📊 SQL Interview Queries – Intermediate Level
━━━━━━━━━━━━━━
❓ Query 01: Find employees earning more than the average salary
SELECT *
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);
❓ Query 02: Find department-wise employee count
SELECT department, COUNT(*) AS emp_count
FROM employees
GROUP BY department;
❓ Query 03: Find departments with average salary greater than 60,000
SELECT department
FROM employees
GROUP BY department
HAVING AVG(salary) > 60000;
❓ Query 04: Fetch employees who do not belong to any department
SELECT e.*
FROM employees e
LEFT JOIN departments d
ON e.department_id = d.department_id
WHERE d.department_id IS NULL;
❓ Query 05: Find second highest salary
SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
❓ Query 06: Get highest salary in each department
SELECT department, MAX(salary)
FROM employees
GROUP BY department;
❓ Query 07: Fetch employees hired in the last 6 months
SELECT *
FROM employees
WHERE hire_date >= DATE_SUB(CURDATE(), INTERVAL 6 MONTH);
❓ Query 08: Find duplicate email IDs
SELECT email, COUNT(*)
FROM employees
GROUP BY email
HAVING COUNT(*) > 1;
❓ Query 09: Rank employees by salary within each department
SELECT *,
RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rank
FROM employees;
❓ Query 10: Fetch top 2 highest paid employees from each department
SELECT *
FROM (
SELECT *,
DENSE_RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rnk
FROM employees
) t
WHERE rnk <= 2;
🔥 Show some love with a reaction ❤️
━━━━━━━━━━━━━━
❓ Query 01: Find employees earning more than the average salary
SELECT *
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);
❓ Query 02: Find department-wise employee count
SELECT department, COUNT(*) AS emp_count
FROM employees
GROUP BY department;
❓ Query 03: Find departments with average salary greater than 60,000
SELECT department
FROM employees
GROUP BY department
HAVING AVG(salary) > 60000;
❓ Query 04: Fetch employees who do not belong to any department
SELECT e.*
FROM employees e
LEFT JOIN departments d
ON e.department_id = d.department_id
WHERE d.department_id IS NULL;
❓ Query 05: Find second highest salary
SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
❓ Query 06: Get highest salary in each department
SELECT department, MAX(salary)
FROM employees
GROUP BY department;
❓ Query 07: Fetch employees hired in the last 6 months
SELECT *
FROM employees
WHERE hire_date >= DATE_SUB(CURDATE(), INTERVAL 6 MONTH);
❓ Query 08: Find duplicate email IDs
SELECT email, COUNT(*)
FROM employees
GROUP BY email
HAVING COUNT(*) > 1;
❓ Query 09: Rank employees by salary within each department
SELECT *,
RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rank
FROM employees;
❓ Query 10: Fetch top 2 highest paid employees from each department
SELECT *
FROM (
SELECT *,
DENSE_RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rnk
FROM employees
) t
WHERE rnk <= 2;
🔥 Show some love with a reaction ❤️
❤11
📊 Excel Interview Question
❓ What is the difference between VLOOKUP and XLOOKUP?
🧠 Key Differences Explained Simply:
🔹 Lookup Direction
• VLOOKUP → Can only search left to right
• XLOOKUP → Can search both left → right and right → left
🔹 Column Dependency
• VLOOKUP → Depends on column number (breaks if columns move)
• XLOOKUP → No column number required (more reliable)
🔹 Error Handling
• VLOOKUP → Returns #N/A if value not found
• XLOOKUP → Built-in option to handle missing values gracefully
🔹 Flexibility & Performance
• VLOOKUP → Limited and outdated
• XLOOKUP → Modern, flexible, and recommended by Microsoft
🚀 Final Verdict:
If Excel version allows, always prefer XLOOKUP for cleaner, safer, and future-proof formulas.
🔥 React ❤️ for more interview questions
❓ What is the difference between VLOOKUP and XLOOKUP?
