missing Number

Duplicate Number

Pairs

Section 10 – 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 a 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 11 – 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

A method in Circular Singly Linked List

Deletion of entire Circular Singly Linked List

Time and Space Complexity of Circular Singly Linked List

Section 12 – 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 13 – 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 14 – 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 15 – Stack

What is a Stack?

What and Why of Stack?

Stack Operations

Stack using Array vs Linked List

Stack Operations using Array (Create, isEmpty, isFull)

Stack Operations using Array (Push, Pop, Peek, Delete)

Time and Space Complexity of Stack using Array

Stack Operations using Linked List

Stack methods – Push, Pop, Peek, Delete, and isEmpty using Linked List

Time and Space Complexity of Stack using Linked List

When to Use/Avoid Stack

Stack Quiz

Section 16 – Queue

What is a Queue?

Linear Queue Operations using Array

Create, isFull, isEmpty, and enQueue methods using Linear Queue Array

Dequeue, Peek and Delete Methods using Linear Queue Array

Time and Space Complexity of Linear Queue using Array

Why Circular Queue?

Circular Queue Operations using Array

Create, Enqueue, isFull and isEmpty Methods in Circular Queue using Array

Dequeue, Peek and Delete Methods in Circular Queue using Array

Time and Space Complexity of Circular Queue using Array

Queue Operations using Linked List

Create, Enqueue and isEmpty Methods in Queue using Linked List

Dequeue, Peek and Delete Methods in Queue using Linked List

Time and Space Complexity of Queue using Linked List

Array vs Linked List Implementation

When to Use/Avoid Queue?

Section 17 – 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 18 – Tree / Binary Tree

What is a Tree?

Why Tree?

Tree Terminology

How to create a basic tree in Java?

Binary Tree

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 (Array)

Insert a value Binary Tree (Array)

Search for a node in Binary Tree (Array)

PreOrder Traversal Binary Tree (Array)

InOrder Traversal Binary Tree (Array)

PostOrder Traversal Binary Tree (Array)

Level Order Traversal Binary Tree (Array)

Delete a node from Binary Tree (Array)

Entire Binary Tree (Array)

Linked List vs Python List Binary Tree

Section 19 – 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 20 – 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 21 – 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 22 – 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 23 – 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 24 – 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 25 – Searching Algorithms

Introduction to Searching Algorithms

Linear Search

Linear Search in Python

Binary Search

Binary Search in Python

Time Complexity of Binary Search

Section 26 – Graph Algorithms

What is a Graph? Why Graph?

Graph Terminology

Types of Graph

Graph Representation

The graph in Java using Adjacency Matrix

The graph in Java using Adjacency List

Section 27 – Graph Traversal

Breadth-First Search Algorithm (BFS)

Breadth-First Search Algorithm (BFS) in Java – Adjacency Matrix

Breadth-First Search Algorithm (BFS) in Java – Adjacency List

Time Complexity of Breadth-First Search (BFS) Algorithm

Depth First Search (DFS) Algorithm

Depth First Search (DFS) Algorithm in Java – Adjacency List

Depth First Search (DFS) Algorithm in Java – Adjacency Matrix

Time Complexity of Depth First Search (DFS) Algorithm

BFS Traversal vs DFS Traversal

Section 28 – Topological Sort

What is Topological Sort?

Topological Sort Algorithm

Topological Sort using Adjacency List

Topological Sort using Adjacency Matrix

Time and Space Complexity of Topological Sort

Section 29 – Single Source Shortest Path Problem

what is Single Source Shortest Path Problem?

Breadth-First Search (BFS) for Single Source Shortest Path Problem (SSSPP)

BFS for SSSPP in Java using Adjacency List

BFS for SSSPP in Java using Adjacency Matrix

Time and Space Complexity of BFS for SSSPP

Why does BFS not work with Weighted Graph?

Why does DFS not work for SSSP?

