📊 𝗧𝗼𝗽 𝟰 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗶𝗻 𝟮𝟬𝟮𝟲 🚀

Want to become a Data Analyst or Data Scientist? 👀
These FREE certifications can help you build job-ready skills & strengthen your resume 🔥

✨ Learn:
✔ SQL & Data Analytics
✔ Power BI Dashboards 📊
✔ Data Cleaning & Visualization
✔ AI & Machine Learning Basics 🤖

💯 FREE + Beginner Friendly

𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-

https://pdlink.in/4dsdTCV

🎓 Perfect for Students, Freshers & Career Switchers

Top 100 Data Science Interview Questions ✅

Data Science Basics
1. What is data science and how is it different from data analytics?
2. What are the key steps in a data science lifecycle?
3. What types of problems does data science solve?
4. What skills does a data scientist need in real projects?
5. What is the difference between structured and unstructured data?
6. What is exploratory data analysis and why do you do it first?
7. What are common data sources in real companies?
8. What is feature engineering?
9. What is the difference between supervised and unsupervised learning?
10. What is bias in data and how does it affect models?

Statistics and Probability
11. What is the difference between mean, median, and mode?
12. What is standard deviation and variance?
13. What is probability distribution?
14. What is normal distribution and where is it used?
15. What is skewness and kurtosis?
16. What is correlation vs causation?
17. What is hypothesis testing?
18. What are Type I and Type II errors?
19. What is p-value?
20. What is confidence interval?

Data Cleaning and Preprocessing
21. How do you handle missing values?
22. How do you treat outliers?
23. What is data normalization and standardization?
24. When do you use Min-Max scaling vs Z-score?
25. How do you handle imbalanced datasets?
26. What is one-hot encoding?
27. What is label encoding?
28. How do you detect data leakage?
29. What is duplicate data and how do you handle it?
30. How do you validate data quality?

Python for Data Science
31. Why is Python popular in data science?
32. Difference between list, tuple, set, and dictionary?
33. What is NumPy and why is it fast?
34. What is Pandas and where do you use it?
35. Difference between loc and iloc?
36. What are vectorized operations?
37. What is lambda function?
38. What is list comprehension?
39. How do you handle large datasets in Python?
40. What are common Python libraries used in data science?

Data Visualization
41. Why is data visualization important?
42. Difference between bar chart and histogram?
43. When do you use box plots?
44. What does a scatter plot show?
45. What are common mistakes in data visualization?
46. Difference between Seaborn and Matplotlib?
47. What is a heatmap used for?
48. How do you visualize distributions?
49. What is dashboarding?
50. How do you choose the right chart?

Machine Learning Basics
51. What is machine learning?
52. Difference between regression and classification?
53. What is overfitting and underfitting?
54. What is train-test split?
55. What is cross-validation?
56. What is bias-variance tradeoff?
57. What is feature selection?
58. What is model evaluation?
59. What is baseline model?
60. How do you choose a model?

Supervised Learning
61. How does linear regression work?
62. Assumptions of linear regression?
63. What is logistic regression?
64. What is decision tree?
65. What is random forest?
66. What is KNN and when do you use it?
67. What is SVM?
68. How does Naive Bayes work?
69. What are ensemble methods?
70. How do you tune hyperparameters?

Unsupervised Learning
71. What is clustering?
72. Difference between K-means and hierarchical clustering?
73. How do you choose value of K?
74. What is PCA?
75. Why is dimensionality reduction needed?
76. What is anomaly detection?
77. What is association rule mining?
78. What is DBSCAN?
79. What is cosine similarity?
80. Where is unsupervised learning used?

Model Evaluation Metrics
81. What is accuracy and when is it misleading?
82. What is precision and recall?
83. What is F1 score?
84. What is ROC curve?
85. What is AUC?
86. Difference between confusion matrix metrics?
87. What is log loss?
88. What is RMSE?
89. What metric do you use for imbalanced data?
90. How do business metrics link to ML metrics?

Deployment and Real-World Practice
91. What is model deployment?
92. What is batch vs real-time prediction?
93. What is model drift?
94. How do you monitor model performance?
95. What is feature store?
96. What is experiment tracking?
97. How do you explain model predictions?
98. What is data versioning?
99. How do you handle failed models?
100. How do you communicate results to non-technical stakeholders?

