Top 50 Coding Interview Questions 💻🚀
1. What is the time and space complexity of your code?
2. Difference between array and linked list.
3. How does a HashMap work internally?
4. What is recursion? Give an example.
5. Explain stack vs. queue.
6. What is a binary search and when to use it?
7. Difference between BFS and DFS.
8. What is dynamic programming?
9. Solve Fibonacci using memoization.
10. Explain two-pointer technique with an example.
11. What is a sliding window algorithm?
12. Detect cycle in a linked list.
13. Find the intersection of two arrays.
14. Reverse a string or linked list.
15. Check if a string is a palindrome.
16. What are the different sorting algorithms?
17. Explain quicksort vs. mergesort.
18. What is a binary search tree (BST)?
19. Inorder, Preorder, Postorder traversals.
20. Implement LRU Cache.
21. Find the longest substring without repeating characters.
22. Explain backtracking with N-Queens problem.
23. What is a trie? Where is it used?
24. Explain bit manipulation tricks.
25. Kadane’s Algorithm for maximum subarray sum.
26. What are heaps and how do they work?
27. Find kth largest element in an array.
28. How to detect cycle in a graph?
29. Topological sort of a DAG.
30. Implement a stack using queues.
31. Explain the difference between pass by value and reference.
32. What is memoization vs. tabulation?
33. Solve the knapsack problem.
34. Find duplicate numbers in an array.
35. What are function closures in Python/JavaScript?
36. How does garbage collection work in Java?
37. What are lambda functions?
38. Explain OOPs concepts: Inheritance, Polymorphism, Encapsulation, Abstraction.
39. What is multithreading vs. multiprocessing?
40. Difference between process and thread.
41. Implement a binary heap.
42. Explain prefix sum technique.
43. Design a parking lot system.
44. Find median in a stream of numbers.
45. Detect anagram strings.
46. Serialize and deserialize a binary tree.
47. Implement a trie with insert and search.
48. Explain design patterns like Singleton, Factory.
49. Discuss trade-offs between readability and performance.
50. How do you debug a tricky bug?
💬 Tap ❤️ for detailed answers!
1. What is the time and space complexity of your code?
2. Difference between array and linked list.
3. How does a HashMap work internally?
4. What is recursion? Give an example.
5. Explain stack vs. queue.
6. What is a binary search and when to use it?
7. Difference between BFS and DFS.
8. What is dynamic programming?
9. Solve Fibonacci using memoization.
10. Explain two-pointer technique with an example.
11. What is a sliding window algorithm?
12. Detect cycle in a linked list.
13. Find the intersection of two arrays.
14. Reverse a string or linked list.
15. Check if a string is a palindrome.
16. What are the different sorting algorithms?
17. Explain quicksort vs. mergesort.
18. What is a binary search tree (BST)?
19. Inorder, Preorder, Postorder traversals.
20. Implement LRU Cache.
21. Find the longest substring without repeating characters.
22. Explain backtracking with N-Queens problem.
23. What is a trie? Where is it used?
24. Explain bit manipulation tricks.
25. Kadane’s Algorithm for maximum subarray sum.
26. What are heaps and how do they work?
27. Find kth largest element in an array.
28. How to detect cycle in a graph?
29. Topological sort of a DAG.
30. Implement a stack using queues.
31. Explain the difference between pass by value and reference.
32. What is memoization vs. tabulation?
33. Solve the knapsack problem.
34. Find duplicate numbers in an array.
35. What are function closures in Python/JavaScript?
36. How does garbage collection work in Java?
37. What are lambda functions?
38. Explain OOPs concepts: Inheritance, Polymorphism, Encapsulation, Abstraction.
39. What is multithreading vs. multiprocessing?
40. Difference between process and thread.
41. Implement a binary heap.
42. Explain prefix sum technique.
43. Design a parking lot system.
44. Find median in a stream of numbers.
45. Detect anagram strings.
46. Serialize and deserialize a binary tree.
47. Implement a trie with insert and search.
48. Explain design patterns like Singleton, Factory.
49. Discuss trade-offs between readability and performance.
50. How do you debug a tricky bug?
💬 Tap ❤️ for detailed answers!