🧠 Key Differences Explained Simply:
🔹 Lookup Direction
• VLOOKUP → Can only search left to right
• XLOOKUP → Can search both left → right and right → left
🔹 Column Dependency
• VLOOKUP → Depends on column number (breaks if columns move)
• XLOOKUP → No column number required (more reliable)
🔹 Error Handling
• VLOOKUP → Returns #N/A if value not found
• XLOOKUP → Built-in option to handle missing values gracefully
🔹 Flexibility & Performance
• VLOOKUP → Limited and outdated
• XLOOKUP → Modern, flexible, and recommended by Microsoft
🚀 Final Verdict:
If Excel version allows, always prefer XLOOKUP for cleaner, safer, and future-proof formulas.
🔥 React ❤️ for more interview questions
❤2
𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 - 𝐆𝐞𝐭 𝐏𝐥𝐚𝐜𝐞𝐝 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂'𝐬 😍
Learn Coding From Scratch - Lectures Taught By IIT Alumni
60+ Hiring Drives Every Month
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:-
🌟 Trusted by 7500+ Students
🤝 500+ Hiring Partners
💼 Avg. Rs. 7.4 LPA
🚀 41 LPA Highest Package
Eligibility: BTech / BCA / BSc / MCA / MSc
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰👇 :-
https://pdlink.in/4hO7rWY
Hurry, limited seats available!
Learn Coding From Scratch - Lectures Taught By IIT Alumni
60+ Hiring Drives Every Month
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:-
🌟 Trusted by 7500+ Students
🤝 500+ Hiring Partners
💼 Avg. Rs. 7.4 LPA
🚀 41 LPA Highest Package
Eligibility: BTech / BCA / BSc / MCA / MSc
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰👇 :-
https://pdlink.in/4hO7rWY
Hurry, limited seats available!