Double Tap ♥️ For Detailed Answers

Want to start your career in 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲😍?

Learn from IIIT Bangalore & upGrad

💫 Beginner Friendly
💫 Industry Recognized Certificate
💫High Demand Career Skills

𝗕𝗼𝗼𝗸 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗻𝘀𝗲𝗹𝗹𝗶𝗻𝗴👇Now & explore your career roadmap

https://pdlink.in/4twH9xg

🎓Top roles you can target:
* Data Analyst , AI Engineer ,Machine Learning Engineer & Data Scientist

If you interview at Google, you’ll be grilled on graph problems and real-world use cases, like Google Maps.

If you interview at Amazon, expect stack/queue questions straight out of their backend systems, think processing millions of print jobs and browser back buttons.

If you interview at Atlassian or Oracle, don’t be surprised if DSA problems are tied to actual product scenarios, like task tracking, caching, and visitor analytics.

Every DSA round cares about:
→ Can you map the right data structure to a real problem?
→ Do you understand WHY Google uses graphs, why Amazon cares about queues, why Microsoft loves sets and tries?

After coaching students and professionals for the last 8+ years and helping them get placed across the board at Google, Amazon, Atlassian, Juspay, Swiggy, and many more companies.

I can tell you with 100% certainty that without mastering these 8 essential data structures and their problems, you won’t be able to clear coding interviews.

Here are the 8 Data Structures You Must Know:

→ 1. Arrays:
Foundation for all DSA. Fast access, easy to use, but slow for inserts/deletes in the middle. Used everywhere, think memory management, and basic storage.

– Learn which pattern to use for which problem
– Map interview keywords to real solutions
– Practice 5–6 Leetcode must-solves per pattern
– Track your progress and build a real interview toolkit }

→ 2. Linked Lists:
Great for inserts/deletes, bad for random access. Useful in implementing queues, stacks, and real-world apps like undo operations.

→ 3. Hash Maps:
Fast key-value lookups, like dictionaries. Power most caching systems and help in solving “find duplicates” or “group by” problems.

→ 4. Stacks & Queues:
Think of your browser history (stack), print jobs (queue), or undo-redo (stack). Interviewers love these for testing order and flow.

→ 5. Trees (including Binary Search Trees):
Used for hierarchical data, searching, sorting, and in system internals. Master BSTs for fast lookups and ordered storage.

→ 6. Tries (Prefix Trees):
Special tree for autocomplete, spell checkers, and prefix matching. Autocomplete in search bars is built on tries.

→ 7. Heaps:
Perfect for getting the min/max element fast. Used in priority queues, scheduling jobs, and heapsort.

→ 8. Graphs:
Most complex but super important. Used in Google Maps, social networks, recommendations, network routing. You need to understand adjacency lists, DFS, BFS, and shortest path algorithms.

Bottom line:
Don’t just practice random Leetcode problems. Master these data structures, and also understand real-world use cases so you don't fall into the trap of tricky questions.

🗄️ 𝗧𝗼𝗽 𝟱 𝗙𝗥𝗘𝗘 𝗦𝗤𝗟 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🚀

SQL is one of the most important skills for Data Analyst & Tech jobs in 2026 🔥
These FREE certification courses can help you learn SQL from scratch & boost your resume 💼

✨ Learn:
✔ SQL Queries & Databases 🗄️
✔ Data Analysis Basics 📊
✔ Real-world Projects
✔ Beginner to Advanced Concepts

𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 
 
https://pdlink.in/4dCHiKI
 
💯 Beginner Friendly + FREE Certificates 🎓
💼 Perfect for Students, Freshers & Career Switchers

✅ 50 Must-Know Web Development Concepts for Interviews 🌐💼

📍 HTML Basics
1. What is HTML?
2. Semantic tags (article, section, nav)
3. Forms and input types
4. HTML5 features
5. SEO-friendly structure

📍 CSS Fundamentals
6. CSS selectors & specificity
7. Box model
8. Flexbox
9. Grid layout
10. Media queries for responsive design

📍 JavaScript Essentials
11. let vs const vs var
12. Data types & type coercion
13. DOM Manipulation
14. Event handling
15. Arrow functions