❤11👍1
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Python Interview Question and Answers for freshers
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Cracking the coding interview free book
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Ultimate Guide to Machine Learning Interviews
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ENJOY LEARNING 👍👍
❤1
✅ 🔤 A–Z of Programming 💻
A – API (Application Programming Interface)
Interface for programs to communicate with each other.
B – Bug
Error or flaw in a program that causes incorrect results.
C – Compiler
Tool that converts code into executable machine language.
D – Debugging
Process of finding and fixing bugs in code.
E – Exception
An error detected during execution, often requiring handling.
F – Function
Reusable block of code that performs a specific task.
G – Git
Version control system for tracking code changes.
H – HTML (HyperText Markup Language)
Standard language for building web pages.
I – IDE (Integrated Development Environment)
Software that combines tools for coding, testing, and debugging.
J – JavaScript
Language for building interactive web applications.
K – Keyword
Reserved word with special meaning in a programming language.
L – Loop
Structure for repeating a block of code multiple times.
M – Module
File containing reusable code, functions, or classes.
N – Namespace
Container to organize identifiers and avoid naming conflicts.
O – Object-Oriented Programming (OOP)
Paradigm based on objects and classes to structure code.
P – Parameter
Value passed to a function to customize its behavior.
Q – Query
Instruction to retrieve data, often from databases.
R – Recursion
Function that calls itself to solve a problem.
S – Syntax
Rules that define how code must be written.
T – Try-Catch
Error-handling structure to catch exceptions.
U – UI (User Interface)
Part of the program users interact with visually.
V – Variable
Named storage for data in a program.
W – While Loop
Loop that continues as long as a condition is true.
X – XML
Markup language for storing and sharing structured data.
Y – YAML
Readable format used for config files in DevOps and backends.
Z – Zero-based Indexing
Common system where counting in arrays starts at 0.
💬 Tap ❤️ for more!
A – API (Application Programming Interface)
Interface for programs to communicate with each other.
B – Bug
Error or flaw in a program that causes incorrect results.
C – Compiler
Tool that converts code into executable machine language.
D – Debugging
Process of finding and fixing bugs in code.
E – Exception
An error detected during execution, often requiring handling.
F – Function
Reusable block of code that performs a specific task.
G – Git
Version control system for tracking code changes.
H – HTML (HyperText Markup Language)
Standard language for building web pages.
I – IDE (Integrated Development Environment)
Software that combines tools for coding, testing, and debugging.
J – JavaScript
Language for building interactive web applications.
K – Keyword
Reserved word with special meaning in a programming language.
L – Loop
Structure for repeating a block of code multiple times.
M – Module
File containing reusable code, functions, or classes.
N – Namespace
Container to organize identifiers and avoid naming conflicts.
O – Object-Oriented Programming (OOP)
Paradigm based on objects and classes to structure code.
P – Parameter
Value passed to a function to customize its behavior.
Q – Query
Instruction to retrieve data, often from databases.
R – Recursion
Function that calls itself to solve a problem.
S – Syntax
Rules that define how code must be written.
T – Try-Catch
Error-handling structure to catch exceptions.
U – UI (User Interface)
Part of the program users interact with visually.
V – Variable
Named storage for data in a program.
W – While Loop
Loop that continues as long as a condition is true.
X – XML
Markup language for storing and sharing structured data.
Y – YAML
Readable format used for config files in DevOps and backends.
Z – Zero-based Indexing
Common system where counting in arrays starts at 0.
💬 Tap ❤️ for more!
❤5
✅ Coding Interview Questions with Answers [Part-1] 💻🚀
1. What is the time and space complexity of your code?
Time complexity measures how the runtime grows with input size. Space complexity measures memory used. Always analyze both to optimize your solution.
