🔹 Placement Ready in 3 Months! 🔹

1. Month 1: Aptitude
- Quantitative Aptitude, Logical Reasoning, Verbal Ability
- Daily practice and mock tests

2. Month 1 & 2: Course Fundamentals
- OOPS, DBMS, OS, CN, Java, C++
- Study plan and resources

3. Months 1, 2, & 3: Coding
- Data Structures and Algorithms (DSA)
- Practice on platforms like Hackerrank, Codechef, and Leetcode

4. Projects, Skills, and Internships
- Full-stack or ML projects
- Internship experiences and interview prep

5. Month 3: Mock Interviews
- Practice with Pramp and peers

Top Coding Interview Resources to prepare for Microsoft, Amazon, Meta, Apple, Adobe, VMware, Visa, Twitter, LinkedIn, JP Morgan, Goldman Sachs, Oracle and Walmart 👇👇 https://topmate.io/coding/951517

All the best 👍👍

Here is how you can explain your project in an interview 🔥

When you’re in an interview, it’s super important to know how to talk about your projects in a way that impresses the interviewer. Here are some key points to help you do just that:

➤ 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.

➤ 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.

➤ 𝗣𝗿𝗼𝗽𝗼𝘀𝗲𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?

➤ 𝗬𝗼𝘂𝗿 𝗥𝗼𝗹𝗲:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure it’s clear whether you were leading the project, a key player, or supporting the team.

➤ 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗼𝗼𝗹𝘀:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.

➤ 𝗜𝗺𝗽𝗮𝗰𝘁 𝗮𝗻𝗱 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got. This helps show the project was a success and highlights your contribution.

➤ 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻:
- If you worked with a team, talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the team’s success?

➤ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁:
- Reflect on what you learned from the project. How did it help you grow professionally? What new skills did you gain, and what would you do differently next time?

➤ 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- Know why you chose the project, what your role was, what decisions you made, and how the results compared to what you expected.
- Be clear on the scope of the project whether it was a long-term effort or a quick task.
- If there’s a pause after you describe the project, don’t hesitate to ask if they’d like more details or if there’s a specific part they’re interested in.

Remember, 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗸𝗲𝘆. You might have done great work, but if you don’t explain it well, it’s hard for the interviewer to understand your impact. So, practice explaining your projects with clarity.

By focusing on clear communication, you can showcase your skills more effectively and increase your chances of landing the job.

make sure to Scroll through the above messages 💞 you will definitely find more interesting things 💝

All the best 👍👍

Here are some of the hardest questions you might face in an interview.

Practice these using the 𝟯-𝟳-𝟭𝟱 𝗿𝘂𝗹𝗲:

First solve the question, then note down the answer. After three days, try to remember the question from the answer and solve it again.

Repeat the same after 7 and 15 days.

This way, you'll solve the same question 4 times in 15 days, making it easier if you encounter it again.

𝟭. 𝗔𝗿𝗿𝗮𝘆𝘀 & 𝗦𝘁𝗿𝗶𝗻𝗴𝘀
- Minimum Window Substring
- Trapping Rain Water
- Largest Rectangle in Histogram

𝟮. 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁𝘀
- Merge k Sorted Lists
- Reverse Nodes in k-Group
- LFU Cache

𝟯. 𝗧𝗿𝗲𝗲𝘀
- Binary Tree Maximum Path Sum
- Serialize and Deserialize Binary Tree
- Vertical Order Traversal of a Binary Tree

𝟰. 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴
- Edit Distance
- Burst Balloons
- Shortest Common Supersequence

𝟱. 𝗚𝗿𝗮𝗽𝗵𝘀
- Alien Dictionary
- Minimum Cost to Make at Least One Valid Path in a Grid
- Swim in Rising Water

𝟲. 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝗼𝗻 & 𝗕𝗮𝗰𝗸𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴
- N-Queens II
- Sudoku Solver
- Word Search II

𝟳. 𝗦𝗼𝗿𝘁𝗶𝗻𝗴 & 𝗦𝗲𝗮𝗿𝗰𝗵𝗶𝗻𝗴
- Count of Smaller Numbers After Self
- Median of Two Sorted Arrays
- Split Array Largest Sum

𝟴. 𝗗𝗲𝘀𝗶𝗴𝗻
- Design Search Autocomplete System
- Design In-Memory File System
- Design Excel Sum Formula

𝟵. 𝗚𝗿𝗲𝗲𝗱𝘆
- Minimum Number of Arrows to Burst Balloons
- Candy
- Patching Array

𝟭𝟬. 𝗕𝗶𝘁 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻
- Maximum Product of Word Lengths
- Smallest Sufficient Team
- Minimum Cost to Connect Two Groups of Points