Section 30 – Dijkstra’s Algorithm

Dijkstra’s Algorithm for SSSPP

Dijkstra’s Algorithm in Java – 1

Dijkstra’s Algorithm in Java – 2

Dijkstra’s Algorithm with Negative Cycle

Section 31 – Bellman-Ford Algorithm

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

Section 32 – All Pairs Shortest Path Problem

All pairs shortest path problem

Dry run for All pair shortest path

Section 33 – Floyd Warshall

Floyd Warshall Algorithm

Why Floyd Warshall?

Floyd Warshall with negative cycle,

Floyd Warshall in Java,

BFS vs Dijkstra vs Bellman Ford vs Floyd Warshall,

Section 34 – Minimum Spanning Tree

Minimum Spanning Tree,

Disjoint Set,

Disjoint Set in Java,

Section 35 – Kruskal’s and Prim’s Algorithms

Kruskal Algorithm,

Kruskal Algorithm in Python,

Prim’s Algorithm,

Prim’s Algorithm in Python,

Prim’s vs Kruskal

Section 36 – Cracking Graph and Tree Interview Questions (Amazon, Facebook, Apple, Microsoft)

Section 37 – Greedy Algorithms

What is a 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 38 – Divide and Conquer Algorithms

What is a Divide and Conquer Algorithm?

Common Divide and Conquer algorithms

How to solve the Fibonacci series using the Divide and Conquer approach?

Number Factor

Number Factor in Java

House Robber

House Robber Problem in Java

Convert one string to another

Convert One String to another in Java

Zero One Knapsack problem

Zero One Knapsack problem in Java

Longest Common Sequence Problem

Longest Common Subsequence in Java

Longest Palindromic Subsequence Problem

Longest Palindromic Subsequence in Java

Minimum cost to reach the Last cell problem

Minimum Cost to reach the Last Cell in 2D array using Java

Number of Ways to reach the Last Cell with given Cost

Number of Ways to reach the Last Cell with given Cost in Java

Section 39 – Dynamic Programming

What is Dynamic Programming? (Overlapping property)

Where does the name of DC come from?

Top-Down with Memoization

Bottom-Up with Tabulation

Top-Down vs Bottom Up

Is Merge Sort Dynamic Programming?

Number Factor Problem using Dynamic Programming

Number Factor: Top-Down and Bottom-Up

House Robber Problem using Dynamic Programming

House Robber: Top-Down and Bottom-Up

Convert one string to another using Dynamic Programming

Convert String using Bottom Up

Zero One Knapsack using Dynamic Programming

Zero One Knapsack – Top Down

Zero One Knapsack – Bottom Up

Section 40 – CHALLENGING Dynamic Programming Problems

Longest repeated Subsequence Length problem

Longest Common Subsequence Length problem

Longest Common Subsequence  problem

Diff Utility

Shortest Common Subsequence  problem

Length of Longest Palindromic Subsequence

Subset Sum Problem

Egg Dropping Puzzle

Maximum Length Chain of Pairs

Section 41 – A Recipe for Problem Solving

Introduction

Step 1 – Understand the problem

Step 2 – Examples

Step 3 – Break it Down

Step 4 – Solve or Simplify

Step 5 – Look Back and Refactor

Section 41 – Wild West

Download

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[fortunatelycoldengineer]

Circular Singly Linked List . . . . for more information http://bit.ly/3JB3o07 check the above link

[felord]

DataStructure Homework1-Singly-Linked List Solved

DataStructure Homework1-Singly-Linked List Solved

You are to code a non-circular singly-linked list with head and tail references. A linked list is a collection of nodes, each having a data item and a reference pointing to the next node. The next reference for the tail/last node in this list will point to null. Do not use a phantom node to represent the start or end of your list. A phantom or sentinel node is a node that does not store data held…

[supermayank17]

What is Circular Linked list in Data Structures?

A circular linked list is a linked list in which the last node points to the first node. A circular linked list can be a singly circular linked list or a doubly circular linked list. We will be studying the singly circular linked list because the doubly circular linked list is not much used because it is expensive.

To read more about Circular Linked lists refer to this link .

[sunbeaminfo]

Elevate your programming expertise by enrolling in the Data Structures and Algorithms course at Sunbeam Institute, Pune. This comprehensive program is designed for students, freshers, and working professionals aiming to deepen their understanding of essential data structures and algorithms using Java.