2. What is the difference between an array and a linked list?
Arrays store elements contiguously with fast access by index. Linked lists store elements as nodes connected by pointers, allowing easy insertion/deletion but slower access.
3. How does a HashMap work internally?
It uses a hash function to convert keys into indexes in an array. Collisions are handled by chaining (linked lists) or open addressing.
4. What is recursion? Give an example.
Recursion is a function calling itself to solve smaller subproblems.
Example: Factorial(n) = n × Factorial(n-1), with base case Factorial(0) = 1.
5. Explain stack vs. queue.
Stack: Last In First Out (LIFO), like a stack of plates.
Queue: First In First Out (FIFO), like a line at a store.
6. What is a binary search and when to use it?
Binary search efficiently finds an item in a sorted array by repeatedly dividing the search interval in half. Use on sorted data for O(log n) time.
7. What is the difference between BFS and DFS?
BFS (Breadth-First Search) explores nodes level by level using a queue.
DFS (Depth-First Search) explores as far as possible along a branch using a stack or recursion.
8. What is dynamic programming?
A method to solve problems by breaking them into overlapping subproblems and storing solutions to avoid repeated work.
9. Solve Fibonacci using memoization.
Memoization stores already calculated Fibonacci numbers in a cache to reduce repeated calculations and improve performance from exponential to linear time.
10. Explain two-pointer technique with an example.
Use two pointers to traverse data structures simultaneously.
Example: Find if a sorted array has two numbers summing to a target by moving pointers from start and end inward.
💬 Double Tap ♥️ For Part-2!
1. What is the time and space complexity of your code?
Time complexity measures how the runtime grows with input size. Space complexity measures memory used. Always analyze both to optimize your solution.
2. What is the difference between an array and a linked list?
Arrays store elements contiguously with fast access by index. Linked lists store elements as nodes connected by pointers, allowing easy insertion/deletion but slower access.
3. How does a HashMap work internally?
It uses a hash function to convert keys into indexes in an array. Collisions are handled by chaining (linked lists) or open addressing.
4. What is recursion? Give an example.
Recursion is a function calling itself to solve smaller subproblems.
Example: Factorial(n) = n × Factorial(n-1), with base case Factorial(0) = 1.
5. Explain stack vs. queue.
Stack: Last In First Out (LIFO), like a stack of plates.
Queue: First In First Out (FIFO), like a line at a store.
6. What is a binary search and when to use it?
Binary search efficiently finds an item in a sorted array by repeatedly dividing the search interval in half. Use on sorted data for O(log n) time.
7. What is the difference between BFS and DFS?
BFS (Breadth-First Search) explores nodes level by level using a queue.
DFS (Depth-First Search) explores as far as possible along a branch using a stack or recursion.
8. What is dynamic programming?
A method to solve problems by breaking them into overlapping subproblems and storing solutions to avoid repeated work.
9. Solve Fibonacci using memoization.
Memoization stores already calculated Fibonacci numbers in a cache to reduce repeated calculations and improve performance from exponential to linear time.
10. Explain two-pointer technique with an example.
Use two pointers to traverse data structures simultaneously.
Example: Find if a sorted array has two numbers summing to a target by moving pointers from start and end inward.
💬 Double Tap ♥️ For Part-2!
❤5
✅ Coding Interview Questions with Answers [Part-2] 💻🚀
11. What is a sliding window algorithm?
A technique for solving problems involving arrays or strings by maintaining a window that slides over data. It helps reduce time complexity by avoiding nested loops.
Example: Finding the max sum of subarrays of size k.
12. Detect cycle in a linked list.
Use Floyd's Cycle Detection Algorithm (Tortoise and Hare).
⦁ Move two pointers at different speeds.
⦁ If they meet, a cycle exists.
⦁ To find the cycle start, reset one pointer to head and move both one step until they meet again.
13. Find the intersection of two arrays.
Use a HashSet to store elements of the first array, then check each element in the second array.