𝟭𝟭. 𝗧𝘄𝗼 𝗣𝗼𝗶𝗻𝘁𝗲𝗿𝘀
- Minimum Window Subsequence
- Minimum Operations to Make a Subsequence
- Minimum Adjacent Swaps to Reach the Kth Smallest Number

𝟭𝟮. 𝗛𝗲𝗮𝗽
- Minimum Number of Refueling Stops
- Sliding Window Median
- Minimum Number of K Consecutive Bit Flips

By following the 3-7-15 rule and practicing these tough questions regularly, you'll build strong problem-solving skills and be well-prepared for your interviews.

Keep pushing yourself, and remember, consistency is key.

Top Coding Interview Resources to prepare for Microsoft, Amazon, Meta, Apple, Adobe, VMware, Visa, Twitter, LinkedIn, JP Morgan, Goldman Sachs, Oracle and Walmart 👇👇 https://topmate.io/coding/951517

All the best 👍👍

Free Placement Resources
👇👇
https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g

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Here are some interview preparation tips 👇👇

Technical Interview
1. Review Core Concepts:
- Data Structures: Be comfortable with LinkedLists, Trees, Graphs, and their representations.
- Algorithms: Brush up on searching and sorting algorithms, time complexities, and common algorithms (like Dijkstra’s or A*).
- Programming Languages: Ensure you understand the language you are most comfortable with (e.g., C++, Java, Python) and know its standard library functions.

2. Practice Coding Problems:
- Utilize platforms like LeetCode, HackerRank, or CodeSignal to practice medium-level coding questions. Focus on common patterns and problem-solving strategies.

3. Mock Interviews: Conduct mock technical interviews with peers or mentors to build confidence and receive feedback.

Personal Interview
1. Prepare Your Story:
- Outline your educational journey, achievements, and any relevant projects. Emphasize experiences that demonstrate leadership, teamwork, and problem-solving skills.
- Be ready to discuss your challenges and how you overcame them.

2. Articulate Your Goals:
- Be clear about why you want to join the program and how it aligns with your career aspirations. Reflect on what you hope to gain from the experience.

- Focus on Fundamentals:
Be thorough with basic subjects like Operating Systems, Networking, OOP, and Databases. Clear concepts are key for technical interviews.

2. Common Interview Questions:

DSA:
- Implement various data structures like Linked Lists, Trees, Graphs, Stacks, and Queues.
- Understand searching and sorting algorithms: Binary Search, Merge Sort, Quick Sort, etc.
- Solve problems involving HashMaps, Sets, and other collections.

Sample DSA Questions
- Reverse a linked list.
- Find the first non-repeating character in a string.
- Detect a cycle in a graph.
- Implement a queue using two stacks.
- Find the lowest common ancestor in a binary tree.

3. Key Topics to Focus On

DSA:
- Arrays, Strings, Linked Lists, Trees, Graphs
- Recursion, Backtracking, Dynamic Programming
- Sorting and Searching Algorithms
- Time and Space Complexity

Core Subjects
- Operating Systems: Concepts like processes, threads, deadlocks, concurrency, and memory management.
- Database Management Systems (DBMS): Understanding SQL, Normalization, and database design.
- Object-Oriented Programming (OOP): Know about inheritance, polymorphism, encapsulation, and design patterns.

5. Tips
- Optimize Your Code: Write clean, optimized code. Discuss time and space complexities during interviews.
- Review Your Projects: Be ready to explain your past projects, the challenges you faced, and the technologies you used.....

Here are the most asked DSA questions to ace your next interview


➤ 𝗔𝗿𝗿𝗮𝘆𝘀 𝗮𝗻𝗱 𝗦𝘁𝗿𝗶𝗻𝗴𝘀:
1. Find the maximum sum subarray.
2. Find all substrings that are palindromes.
3. Implement the "two sum" problem.
4. Implement Kadane's algorithm for maximum subarray sum.
5. Find the missing number in an array of integers.
6. Merge two sorted arrays into one sorted array.
7. Check if a string is a palindrome.
8. Find the first non-repeating character in a string.
9. Write a program to remove duplicates from a sorted array.

➤ 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁𝘀:
10. Reverse a linked list.
11. Detect a cycle in a linked list.
12. Find the middle of a linked list.
13. Merge two sorted linked lists.
14. Implement a stack using linked list.
15. Find the intersection point of two linked lists.