✅ Top 50 Coding Interview Questions You Must Prepare 💻🧠
1. What is the difference between compiled and interpreted languages?
2. What is time complexity? Why does it matter in interviews?
3. What is space complexity?
4. Explain Big O notation with examples.
5. Difference between array and linked list.
6. What is a stack? Real use cases.
7. What is a queue? Types of queues.
8. Difference between stack and queue.
9. What is recursion? When should you avoid it?
10. Difference between recursion and iteration.
11. What is a hash table? How does hashing work?
12. What are collisions in hashing? How do you handle them?
13. Difference between HashMap and HashSet.
14. What is a binary tree?
15. What is a binary search tree?
16. Difference between BFS and DFS.
17. What is a balanced tree?
18. What is heap data structure?
19. Difference between min heap and max heap.
20. What is a graph? Directed vs undirected.
21. What is adjacency matrix vs adjacency list?
22. What is sorting? Name common sorting algorithms.
23. Difference between quick sort and merge sort.
24. Which sorting algorithm is fastest and why?
25. What is searching? Linear vs binary search.
26. Why binary search needs sorted data?
27. What is dynamic programming?
28. Difference between greedy and dynamic programming.
29. What is memoization?
30. What is backtracking?
31. What is a pointer?
32. Difference between pointer and reference.
33. What is memory leak?
34. What is segmentation fault?
35. Difference between process and thread.
36. What is multithreading?
37. What is synchronization?
38. What is deadlock?
39. Conditions for deadlock.
40. Difference between shallow copy and deep copy.
41. What is exception handling?
42. Checked vs unchecked exceptions.
43. What is mutable vs immutable object?
44. What is garbage collection?
45. What is REST API?
46. What is JSON?
47. Difference between HTTP and HTTPS.
48. What is version control? Why Git matters?
49. Explain a coding problem you optimized recently.
50. How do you approach a new coding problem in interviews?
💬 Tap ❤️ for detailed answers
1. What is the difference between compiled and interpreted languages?
2. What is time complexity? Why does it matter in interviews?
3. What is space complexity?
4. Explain Big O notation with examples.
5. Difference between array and linked list.
6. What is a stack? Real use cases.
7. What is a queue? Types of queues.
8. Difference between stack and queue.
9. What is recursion? When should you avoid it?
10. Difference between recursion and iteration.
11. What is a hash table? How does hashing work?
12. What are collisions in hashing? How do you handle them?
13. Difference between HashMap and HashSet.
14. What is a binary tree?
15. What is a binary search tree?
16. Difference between BFS and DFS.
17. What is a balanced tree?
18. What is heap data structure?
19. Difference between min heap and max heap.
20. What is a graph? Directed vs undirected.
21. What is adjacency matrix vs adjacency list?
22. What is sorting? Name common sorting algorithms.
23. Difference between quick sort and merge sort.
24. Which sorting algorithm is fastest and why?
25. What is searching? Linear vs binary search.
26. Why binary search needs sorted data?
27. What is dynamic programming?
28. Difference between greedy and dynamic programming.
29. What is memoization?
30. What is backtracking?
31. What is a pointer?
32. Difference between pointer and reference.
33. What is memory leak?
34. What is segmentation fault?
35. Difference between process and thread.
36. What is multithreading?
37. What is synchronization?
38. What is deadlock?
39. Conditions for deadlock.
40. Difference between shallow copy and deep copy.
41. What is exception handling?
42. Checked vs unchecked exceptions.
43. What is mutable vs immutable object?
44. What is garbage collection?
45. What is REST API?
46. What is JSON?
47. Difference between HTTP and HTTPS.
48. What is version control? Why Git matters?
49. Explain a coding problem you optimized recently.
50. How do you approach a new coding problem in interviews?
💬 Tap ❤️ for detailed answers
❤8
𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗜𝗻 𝗧𝗼𝗽 𝗠𝗡𝗖𝘀😍
Learn Data Analytics, Data Science & AI From Top Data Experts
𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗲𝘀:-
- 12.65 Lakhs Highest Salary
- 500+ Partner Companies
- 100% Job Assistance
- 5.7 LPA Average Salary
𝗕𝗼𝗼𝗸 𝗮 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼👇:-
𝗢𝗻𝗹𝗶𝗻𝗲:- https://pdlink.in/4fdWxJB
🔹 Hyderabad :- https://pdlink.in/4kFhjn3
🔹 Pune:- https://pdlink.in/45p4GrC
🔹 Noida :- https://linkpd.in/DaNoida
( Hurry Up 🏃♂️Limited Slots )
Learn Data Analytics, Data Science & AI From Top Data Experts
𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗲𝘀:-
- 12.65 Lakhs Highest Salary
- 500+ Partner Companies
- 100% Job Assistance
- 5.7 LPA Average Salary
𝗕𝗼𝗼𝗸 𝗮 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼👇:-
𝗢𝗻𝗹𝗶𝗻𝗲:- https://pdlink.in/4fdWxJB
🔹 Hyderabad :- https://pdlink.in/4kFhjn3
🔹 Pune:- https://pdlink.in/45p4GrC
🔹 Noida :- https://linkpd.in/DaNoida
( Hurry Up 🏃♂️Limited Slots )
Python CheatSheet 📚 ✅
1. Basic Syntax
- Print Statement:
- Comments:
2. Data Types
- Integer:
- Float:
- String:
- List:
- Tuple:
- Dictionary:
3. Control Structures
- If Statement:
- For Loop:
- While Loop:
4. Functions
- Define Function:
- Lambda Function:
5. Exception Handling
- Try-Except Block:
6. File I/O
- Read File:
- Write File:
7. List Comprehensions
- Basic Example:
- Conditional Comprehension:
8. Modules and Packages
- Import Module:
- Import Specific Function:
9. Common Libraries
- NumPy:
- Pandas:
- Matplotlib:
10. Object-Oriented Programming
- Define Class:
11. Virtual Environments
- Create Environment:
- Activate Environment:
- Windows:
- macOS/Linux:
12. Common Commands
- Run Script:
- Install Package:
- List Installed Packages:
This Python checklist serves as a quick reference for essential syntax, functions, and best practices to enhance your coding efficiency!