⦁ Time: O(n + m)
⦁ Space: O(min(n, m))
14. Reverse a string or linked list.
⦁ For a string: Use two-pointer swap or Python's slicing.
⦁ For a linked list: Use three pointers (prev, curr, next) and iterate while reversing links.
15. Check if a string is a palindrome.
Use two pointers from start and end, compare characters.
Return false if mismatch, true if all characters match.
16. What are the different sorting algorithms?
⦁ Bubble Sort
⦁ Selection Sort
⦁ Insertion Sort
⦁ Merge Sort
⦁ Quick Sort
⦁ Heap Sort
⦁ Radix Sort
Each has different time and space complexities.
17. Explain quicksort vs. mergesort.
⦁ Quicksort: Divide and conquer, picks a pivot.
⦁ Average: O(n log n), Worst: O(n²), Space: O(log n)
⦁ Mergesort: Always divides array into halves, then merges.
⦁ Time: O(n log n), Space: O(n), Stable sort
18. What is a binary search tree (BST)?
A tree where left child < node < right child.
⦁ Efficient for searching, insertion, deletion: O(log n) if balanced.
⦁ Unbalanced BST can degrade to O(n)
19. Inorder, Preorder, Postorder traversals.
⦁ Inorder (LNR): Sorted order in BST
⦁ Preorder (NLR): Used to copy or serialize tree
⦁ Postorder (LRN): Used to delete tree
20. Implement LRU Cache.
Use a combination of HashMap + Doubly Linked List.
⦁ HashMap stores key-node pairs.
⦁ Linked list maintains access order.
⦁ When cache is full, remove the least recently used node.
Operations (get, put): O(1) time.
💬 Double Tap ♥️ For Part-3!
11. What is a sliding window algorithm?
A technique for solving problems involving arrays or strings by maintaining a window that slides over data. It helps reduce time complexity by avoiding nested loops.
Example: Finding the max sum of subarrays of size k.
12. Detect cycle in a linked list.
Use Floyd's Cycle Detection Algorithm (Tortoise and Hare).
⦁ Move two pointers at different speeds.
⦁ If they meet, a cycle exists.
⦁ To find the cycle start, reset one pointer to head and move both one step until they meet again.
13. Find the intersection of two arrays.
Use a HashSet to store elements of the first array, then check each element in the second array.
⦁ Time: O(n + m)
⦁ Space: O(min(n, m))
14. Reverse a string or linked list.
⦁ For a string: Use two-pointer swap or Python's slicing.
⦁ For a linked list: Use three pointers (prev, curr, next) and iterate while reversing links.
15. Check if a string is a palindrome.
Use two pointers from start and end, compare characters.
Return false if mismatch, true if all characters match.
16. What are the different sorting algorithms?
⦁ Bubble Sort
⦁ Selection Sort
⦁ Insertion Sort
⦁ Merge Sort
⦁ Quick Sort
⦁ Heap Sort
⦁ Radix Sort
Each has different time and space complexities.
17. Explain quicksort vs. mergesort.
⦁ Quicksort: Divide and conquer, picks a pivot.
⦁ Average: O(n log n), Worst: O(n²), Space: O(log n)
⦁ Mergesort: Always divides array into halves, then merges.
⦁ Time: O(n log n), Space: O(n), Stable sort
18. What is a binary search tree (BST)?
A tree where left child < node < right child.
⦁ Efficient for searching, insertion, deletion: O(log n) if balanced.
⦁ Unbalanced BST can degrade to O(n)
19. Inorder, Preorder, Postorder traversals.
⦁ Inorder (LNR): Sorted order in BST
⦁ Preorder (NLR): Used to copy or serialize tree
⦁ Postorder (LRN): Used to delete tree
20. Implement LRU Cache.
Use a combination of HashMap + Doubly Linked List.
⦁ HashMap stores key-node pairs.
⦁ Linked list maintains access order.
⦁ When cache is full, remove the least recently used node.
Operations (get, put): O(1) time.