➤ 𝗦𝘁𝗮𝗰𝗸𝘀 𝗮𝗻𝗱 𝗤𝘂𝗲𝘂𝗲𝘀:
16. Implement a stack using an array.
17. Implement a stack that supports push, pop, top, and retrieving the minimum element.
18. Implement a circular queue.
19. Design a max stack that supports push, pop, top, retrieve maximum element.
20. Design a queue using stacks.

➤ 𝗧𝗿𝗲𝗲𝘀 𝗮𝗻𝗱 𝗕𝗶𝗻𝗮𝗿𝘆 𝗦𝗲𝗮𝗿𝗰𝗵 𝗧𝗿𝗲𝗲𝘀:
21. Find the height of a binary tree.
22. Find the lowest common ancestor of two nodes in a binary tree.
23. Validate if a binary tree is a valid binary search tree.
24. Serialize and deserialize a binary tree.
25. Implement an inorder traversal of a binary tree.
26. Find the diameter of a binary tree.
27. Convert a binary tree to its mirror tree.

➤ 𝗚𝗿𝗮𝗽𝗵𝘀:
28. Implement depth-first search (DFS).
29. Implement breadth-first search (BFS).
30. Find the shortest path between two nodes in an unweighted graph.
31. Detect a cycle in an undirected graph using DFS.
32. Check if a graph is bipartite.
33. Find the number of connected components in an undirected graph.
34. Find bridges in a graph.

➤ 𝗦𝗼𝗿𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗲𝗮𝗿𝗰𝗵𝗶𝗻𝗴:
35. Implement (bubble, insertion, selection, merge) sort.
36. Implement quicksort.
37. Implement binary search.
38. Implement interpolation search.
39. Find the kth smallest element in an array.
40. Given an array of integers, count the number of inversions it has. An inversion occurs when two elements in the array are out of order.

Best DSA RESOURCES: https://topmate.io/coding/886874

All the best 👍👍

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COMMON TERMINOLOGIES IN PYTHON - PART 1

Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them?

In this series, we would be looking at the common Terminologies in python.

It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few:

IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts.

Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately

System Python - This is the version of python that comes with your operating system

Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions

REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed)

Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed.

Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function

Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g.
>>> print("Hello World")
Hello World
Where Hello World is your return value.

Note: A return value can be any of these variable types: handle, integer, object, or string

Script - This is a file where you store your python code in a text file and execute all of the code with a single command

Script files - this is a file containing a group of python scripts

Here are the top 10 most-asked React interview questions🎯

🌴 How does the virtual DOM work in React?
🌴 What are React Fiber and how does React's reconciliation algorithm work?
🌴 What is the difference between useLayoutEffect and useEffect?
🌴 How do you implement code splitting in a React application?
🌴 What is React.memo, and how does it differ from useMemo?
🌴 How can you optimize performance in a React application?
🌴 What are the different ways to manage state in React (local, global, server state)?
🌴 What is the context API in React, and when would you use it?
🌴 How do you prevent unnecessary re-renders in React components?
🌴 How do you handle SSR hydration issues in React applications?

Take these questions as a starting point and build your core logic through them before moving to more advanced ones. As problem-solving is the number 1 skill interviewers’ test💯

Free Programming Resources
👇👇
https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g

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20 Algorithms Every programmer should know


- Merge Sort

- Quick Sort

- Quickselect

- Binary Search

- Depth-First Search (DFS)

- Breadth-First Search (BFS)

- Dijkstra's Algorithm

- Dynamic Programming

- Fibonacci Sequence

- Longest Common Subsequence

- Binary Tree Traversals (Inorder, Preorder, Postorder)

- Heap Sort

- Knapsack Problem

- Floyd-Warshall Algorithm

- Union Find

- Topological Sort

- Kruskal's Algorithm

- Prim's Algorithm

- Bellman-Ford Algorithm

- Kadane's Algorithm

- Flood Fill Algorithm

Bonus:

- Rabin-Karp Algorithm

- A* Algorithm

Best DSA RESOURCES: https://topmate.io/coding/886874

All the best 👍👍

Complete DSA Roadmap

|-- Basic_Data_Structures
| |-- Arrays
| |-- Strings
| |-- Linked_Lists
| |-- Stacks
| └─ Queues
|
|-- Advanced_Data_Structures
| |-- Trees
| | |-- Binary_Trees
| | |-- Binary_Search_Trees
| | |-- AVL_Trees
| | └─ B-Trees
| |
| |-- Graphs
| | |-- Graph_Representation
| | | |- Adjacency_Matrix
| | | └ Adjacency_List
| | |
| | |-- Depth-First_Search
| | |-- Breadth-First_Search
| | |-- Shortest_Path_Algorithms
| | | |- Dijkstra's_Algorithm
| | | └ Bellman-Ford_Algorithm
| | |
| | └─ Minimum_Spanning_Tree
| | |- Prim's_Algorithm
| | └ Kruskal's_Algorithm
| |
| |-- Heaps
| | |-- Min_Heap
| | |-- Max_Heap
| | └─ Heap_Sort
| |
| |-- Hash_Tables
| |-- Disjoint_Set_Union
| |-- Trie
| |-- Segment_Tree
| └─ Fenwick_Tree
|
|-- Algorithmic_Paradigms
| |-- Brute_Force
| |-- Divide_and_Conquer
| |-- Greedy_Algorithms
| |-- Dynamic_Programming
| |-- Backtracking
| |-- Sliding_Window_Technique
| |-- Two_Pointer_Technique
| └─ Divide_and_Conquer_Optimization
| |-- Merge_Sort_Tree
| └─ Persistent_Segment_Tree
|
|-- Searching_Algorithms
| |-- Linear_Search
| |-- Binary_Search
| |-- Depth-First_Search
| └─ Breadth-First_Search
|
|-- Sorting_Algorithms
| |-- Bubble_Sort
| |-- Selection_Sort
| |-- Insertion_Sort
| |-- Merge_Sort
| |-- Quick_Sort
| └─ Heap_Sort
|
|-- Graph_Algorithms
| |-- Depth-First_Search
| |-- Breadth-First_Search
| |-- Topological_Sort
| |-- Strongly_Connected_Components
| └─ Articulation_Points_and_Bridges
|
|-- Dynamic_Programming
| |-- Introduction_to_DP
| |-- Fibonacci_Series_using_DP
| |-- Longest_Common_Subsequence
| |-- Longest_Increasing_Subsequence
| |-- Knapsack_Problem
| |-- Matrix_Chain_Multiplication
| └─ Dynamic_Programming_on_Trees
|
|-- Mathematical_and_Bit_Manipulation_Algorithms
| |-- Prime_Numbers_and_Sieve_of_Eratosthenes
| |-- Greatest_Common_Divisor
| |-- Least_Common_Multiple
| |-- Modular_Arithmetic
| └─ Bit_Manipulation_Tricks
|
|-- Advanced_Topics
| |-- Trie-based_Algorithms
| | |-- Auto-completion
| | └─ Spell_Checker
| |
| |-- Suffix_Trees_and_Arrays
| |-- Computational_Geometry
| |-- Number_Theory
| | |-- Euler's_Totient_Function
| | └─ Mobius_Function
| |
| └─ String_Algorithms
| |-- KMP_Algorithm
| └─ Rabin-Karp_Algorithm
|
|-- OnlinePlatforms
| |-- LeetCode
| |-- HackerRank

Best DSA RESOURCES: https://topmate.io/coding/886874

Credits: https://t.me/free4unow_backup

All the best 👍👍

Essential 22 DSA patterns for coding interviews 👇👇

1. Fast and Slow Pointer
- Cycle detection method
- O(1) space efficiency
- Linked list problems

2. Merge Intervals
- Sort and merge
- O(n log n) complexity
- Overlapping interval handling

3. Sliding Window
- Fixed/variable window
- O(n) time optimization
- Subarray/substring problems

4. Islands (Matrix Traversal)
- DFS/BFS traversal
- Connected component detection
- 2D grid problems

5. Two Pointers
- Dual pointer strategy
- Linear time complexity
- Array/list problems

6. Cyclic Sort
- Sorting in cycles
- O(n) time complexity
- Constant space usage

7. In-place Reversal of Linked List
- Reverse without extra space
- O(n) time efficiency
- Pointer manipulation technique

8. Breadth First Search
- Level-by-level traversal
- Uses queue structure
- Shortest path problems

9. Depth First Search
- Recursive/backtracking approach
- Uses stack (or recursion)
- Tree/graph traversal

10. Two Heaps
- Max and min heaps
- Median tracking efficiently
- O(log n) insertions

11. Subsets
- Generate all subsets
- Recursive or iterative
- Backtracking or bitmasking

12. Modified Binary Search
- Search in variations
- O(log n) time
- Rotated/specialized arrays

13. Bitwise XOR
- Toggle bits operation
- O(1) space complexity
- Efficient for pairing

14. Top 'K' elements
- Use heap/quickselect
- O(n log k) time
- Efficient selection problem

15. K-way Merge
- Merge sorted lists
- Min-heap based approach
- O(n log k) complexity

16. 0/1 Knapsack (Dynamic Programming)
- Choose or skip items
- O(n * W) complexity
- Maximize value selection

