Free Placement Resources
๐๐
https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g
Like for more โค๏ธ
๐๐
https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g
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๐4โค2
Here are some interview preparation tips ๐๐
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 Dijkstraโs 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.....
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 Dijkstraโs 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.....
๐9โค1
Here are the most asked DSA questions to ace your next interview
โค ๐๐ฟ๐ฟ๐ฎ๐๐ ๐ฎ๐ป๐ฑ ๐ฆ๐๐ฟ๐ถ๐ป๐ด๐:
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.
โค ๐๐ถ๐ป๐ธ๐ฒ๐ฑ ๐๐ถ๐๐๐:
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.
โค ๐ฆ๐๐ฎ๐ฐ๐ธ๐ ๐ฎ๐ป๐ฑ ๐ค๐๐ฒ๐๐ฒ๐:
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.
โค ๐ง๐ฟ๐ฒ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐๐ถ๐ป๐ฎ๐ฟ๐ ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐ง๐ฟ๐ฒ๐ฒ๐:
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.
โค ๐๐ฟ๐ฎ๐ฝ๐ต๐:
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.
โค ๐ฆ๐ผ๐ฟ๐๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐ป๐ด:
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 ๐๐
โค ๐๐ฟ๐ฟ๐ฎ๐๐ ๐ฎ๐ป๐ฑ ๐ฆ๐๐ฟ๐ถ๐ป๐ด๐:
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.
โค ๐๐ถ๐ป๐ธ๐ฒ๐ฑ ๐๐ถ๐๐๐:
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.
โค ๐ฆ๐๐ฎ๐ฐ๐ธ๐ ๐ฎ๐ป๐ฑ ๐ค๐๐ฒ๐๐ฒ๐:
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.
โค ๐ง๐ฟ๐ฒ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐๐ถ๐ป๐ฎ๐ฟ๐ ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐ง๐ฟ๐ฒ๐ฒ๐:
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.
โค ๐๐ฟ๐ฎ๐ฝ๐ต๐:
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.
โค ๐ฆ๐ผ๐ฟ๐๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐ป๐ด:
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 ๐๐
๐11โค4๐1
Top 10 Sites to review your resume for free:
1. Zety Resume Builder
2. Resumonk
3. Free Resume Builder
4. VisualCV
5. Cvmaker
6. ResumUP
7. Resume Genius
8. Resumebuilder
9. Resume Baking
10. Enhancv
1. Zety Resume Builder
2. Resumonk
3. Free Resume Builder
4. VisualCV
5. Cvmaker
6. ResumUP
7. Resume Genius
8. Resumebuilder
9. Resume Baking
10. Enhancv
โค14
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
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
๐11
Here are the top 10 most-asked React interview questions๐ฏ
๐ด How does the virtual DOM work in React?
๐ด What are React Fiber and how does React's reconciliation algorithm work?
๐ด What is the difference between useLayoutEffect and useEffect?
๐ด How do you implement code splitting in a React application?
๐ด What is React.memo, and how does it differ from useMemo?
๐ด How can you optimize performance in a React application?
๐ด What are the different ways to manage state in React (local, global, server state)?
๐ด What is the context API in React, and when would you use it?
๐ด How do you prevent unnecessary re-renders in React components?
๐ด 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 interviewersโ test๐ฏ
Free Programming Resources
๐๐
https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g
Like for more โค๏ธ
๐ด How does the virtual DOM work in React?
๐ด What are React Fiber and how does React's reconciliation algorithm work?
๐ด What is the difference between useLayoutEffect and useEffect?
๐ด How do you implement code splitting in a React application?
๐ด What is React.memo, and how does it differ from useMemo?
๐ด How can you optimize performance in a React application?
๐ด What are the different ways to manage state in React (local, global, server state)?
๐ด What is the context API in React, and when would you use it?
๐ด How do you prevent unnecessary re-renders in React components?