💬 Double Tap ♥️ For Part-3!
❤6
✅ Coding Interview Questions with Answers [Part-3] 💻🚀
21. Find the longest substring without repeating characters
Use a sliding window with a set to track characters.
22. Explain backtracking with N-Queens problem
Backtracking tries placing a queen in each column, then recursively places the next queen if safe. If no safe position is found, it backtracks.
23. What is a trie? Where is it used?
A Trie is a tree-like data structure used for efficient retrieval of strings, especially for autocomplete or prefix matching.
Used in:
- Dictionary lookups
- Search engines
- IP routing
24. Explain bit manipulation tricks
- Check if number is power of 2:
- Count set bits:
- Swap without temp:
25. Kadane’s Algorithm for maximum subarray sum
26. What are heaps and how do they work?
Heap is a binary tree where parent is always smaller (min-heap) or larger (max-heap) than children. Supports O(log n) insert and delete.
Use Python’s heapq for min-heaps.
27. Find kth largest element in an array
28. How to detect cycle in a graph?
Use DFS with visited and recursion stack.
29. Topological sort of a DAG
Used to sort tasks with dependencies.
30. Implement a stack using queues
21. Find the longest substring without repeating characters
Use a sliding window with a set to track characters.
def length_of_longest_substring(s):
seen = set()
left = max_len = 0
for right in range(len(s)):
while s[right] in seen:
seen.remove(s[left])
left += 1
seen.add(s[right])
max_len = max(max_len, right - left + 1)
return max_len
22. Explain backtracking with N-Queens problem
Backtracking tries placing a queen in each column, then recursively places the next queen if safe. If no safe position is found, it backtracks.
def solve_n_queens(n):
result = []
board = [-1]×n
def is_safe(row, col):
for r in range(row):
if board[r] == col or abs(board[r] - col) == abs(r - row):
return False
return True
def backtrack(row=0):
if row == n:
result.append(board[:])
return
for col in range(n):
if is_safe(row, col):
board[row] = col
backtrack(row + 1)
board[row] = -1
backtrack()
return result
23. What is a trie? Where is it used?
A Trie is a tree-like data structure used for efficient retrieval of strings, especially for autocomplete or prefix matching.
Used in:
- Dictionary lookups
- Search engines
- IP routing
24. Explain bit manipulation tricks
- Check if number is power of 2:
n & (n - 1) == 0 - Count set bits:
bin(n).count('1') - Swap without temp:
x = x ^ y; y = x ^ y; x = x ^ y25. Kadane’s Algorithm for maximum subarray sum
def max_subarray(nums):
max_sum = current = nums[0]
for num in nums[1:]:
current = max(num, current + num)
max_sum = max(max_sum, current)
return max_sum
26. What are heaps and how do they work?
Heap is a binary tree where parent is always smaller (min-heap) or larger (max-heap) than children. Supports O(log n) insert and delete.
Use Python’s heapq for min-heaps.
27. Find kth largest element in an array
import heapq
def find_kth_largest(nums, k):
return heapq.nlargest(k, nums)[-1]
28. How to detect cycle in a graph?
Use DFS with visited and recursion stack.
def has_cycle(graph):
visited = set()
rec_stack = set()
def dfs(v):
visited.add(v)
rec_stack.add(v)
for neighbor in graph[v]:
if neighbor not in visited and dfs(neighbor):
return True
elif neighbor in rec_stack:
return True
rec_stack.remove(v)
return False
for node in graph:
if node not in visited and dfs(node):
return True
return False
29. Topological sort of a DAG
Used to sort tasks with dependencies.
def topological_sort(graph):
visited, result = set(), []
def dfs(node):
if node in visited:
return
visited.add(node)
for neighbor in graph.get(node, []):
dfs(neighbor)
result.append(node)
for node in graph:
dfs(node)
return result[::-1]
30. Implement a stack using queues
from collections import deque
class Stack:
def init(self):
self.q = deque()
def push(self, x):
self.q.append(x)
for _ in range(len(self.q) - 1):
self.q.append(self.q.popleft())
def pop(self):
return self.q.popleft()
def top(self):
return self.q[0]
def empty(self):
return not self.q
💬 Double Tap ♥️ For Part-4!❤9👍1
Sometimes reality outpaces expectations in the most unexpected ways.