📍 Advanced JavaScript
16. Closures
17. Hoisting
18. Callbacks vs Promises
19. async/await
20. ES6+ features

📍 Frontend Frameworks
21. React: props, state, hooks
22. Vue: directives, computed properties
23. Angular: components, services
24. Component lifecycle
25. Conditional rendering

📍 Backend Basics
26. Node.js fundamentals
27. Express.js routing
28. Middleware functions
29. REST API creation
30. Error handling

📍 Databases
31. SQL vs NoSQL
32. MongoDB basics
33. CRUD operations
34. Indexes & performance
35. Data relationships

📍 Authentication & Security
36. Cookies vs LocalStorage
37. JWT (JSON Web Token)
38. HTTPS & SSL
39. CORS
40. XSS & CSRF protection

📍 APIs & Web Services
41. REST vs GraphQL
42. Fetch API
43. Axios basics
44. Status codes
45. JSON handling

📍 DevOps & Tools
46. Git basics & GitHub
47. CI/CD pipelines
48. Docker (basics)
49. Deployment (Netlify, Vercel, Heroku)
50. Environment variables (.env)

Double Tap ♥️ For More

𝗔𝗜 𝗮𝗻𝗱 𝗠𝗟 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗯𝘆 𝗖𝗖𝗘, 𝗜𝗜𝗧 𝗠𝗮𝗻𝗱𝗶😍

Freshers get 15 LPA Average Salary with AI & ML Skills!

💻 100% Online
⏳ 6 Months Duration
👨‍🏫 Learn from IIT Professors
📌 Open for Students ,Freshers & Working Professionals

💼 Placement Assistance with 5000+ Companies
📈 High Demand Skills for Future Tech Jobs

Top companies are hiring for candidates with 𝗔𝗜, 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 skills in 2026

🔥Deadline :- 17th May

  𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :- 

https://pdlink.in/4nmI024
.
Get Placement Assistance With 5000+ Companies

🚀 Coding Interview Questions with Answers — Part 1

🧠 1. What is an array and how is it stored in memory?
An array is a data structure used to store multiple elements of the same data type in a contiguous block of memory.

Example: arr = [10, 20, 30, 40]

🔹 Key Features
- Fixed size (in most languages)
- Fast access using index
- Stores elements sequentially

🔹 Memory Representation
If an integer takes 4 bytes:

Index | Value | Memory Address
0 | 10 | 1000
1 | 20 | 1004
2 | 30 | 1008
3 | 40 | 1012

Each element is stored next to the previous one.

🔹 Time Complexity
Operation | Complexity
Access | O(1)
Search | O(n)
Insert/Delete (middle) | O(n)

🔹 Interview Tip
Arrays are preferred when:
- Fast indexing is needed
- Memory efficiency matters
- Data size is mostly fixed

🚀 2. What is the difference between an array and a linked list?

Feature | Array | Linked List
Memory | Contiguous | Non-contiguous
Access Speed | O(1) | O(n)
Insert/Delete | Slow | Fast
Size | Fixed | Dynamic
Extra Memory | Less | More (pointer storage)

🔹 Array Example: arr = [1, 2, 3]
🔹 Linked List Example: 1 → 2 → 3 → NULL
Each node stores: Data + Pointer to next node

🔹 When to Use
✅ Use Arrays: Random access needed, Cache-friendly operations
✅ Use Linked Lists: Frequent insertions/deletions, Dynamic memory allocation

🔹 Interview Tip
Linked lists solve resizing problems of arrays but sacrifice fast access speed.

🚀 3. Explain time complexity using Big-O notation
Big-O notation measures how an algorithm grows as input size increases.