17. Unbounded Knapsack (Dynamic Programming)
- Unlimited item choices
- O(n * W) complexity
- Multiple item selection

18. Topological Sort (Graphs)
- Directed acyclic graph
- Order dependency resolution
- Uses DFS or BFS

19. Monotonic Stack
- Maintain increasing/decreasing stack
- Optimized for range queries
- O(n) time complexity

20. Backtracking
- Recursive decision-making
- Explore all possibilities
- Pruning with constraints

21. Union Find
- Track and merge connected components
- Used for disjoint sets
- Great for network connectivity

22. Greedy Algorithm
- Make locally optimal choices
- Efficient for problems with optimal substructure
- Covers tasks like activity selection, minimum coins

Best DSA Resources: 👇
https://topmate.io/coding/886874

All the best 👍👍

Life-changing advice for college students 👇👇
https://medium.com/@data_analyst/life-changing-advice-for-college-students-9b41c74f188d

Worth sharing with you guys ❤️

Top 20 most asked DSA questions to ace your next interview:

➤ Arrays and Strings:

1. Find the maximum sum subarray.

2. Implement the "two sum" problem.

3. Implement Kadane's algorithm for maximum subarray sum.

4. Find the missing number in an array of integers.

5. Merge two sorted arrays into one sorted array.

6. Check if a string is a palindrome.

➤ Linked Lists:

7. Reverse a linked list.

8. Detect a cycle in a linked list.

9. Find the middle of a linked list.

10. Merge two sorted linked lists.

➤ Stacks and Queues:

11. Implement a stack that supports push, pop, top, and retrieving the minimum element.

12. Implement a circular queue.

13. Design a queue using stacks.



➤ Trees and Binary Search Trees:

14. Find the height of a binary tree.

15. Validate if a binary tree is a valid binary search tree.

16. Implement an inorder traversal of a binary tree.



➤ Graphs:

17. Implement depth-first search (DFS).

18. Find the shortest path between two nodes in an unweighted graph.


➤ Sorting and Searching:

19. Implement quicksort.

20. Implement binary search.

Best DSA Resources: 👇
https://topmate.io/coding/886874

All the best 👍👍

DSA INTERVIEW QUESTIONS AND ANSWERS

1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.

2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.

3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.

4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.

5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).

6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.

7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.

8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.

Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).

Time complexity: best case O(n2); worst O(n2)

Space complexity: worst O(1)

9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them

10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.

11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect

You can check these resources for Coding interview Preparation

Credits: https://t.me/free4unow_backup

All the best 👍👍

🔹 Placement Ready in 3 Months! 🔹

1. Month 1: Aptitude
- Quantitative Aptitude, Logical Reasoning, Verbal Ability
- Daily practice and mock tests

2. Month 1 & 2: Course Fundamentals
- OOPS, DBMS, OS, CN, Java, C++
- Study plan and resources

3. Months 1, 2, & 3: Coding
- Data Structures and Algorithms (DSA)
- Practice on platforms like Hackerrank, Codechef, and Leetcode

4. Projects, Skills, and Internships
- Full-stack or ML projects
- Internship experiences and interview prep

5. Month 3: Mock Interviews
- Practice with Pramp and peers

Top Coding Interview Resources to prepare for Microsoft, Amazon, Meta, Apple, Adobe, VMware, Visa, Twitter, LinkedIn, JP Morgan, Goldman Sachs, Oracle and Walmart 👇👇 https://topmate.io/coding/951517

All the best 👍👍

How long are coding interviews?
The phone screen portion of the coding interview typically lasts up to one hour. The second, more technical part of the interview can take multiple hours.

Where can I practice coding?
There are many ways to practice coding and prepare for your coding interview. LeetCode provides practice opportunities in more than 14 languages and more than 1,500 sample problems. Applicants can also practice their coding skills and interview prep with HackerRank.

How do I know if my coding interview went well?
There are a variety of indicators that your coding interview went well. These may include going over the allotted time, being introduced to additional team members, and receiving a quick response to your thank you email.

In class, there are some students who are really good at coding from the start, and seeing them can make us feel quite demotivated, especially since they often appear overconfident.

But it's not important how much someone already knows. If you start and practice consistently, it's not that tough to match their level or even surpass them.

And often, these overconfident people don’t perform as well as you can because you have the desire to learn, while they think they already know everything.

So, my friend, don’t get demotivated—just give it time!