While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included.
✅ No API paywalls.
✅ No usage restrictions.
✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs.
What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers.
GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments.
GitHub | HuggingFace | GitVerse
GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count.
Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support.
GitHub | Hugging Face | GitVerse
Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation.
GitHub | GitVerse | Hugging Face | Technical report
Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech.
GitHub | HuggingFace | GitVerse
Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.
While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included.
✅ No API paywalls.
✅ No usage restrictions.
✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs.
What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers.
GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments.
GitHub | HuggingFace | GitVerse
GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count.
Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support.
GitHub | Hugging Face | GitVerse
Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation.
GitHub | GitVerse | Hugging Face | Technical report
Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech.
GitHub | HuggingFace | GitVerse
Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.
❤4👍2
✅ DSA Roadmap for Beginners (2025) 🔢🧠
1. Understand What DSA Is
⦁ Data Structures organize data efficiently; Algorithms solve problems step-by-step
⦁ Why learn: Boosts coding interviews, optimizes code for tech jobs
2. Pick a Programming Language
⦁ Start with Python, C++, or Java for syntax basics
⦁ Focus on loops, arrays, functions before diving deep
3. Learn Time & Space Complexity
⦁ Big-O notation: O(1), O(n), O(n²)
⦁ Analyze efficiency to write better code
4. Master Basic Data Structures
⦁ Arrays & Strings: Indexing, manipulation
⦁ Linked Lists: Insertion, deletion, reversal
5. Explore Stacks & Queues
⦁ LIFO (Stack) for undo operations, FIFO (Queue) for tasks
⦁ Applications: Parentheses balancing, BFS
6. Dive into Trees & Graphs
⦁ Binary Trees, BSTs: Traversal (BFS/DFS)
⦁ Graphs: Adjacency lists, shortest paths (Dijkstra)
7. Learn Sorting & Searching
⦁ Algorithms: Bubble, Merge, Quick Sort; Binary Search
⦁ Understand when to use each for efficiency
8. Tackle Recursion & Backtracking
⦁ Base cases, recursive calls
⦁ Problems: Subsets, N-Queens
9. Work on Dynamic Programming
⦁ Memoization, tabulation
⦁ Classics: Fibonacci, Knapsack, LCS
10. Bonus Skills
⦁ Heaps, Tries, Greedy algorithms
⦁ Practice on LeetCode, HackerRank; build projects like pathfinders
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1. Understand What DSA Is
⦁ Data Structures organize data efficiently; Algorithms solve problems step-by-step
⦁ Why learn: Boosts coding interviews, optimizes code for tech jobs
2. Pick a Programming Language
⦁ Start with Python, C++, or Java for syntax basics
⦁ Focus on loops, arrays, functions before diving deep
3. Learn Time & Space Complexity
⦁ Big-O notation: O(1), O(n), O(n²)
⦁ Analyze efficiency to write better code
4. Master Basic Data Structures
⦁ Arrays & Strings: Indexing, manipulation
⦁ Linked Lists: Insertion, deletion, reversal
5. Explore Stacks & Queues
⦁ LIFO (Stack) for undo operations, FIFO (Queue) for tasks
⦁ Applications: Parentheses balancing, BFS
6. Dive into Trees & Graphs
⦁ Binary Trees, BSTs: Traversal (BFS/DFS)
⦁ Graphs: Adjacency lists, shortest paths (Dijkstra)
7. Learn Sorting & Searching
⦁ Algorithms: Bubble, Merge, Quick Sort; Binary Search
⦁ Understand when to use each for efficiency
8. Tackle Recursion & Backtracking
⦁ Base cases, recursive calls
⦁ Problems: Subsets, N-Queens
9. Work on Dynamic Programming
⦁ Memoization, tabulation
⦁ Classics: Fibonacci, Knapsack, LCS
10. Bonus Skills
⦁ Heaps, Tries, Greedy algorithms
⦁ Practice on LeetCode, HackerRank; build projects like pathfinders
💬 Double Tap ♥️ For More
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📈How to make $15,000 in a month in 2025?