🔹 Common Complexities
Complexity | Meaning
O(1) | Constant
O(log n) | Logarithmic
O(n) | Linear
O(n log n) | Efficient sorting
O(n²) | Nested loops
O(2ⁿ) | Exponential

🔹 Example:
for i in range(n):
print(i)
This runs n times. ➡️ Complexity = O(n)

🔹 Nested Loop Example:
for i in range(n):
for j in range(n):
print(i, j)

➡️ Complexity = O(n²)

🔹 Why It Matters
Interviewers use Big-O to evaluate: Scalability, Efficiency, Optimization skills

🔹 Interview Tip
Always discuss: Time complexity, Space complexity, Trade-offs

🚀 4. How do you implement a stack using an array?
A stack follows the LIFO principle: Last In, First Out

Operations: Push, Pop, Peek

🔹 Python Implementation:
class Stack:
def init(self):
self.stack = []

def push(self, value):
self.stack.append(value)

def pop(self):
if self.is_empty():
return "Stack Underflow"
return self.stack.pop()

def peek(self):
if self.is_empty():
return None
return self.stack[-1]

def is_empty(self):
return len(self.stack) == 0

🔹 Example:
s = Stack()
s.push(10)
s.push(20)
print(s.pop()) # 20

🔹 Complexity
Operation | Complexity
Push | O(1)
Pop | O(1)
Peek | O(1)

🔹 Real-World Uses
Undo feature, Browser history, Function call stack, Expression evaluation

🚀 5. How do you implement a queue using an array or linked list?
A queue follows the FIFO principle: First In, First Out

Operations: Enqueue, Dequeue

🔹 Queue Using Array:
class Queue:
def init(self):
self.queue = []

def enqueue(self, value):
self.queue.append(value)

def dequeue(self):
if not self.queue:
return "Empty Queue"
return self.queue.pop(0)

⚠️ Problem: pop(0) takes O(n) because elements shift.

🔹 Queue Using Linked List:
from collections import deque

q = deque()
q.append(10)
q.append(20)
print(q.popleft())

🔹 Complexity
Operation | Complexity
Enqueue | O(1)
Dequeue | O(1)

🔹 Real-World Uses
CPU scheduling, Task queues, Messaging systems, BFS traversal

🚀 6. How does a hash table work?
A hash table stores key-value pairs using a hash function.

🔹 Example:
student = {
"name": "John",
"age": 22
}

🔹 Working: 
1. Key goes into hash function 
2. Hash function generates index 
3. Value stored at that index 

🔹 Example Flow 
hash("age") → index 5 
Store: table[5] = 22

🚀 7. How do you handle collisions in a hash table?
A collision happens when two keys generate the same index.

🔹 Example:
hash("abc") = 5
hash("xyz") = 5
Both want index 5.

🔹 Collision Handling Techniques

1️⃣ Chaining
Store multiple values in a linked list.
Index 5: abc → xyz

2️⃣ Open Addressing
Find another empty slot.
Methods: Linear probing, Quadratic probing, Double hashing

🔹 Linear Probing Example
Index occupied? Move to next slot.

🔹 Interview Tip
Most interviewers expect Chaining and Linear probing to be explained clearly.

🚀 8. What is a binary tree and a binary search tree (BST)?

🔹 Binary Tree
A tree where each node has at most 2 children.
10
/ \
5 20

🔹 Binary Search Tree (BST)
Special binary tree where: Left < Root < Right
10
/ \
5 20

🔹 BST Advantages
Fast searching, Sorted traversal, Efficient insert/delete

🔹 Complexity
Operation | Average
Search | O(log n)
Insert | O(log n)
Delete | O(log n)

Worst case: O(n)

🔹 Interview Tip
BST questions are among the most asked DSA interview topics.

🚀 9. How do you traverse a tree (inorder, preorder, postorder)?
Tree traversal means visiting all nodes.

🔹 Inorder Traversal
Left → Root → Right
def inorder(root):
if root:
inorder(root.left)
print(root.val)
inorder(root.right)

➡️ Used in BST to get sorted order.

🔹 Preorder Traversal
Root → Left → Right
Used for: Tree copying, Serialization

🔹 Postorder Traversal
Left → Right → Root
Used for: Deletion, Bottom-up processing

🔹 Complexity
All traversals: Time O(n), Space O(h)

🚀 10. What is recursion and when is it useful?
Recursion is when a function calls itself.

🔹 Example:
def factorial(n):
if n == 0:
return 1
return n * factorial(n - 1)

🔹 Recursive Flow
factorial(4) = 4 × factorial(3) = 4 × 3 × factorial(2)...