๐ด 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 interviewersโ test๐ฏ
Free Programming Resources
๐๐
https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g
Like for more โค๏ธ
๐7โค1
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 ๐๐
- 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 ๐๐
๐18
Complete DSA Roadmap
|-- Basic_Data_Structures
| |-- Arrays
| |-- Strings
| |-- Linked_Lists
| |-- Stacks
| โโ Queues
|
|-- Advanced_Data_Structures
| |-- Trees
| | |-- Binary_Trees
| | |-- Binary_Search_Trees
| | |-- AVL_Trees
| | โโ B-Trees
| |
| |-- Graphs
| | |-- Graph_Representation
| | | |- Adjacency_Matrix
| | | โ Adjacency_List
| | |
| | |-- Depth-First_Search
| | |-- Breadth-First_Search
| | |-- Shortest_Path_Algorithms
| | | |- Dijkstra's_Algorithm
| | | โ Bellman-Ford_Algorithm
| | |
| | โโ Minimum_Spanning_Tree
| | |- Prim's_Algorithm
| | โ Kruskal's_Algorithm
| |
| |-- Heaps
| | |-- Min_Heap
| | |-- Max_Heap
| | โโ Heap_Sort
| |
| |-- Hash_Tables
| |-- Disjoint_Set_Union
| |-- Trie
| |-- Segment_Tree
| โโ Fenwick_Tree
|
|-- Algorithmic_Paradigms
| |-- Brute_Force
| |-- Divide_and_Conquer
| |-- Greedy_Algorithms
| |-- Dynamic_Programming
| |-- Backtracking
| |-- Sliding_Window_Technique
| |-- Two_Pointer_Technique
| โโ Divide_and_Conquer_Optimization
| |-- Merge_Sort_Tree
| โโ Persistent_Segment_Tree
|
|-- Searching_Algorithms
| |-- Linear_Search
| |-- Binary_Search
| |-- Depth-First_Search
| โโ Breadth-First_Search
|
|-- Sorting_Algorithms
| |-- Bubble_Sort
| |-- Selection_Sort
| |-- Insertion_Sort
| |-- Merge_Sort
| |-- Quick_Sort
| โโ Heap_Sort
|
|-- Graph_Algorithms
| |-- Depth-First_Search
| |-- Breadth-First_Search
| |-- Topological_Sort
| |-- Strongly_Connected_Components
| โโ Articulation_Points_and_Bridges
|
|-- Dynamic_Programming
| |-- Introduction_to_DP
| |-- Fibonacci_Series_using_DP
| |-- Longest_Common_Subsequence
| |-- Longest_Increasing_Subsequence
| |-- Knapsack_Problem
| |-- Matrix_Chain_Multiplication
| โโ Dynamic_Programming_on_Trees
|
|-- Mathematical_and_Bit_Manipulation_Algorithms
| |-- Prime_Numbers_and_Sieve_of_Eratosthenes
| |-- Greatest_Common_Divisor
| |-- Least_Common_Multiple
| |-- Modular_Arithmetic
| โโ Bit_Manipulation_Tricks
|
|-- Advanced_Topics
| |-- Trie-based_Algorithms
| | |-- Auto-completion
| | โโ Spell_Checker
| |
| |-- Suffix_Trees_and_Arrays
| |-- Computational_Geometry
| |-- Number_Theory
| | |-- Euler's_Totient_Function
| | โโ Mobius_Function
| |
| โโ String_Algorithms
| |-- KMP_Algorithm
| โโ Rabin-Karp_Algorithm
|
|-- OnlinePlatforms
| |-- LeetCode
| |-- HackerRank
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.me/free4unow_backup
All the best ๐๐
|-- Basic_Data_Structures
| |-- Arrays
| |-- Strings
| |-- Linked_Lists
| |-- Stacks
| โโ Queues
|
|-- Advanced_Data_Structures
| |-- Trees
| | |-- Binary_Trees
| | |-- Binary_Search_Trees
| | |-- AVL_Trees
| | โโ B-Trees
| |
| |-- Graphs
| | |-- Graph_Representation
| | | |- Adjacency_Matrix
| | | โ Adjacency_List
| | |
| | |-- Depth-First_Search
| | |-- Breadth-First_Search
| | |-- Shortest_Path_Algorithms
| | | |- Dijkstra's_Algorithm
| | | โ Bellman-Ford_Algorithm
| | |
| | โโ Minimum_Spanning_Tree
| | |- Prim's_Algorithm
| | โ Kruskal's_Algorithm
| |
| |-- Heaps
| | |-- Min_Heap
| | |-- Max_Heap
| | โโ Heap_Sort
| |
| |-- Hash_Tables
| |-- Disjoint_Set_Union
| |-- Trie
| |-- Segment_Tree
| โโ Fenwick_Tree
|
|-- Algorithmic_Paradigms
| |-- Brute_Force
| |-- Divide_and_Conquer
| |-- Greedy_Algorithms
| |-- Dynamic_Programming
| |-- Backtracking
| |-- Sliding_Window_Technique
| |-- Two_Pointer_Technique
| โโ Divide_and_Conquer_Optimization
| |-- Merge_Sort_Tree
| โโ Persistent_Segment_Tree
|
|-- Searching_Algorithms
| |-- Linear_Search
| |-- Binary_Search
| |-- Depth-First_Search
| โโ Breadth-First_Search
|
|-- Sorting_Algorithms
| |-- Bubble_Sort
| |-- Selection_Sort
| |-- Insertion_Sort
| |-- Merge_Sort
| |-- Quick_Sort
| โโ Heap_Sort
|
|-- Graph_Algorithms
| |-- Depth-First_Search
| |-- Breadth-First_Search
| |-- Topological_Sort
| |-- Strongly_Connected_Components
| โโ Articulation_Points_and_Bridges
|
|-- Dynamic_Programming
| |-- Introduction_to_DP
| |-- Fibonacci_Series_using_DP
| |-- Longest_Common_Subsequence
| |-- Longest_Increasing_Subsequence
| |-- Knapsack_Problem
| |-- Matrix_Chain_Multiplication
| โโ Dynamic_Programming_on_Trees
|
|-- Mathematical_and_Bit_Manipulation_Algorithms
| |-- Prime_Numbers_and_Sieve_of_Eratosthenes
| |-- Greatest_Common_Divisor
| |-- Least_Common_Multiple
| |-- Modular_Arithmetic
| โโ Bit_Manipulation_Tricks
|
|-- Advanced_Topics
| |-- Trie-based_Algorithms