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Easy!!! Lisa is now the hippest trader who is showing crazy results in the market!
She was able to make over $15,000 in the last month! ❗️
Right now she has started a marathon on her channel and is running it absolutely free. 💡
To participate in the marathon, you will need to :
1. Subscribe to the channel SIGNALS BY LISA TRADER 📈
2. Write in private messages : “Marathon” and start participating!
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✅ JavaScript Essentials – Interview Questions with Answers 🧠💻
1️⃣ Q: What is the difference between let, const, and var?
A:
⦁ var: Function-scoped, hoisted, can be redeclared.
⦁ let: Block-scoped, not hoisted like var, can't be redeclared in same scope.
⦁ const: Block-scoped, must be assigned at declaration, cannot be reassigned.
2️⃣ Q: What are JavaScript data types?
A:
⦁ Primitive types: string, number, boolean, null, undefined, symbol, bigint
⦁ Non-primitive: object, array, function
Type coercion: JS automatically converts between types in operations ('5' + 2 → '52')
3️⃣ Q: How does DOM Manipulation work in JS?
A:
The DOM (Document Object Model) represents the HTML structure. JS can access and change elements using:
⦁
⦁
⦁
Example:
4️⃣ Q: What is event handling in JavaScript?
A:
It allows reacting to user actions like clicks or key presses.
Example:
5️⃣ Q: What are arrow functions?
A:
A shorter syntax for functions introduced in ES6.
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1️⃣ Q: What is the difference between let, const, and var?
A:
⦁ var: Function-scoped, hoisted, can be redeclared.
⦁ let: Block-scoped, not hoisted like var, can't be redeclared in same scope.
⦁ const: Block-scoped, must be assigned at declaration, cannot be reassigned.
2️⃣ Q: What are JavaScript data types?
A:
⦁ Primitive types: string, number, boolean, null, undefined, symbol, bigint
⦁ Non-primitive: object, array, function
Type coercion: JS automatically converts between types in operations ('5' + 2 → '52')
3️⃣ Q: How does DOM Manipulation work in JS?
A:
The DOM (Document Object Model) represents the HTML structure. JS can access and change elements using:
⦁
document.getElementById()⦁
document.querySelector()⦁
element.innerHTML (sets HTML content), element.textContent (sets text only), element.style (applies CSS) Example:
document.querySelector('p').textContent = 'Updated text!';4️⃣ Q: What is event handling in JavaScript?
A:
It allows reacting to user actions like clicks or key presses.
Example:
document.getElementById("btn").addEventListener("click", () => {
alert("Button clicked!");
});5️⃣ Q: What are arrow functions?
A:
A shorter syntax for functions introduced in ES6.
const add = (a, b) => a + b;
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👨🎓System Design Topics: Cheat Sheet for Interview Preparation
☑️ Load Balancing
☑️ API Gateway
☑️ Communication Protocols
☑️ CDN (Content Delivery Network)
☑️ Database
☑️ Cache
☑️ Message Queue
☑️ Generating Unique Identifiers
☑️ Scalability
☑️ Availability
☑️ Performance
☑️ Fault Tolerance and Recovery
☑️ Security and much more
☑️ Load Balancing
☑️ API Gateway
☑️ Communication Protocols
☑️ CDN (Content Delivery Network)
☑️ Database
☑️ Cache
☑️ Message Queue
☑️ Generating Unique Identifiers
☑️ Scalability
☑️ Availability
☑️ Performance
☑️ Fault Tolerance and Recovery
☑️ Security and much more
👍5❤2👏2