🔹 Key Components
1. Base case
2. Recursive case

🔹 Where Recursion is Useful
Trees, Graphs, DFS, Backtracking, Divide & Conquer

🔹 Interview Tip
Always explain: Base condition, Stack usage, Time complexity

🔹 Common Mistake
Missing base case causes: Stack Overflow Error

🔥 Double Tap ❤️ For Part-2

🚀 𝗕𝗲𝗰𝗼𝗺𝗲 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗘𝘅𝗽𝗲𝗿𝘁𝘀! 📊

Learn the most in-demand skills of 2026

💫Data Science ,AI,ML &Python & SQL
✅
💼 Get Placement Assistance
🎓 Beginner Friendly Program
💻 Learn Online from Anywhere
📈 Build Skills Companies Actually Hire For

🔥 AI is changing every industry — this is the best time to upskill and secure high-paying tech jobs.

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰 👇:-

 https://pdlink.in/4fdWxJB

⚡ Limited Seats Available – Apply Fast!

🚀 Coding Interview Questions with Answers — Part 2

🌱 Arrays, Strings & Two-Pointers

🚀 11. How do you remove duplicates from a sorted array?
Since the array is already sorted, duplicates appear together.

🔹 Best Approach
Use the Two-Pointer Technique.
- One pointer tracks unique elements
- Another scans the array

🔹 Python Solution
def remove_duplicates(arr):
    if not arr:
        return 0

    i = 0

    for j in range(1, len(arr)):
        if arr[j]!= arr[i]:
            i += 1
            arr[i] = arr[j]

    return i + 1

arr = [1,1,2,2,3,4,4]
length = remove_duplicates(arr)
print(arr[:length])

🔹 Output
[1][2][3][4]

🔹 Complexity
Time → O(n)
Space → O(1)

🔹 Interview Tip
This is one of the most common two-pointer interview problems.

🚀 12. How do you solve “Two Sum” efficiently?
Problem: Find two numbers whose sum equals target.

🔹 Brute Force
for i in range(len(arr)):
    for j in range(i+1, len(arr)):
        if arr[i] + arr[j] == target:
            return [i, j]
Complexity → O(n²)

🔹 Optimized HashMap Solution
def two_sum(arr, target):
    hashmap = {}

    for i, num in enumerate(arr):
        complement = target - num

        if complement in hashmap:
            return [hashmap[complement], i]

        hashmap[num] = i

print(two_sum([2,7,11,15], 9))

🔹 Output
[0][1]

🔹 Complexity
Time → O(n)
Space → O(n)

🔹 Interview Tip
Hashing is the key optimization here.

🚀 13. How do you reverse a string or array?

🔹 Reverse String
s = "hello"
print(s[::-1])

Output → olleh

🔹 Two-Pointer Method
def reverse_array(arr):
    left = 0
    right = len(arr) - 1

    while left < right:
        arr[left], arr[right] = arr[right], arr[left]

        left += 1
        right -= 1

    return arr

print(reverse_array([1,2,3,4]))

🔹 Complexity
Time → O(n)
Space → O(1)

🔹 Interview Tip
Interviewers often prefer the two-pointer approach.

🚀 14. How do you find the maximum subarray sum (Kadane’s Algorithm)?
Problem: Find contiguous subarray with maximum sum.

🔹 Kadane’s Algorithm
def max_subarray(arr):
    current_sum = arr[0]
    max_sum = arr[0]

    for num in arr[1:]:
        current_sum = max(num, current_sum + num)
        max_sum = max(max_sum, current_sum)

    return max_sum

print(max_subarray([-2,1,-3,4,-1,2,1,-5,4]))

🔹 Output
6

Subarray:
[4, -1, 2, 1]

🔹 Complexity
Time → O(n)
Space → O(1)

🔹 Interview Tip
Kadane’s Algorithm is a very high-frequency interview question.

🚀 15. How do you rotate an array?
Rotate array by k positions.

🔹 Python Solution
def rotate(arr, k):
    k = k % len(arr)
    return arr[-k:] + arr[:-k]

print(rotate([1,2,3,4,5], 2))

🔹 Output
[4][5][1][2][3]

🔹 Complexity
Time → O(n)
Space → O(n)

🔹 In-Place Optimization
Can be solved in O(1) extra space using reversal algorithm.