Course Highlights:

Algorithm Analysis: Learn to evaluate time and space complexity for efficient coding.

Linked Lists: Master various types, including singly, doubly, and circular linked lists.

Stacks and Queues: Understand their implementation using arrays and linked lists, and apply them in expression evaluation and parenthesis balancing.

Sorting and Searching: Gain proficiency in algorithms like Quick Sort, Merge Sort, Heap Sort, Linear Search, Binary Search, and Hashing.

Trees and Graphs: Explore tree traversals, Binary Search Trees (BST), and graph algorithms such as Prim’s MST, Kruskal’s MST, Dijkstra's, and A* search.

Course Details:

Duration: 60 hours

Schedule: Weekdays (Monday to Saturday), 5:00 PM to 8:00 PM

Upcoming Batch: January 27, 2025, to February 18, 2025

Fees: ₹7,500 (including 18% GST)

Prerequisites:

Basic knowledge of Java programming, including classes, objects, generics, and Java collections (e.g., ArrayList).

Why Choose Sunbeam Institute?

Sunbeam Institute is renowned for its effective IT training programs in Pune, offering a blend of theoretical knowledge and practical application to ensure a thorough understanding of complex concepts.

[cloudwebworld-blog]

INTRODUCTION TO 64 BIT WINDOWS ASSEMBLY PROGRAMMING 1ST EDITION

This book introduces programmers to 64 bit Intel assembly language using the Microsoft Windows operating system. The book also discusses how to use the free integrated development environment, ebe, designed by the author specifically to meet the needs of assembly language programmers. Ebe is a C++ program which uses the Qt library to implement a GUI environment consisting of a source window, a data window, a register window, a floating point register window, a backtrace window, a console window, a terminal window, a project window and a pair of teaching tools called the “Toy Box” and the “Bit Bucket”. The source window includes a full-featured text editor with convenient controls for assembling, linking and debugging a program. The project facility allows a program to be built from C source code files and assembly source files. Assembly is performed automatically using the yasm assembler and linking is performed with ld or gcc. Debugging operates by transparently sending commands into the gdb debugger while automatically displaying registers and variables after each debugging step. The Toy Box allows the use to enter variable definitions and expressions in either C++ or Fortran and it builds a program to evaluate the expressions. Then the user can inspect the format of each expression. The Bit Bucket allows the user to explore how the computer stores and manipulates integers and floating point numbers. Additional information about ebe can be found at http://www.rayseyfarth.com. The book is intended as a first assembly language book for programmers experienced in high level programming in a language like C or C++. The assembly programming is performed using the yasm assembler automatically from the ebe IDE under the Linux operating system. The book primarily teaches how to write assembly code compatible with C programs. The reader will learn to call C functions from assembly language and to call assembly functions from C in addition to writing complete programs in assembly language. The gcc compiler is used internally to compile C programs. The book starts early emphasizing using ebe to debug programs. Being able to single-step assembly programs is critical in learning assembly programming. Ebe makes this far easier than using gdb directly. Highlights of the book include doing input/output programming using Windows API functions and the C library, implementing data structures in assembly language and high performance assembly language programming. Early chapters of the book rely on using the debugger to observe program behavior. After a chapter on functions, the user is prepared to use printf and scanf from the C library to perform I/O. The chapter on data structures covers singly linked lists, doubly linked circular lists, hash tables and binary trees. Test programs are presented for all these data structures. There is a chapter on optimization techniques and 3 chapters on specific optimizations. One chapter covers how to efficiently count the 1 bits in an array with the most efficient version using the recently-introduced popcnt instruction. Another chapter covers using SSE instructions to create an efficient implementation of the Sobel filtering algorithm. The final high performance programming chapter discusses computing correlation between data in 2 arrays. There is an AVX implementation which achieves 20.5 GFLOPs on a single core of a Core i7 CPU. A companion web site, http://www.rayseyfarth.com, has a collection of PDF slides which instructors can use for in-class presentations and source code for sample programs.

https://video-course.com/2019/05/08/introduction-to-64-bit-windows-assembly-programming-1st-edition/