| | |-- Auto-completion
| | โโ Spell_Checker
| |
| |-- Suffix_Trees_and_Arrays
| |-- Computational_Geometry
| |-- Number_Theory
| | |-- Euler's_Totient_Function
| | โโ Mobius_Function
| |
| โโ String_Algorithms
| |-- KMP_Algorithm
| โโ Rabin-Karp_Algorithm
|
|-- OnlinePlatforms
| |-- LeetCode
| |-- HackerRank
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.me/free4unow_backup
All the best ๐๐
๐13โค2
Essential 22 DSA patterns for coding interviews ๐๐
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: ๐
https://topmate.io/coding/886874
All the best ๐๐
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: ๐
https://topmate.io/coding/886874
All the best ๐๐
๐7โค3
Life-changing advice for college students ๐๐
https://medium.com/@data_analyst/life-changing-advice-for-college-students-9b41c74f188d
Worth sharing with you guys โค๏ธ
https://medium.com/@data_analyst/life-changing-advice-for-college-students-9b41c74f188d
Worth sharing with you guys โค๏ธ
โค4
Top 20 most asked DSA questions to ace your next interview:
โค 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.
โค 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.
โค 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.
โค 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.
โค Graphs:
17. Implement depth-first search (DFS).
18. Find the shortest path between two nodes in an unweighted graph.
โค Sorting and Searching:
19. Implement quicksort.
20. Implement binary search.
Best DSA Resources: ๐
https://topmate.io/coding/886874
All the best ๐๐
โค 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.
โค 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.
โค 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.
โค 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.
โค Graphs:
17. Implement depth-first search (DFS).
18. Find the shortest path between two nodes in an unweighted graph.
โค Sorting and Searching:
19. Implement quicksort.
20. Implement binary search.
Best DSA Resources: ๐
https://topmate.io/coding/886874
All the best ๐๐
๐11โค5
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 ๐๐
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 ๐๐
๐16โค1
๐น Placement Ready in 3 Months! ๐น
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 ๐๐ https://topmate.io/coding/951517
All the best ๐๐
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 ๐๐ https://topmate.io/coding/951517
All the best ๐๐
๐11
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.
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.
๐9
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 donโt perform as well as you can because you have the desire to learn, while they think they already know everything.
So, my friend, donโt get demotivatedโjust give it time!
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 donโt perform as well as you can because you have the desire to learn, while they think they already know everything.
So, my friend, donโt get demotivatedโjust give it time!
โค35๐5
Are you a part of our exclusive whatsapp community?
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
https://topmate.io/coding/886874
If you're a job seeker, these well structured document DSA resources will help you to know and learn all the real time DSA & OOPS Interview questions with their exact answer. folks who are having 0-4+ years of experience have cracked the interview using this guide!
Please use the above link to avail them!๐
NOTE: -Most people hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it.
Hope this helps in your job search journey... All the best!๐โ๏ธ
If you're a job seeker, these well structured document DSA resources will help you to know and learn all the real time DSA & OOPS Interview questions with their exact answer. folks who are having 0-4+ years of experience have cracked the interview using this guide!
Please use the above link to avail them!๐
NOTE: -Most people hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it.
Hope this helps in your job search journey... All the best!๐โ๏ธ
๐7โค1
Useful Websites.pdf_20231118_154343_0000.pdf
608.9 KB
Useful Websites for Jobs & Resume
๐๐ป LIKE IF YOU WANT MORE CONTENT LIKE THIS FOR FREE ๐
๐๐ป LIKE IF YOU WANT MORE CONTENT LIKE THIS FOR FREE ๐
๐12โค2
Clear all DSA rounds,
By mastering these 20 DSA patterns
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
Best DSA Resources: ๐
https://topmate.io/coding/886874
All the best ๐๐
By mastering these 20 DSA patterns
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
Best DSA Resources: ๐
https://topmate.io/coding/886874
All the best ๐๐
๐14๐1