🚀 16. How do you find the first missing positive number?
Problem: Find smallest missing positive integer.
Example: [3,4,-1,1]
Output: 2

🔹 Optimized Solution Idea
Place each number at its correct index.
1 → index 0
2 → index 1

🔹 Python Solution
def first_missing_positive(nums):
    n = len(nums)

    for i in range(n):
        while 1 <= nums[i] <= n and nums[nums[i]-1]!= nums[i]:
            nums[nums[i]-1], nums[i] = nums[i], nums[nums[i]-1]

    for i in range(n):
        if nums[i]!= i + 1:
            return i + 1

    return n + 1

print(first_missing_positive([3,4,-1,1]))

🔹 Complexity
Time → O(n)
Space → O(1)

🔹 Interview Tip
This is considered a hard interview problem.

🚀 17. How do you implement sliding-window problems?
Sliding window helps optimize subarray/substring problems.

🔹 Example Problem
Maximum sum of subarray of size k.
def max_sum(arr, k):
    window_sum = sum(arr[:k])
    max_sum = window_sum

    for i in range(k, len(arr)):
        window_sum += arr[i] - arr[i-k]
        max_sum = max(max_sum, window_sum)

    return max_sum

print(max_sum([1,2,3,4,5], 3))

🔹 Output
12

🔹 Complexity
Time → O(n)
Space → O(1)

🔹 Interview Tip
Sliding window is heavily used in:
- Substrings
- Subarrays
- Streaming data

🚀 18. How do you merge two sorted arrays?

🔹 Python Solution
def merge(arr1, arr2):
i = j = 0
result = []

while i < len(arr1) and j < len(arr2):
if arr1[i] < arr2[j]:
result.append(arr1[i])
i += 1
else:
result.append(arr2[j])
j += 1

result.extend(arr1[i:])
result.extend(arr2[j:])

return result

print(merge([1,3,5], [2,4,6]))

🔹 Output
[1][2][3][4][5][6]

🔹 Complexity
Time → O(n + m)
Space → O(n + m)

🔹 Interview Tip
This is the foundation of Merge Sort.

🚀 19. How do you find the longest substring without repeating characters?

🔹 Sliding Window + HashSet
def longest_substring(s):
char_set = set()
left = 0
max_len = 0

for right in range(len(s)):
while s[right] in char_set:
char_set.remove(s[left])
left += 1

char_set.add(s[right])
max_len = max(max_len, right - left + 1)

return max_len

print(longest_substring("abcabcbb"))

🔹 Output
3

Substring:
"abc"

🔹 Complexity
Time → O(n)
Space → O(n)

🔹 Interview Tip
Very frequently asked in FAANG interviews.

🚀 20. How do you implement a circular buffer?
A circular buffer reuses empty spaces efficiently.

🔹 Visualization
[1, 2, 3, _, _]

After removal:
[_, 2, 3, _, _]

Next insert goes to empty slot.

🔹 Python Implementation
class CircularBuffer:
def init(self, size):
self.buffer = [None] * size
self.size = size
self.head = 0
self.tail = 0
self.count = 0

def enqueue(self, value):
if self.count == self.size:
return "Buffer Full"

self.buffer[self.tail] = value
self.tail = (self.tail + 1) % self.size
self.count += 1

def dequeue(self):
if self.count == 0:
return "Buffer Empty"

value = self.buffer[self.head]
self.head = (self.head + 1) % self.size
self.count -= 1

return value

🔹 Uses
- Streaming systems
- Audio processing
- Producer-consumer problems
- Network buffers

🔹 Complexity
Enqueue → O(1)
Dequeue → O(1)

🔥 Double Tap ❤️ For Part-3

𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 by iHUB IIT Roorkee 😍

Freshers get paid 12 LPA average salary for the role of Associate Product Manager! 💼

𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:
✅ Learn from IIT Roorkee Professors
✅Placement support from 5,000+ companies
✅ Professional Certification in Product Management with Applied AI
✅ 100% Online Program
✅ Open to Everyone

📅𝗗𝗲𝗮𝗱𝗹𝗶𝗻𝗲: 17th May 2026

  𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :- 

https://pdlink.in/4ddJZ5C

⚡ Limited Seats Available — Apply Soon!