Checklist for Data Analyst: https://dataanalytics.beehiiv.com/p/data
Here you can find essential Python Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like for more resources like this 👍 ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
1. Basic Syntax
- Print Statement:
print("Hello, World!")- Comments:
# This is a comment2. Data Types
- Integer:
x = 10- Float:
y = 10.5- String:
name = "Alice"- List:
fruits = ["apple", "banana", "cherry"]- Tuple:
coordinates = (10, 20)- Dictionary:
person = {"name": "Alice", "age": 25}3. Control Structures
- If Statement:
if x > 10:
print("x is greater than 10")
- For Loop:
for fruit in fruits:
print(fruit)
- While Loop:
while x < 5:
x += 1
4. Functions
- Define Function:
def greet(name):
return f"Hello, {name}!"
- Lambda Function:
add = lambda a, b: a + b5. Exception Handling
- Try-Except Block:
try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero.")
6. File I/O
- Read File:
with open('file.txt', 'r') as file:
content = file.read()
- Write File:
with open('file.txt', 'w') as file:
file.write("Hello, World!")
7. List Comprehensions
- Basic Example:
squared = [x**2 for x in range(10)]- Conditional Comprehension:
even_squares = [x**2 for x in range(10) if x % 2 == 0]8. Modules and Packages
- Import Module:
import math- Import Specific Function:
from math import sqrt9. Common Libraries
- NumPy:
import numpy as np- Pandas:
import pandas as pd- Matplotlib:
import matplotlib.pyplot as plt10. Object-Oriented Programming
- Define Class:
class Dog:
def __init__(self, name):
self.name = name
def bark(self):
return "Woof!"
11. Virtual Environments
- Create Environment:
python -m venv myenv- Activate Environment:
- Windows:
myenv\Scripts\activate- macOS/Linux:
source myenv/bin/activate12. Common Commands
- Run Script:
python noscript.py- Install Package:
pip install package_name- List Installed Packages:
pip listThis Python checklist serves as a quick reference for essential syntax, functions, and best practices to enhance your coding efficiency!
Checklist for Data Analyst: https://dataanalytics.beehiiv.com/p/data
Here you can find essential Python Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like for more resources like this 👍 ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤2
𝗧𝗵𝗲 𝟯 𝗦𝗸𝗶𝗹𝗹𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗠𝗮𝗸𝗲 𝗬𝗼𝘂 𝗨𝗻𝘀𝘁𝗼𝗽𝗽𝗮𝗯𝗹𝗲 𝗶𝗻 𝟮𝟬𝟮𝟲😍
Start learning for FREE and earn a certification that adds real value to your resume.
𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴:- https://pdlink.in/3LoutZd
𝗖𝘆𝗯𝗲𝗿 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:- https://pdlink.in/3N9VOyW
𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:- https://pdlink.in/497MMLw
👉 Enroll today & future-proof your career!
Start learning for FREE and earn a certification that adds real value to your resume.
𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴:- https://pdlink.in/3LoutZd
𝗖𝘆𝗯𝗲𝗿 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:- https://pdlink.in/3N9VOyW
𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:- https://pdlink.in/497MMLw
👉 Enroll today & future-proof your career!
✅ Coding Interview Questions with Answers Part-1 🧠💻
1. Difference between Compiled and Interpreted Languages
Compiled languages
• Code converts into machine code before execution
• Execution runs faster
• Errors appear at compile time
• Examples: C, C++, Java
Interpreted languages
• Code runs line by line
• Execution runs slower
• Errors appear during runtime
• Examples: Python, JavaScript
Interview tip
• Compiled equals speed
• Interpreted equals flexibility
2. What is Time Complexity? Why it Matters
Time complexity measures how runtime grows with input size
It ignores hardware and focuses on algorithm behavior
Why interviewers care
• Predict performance at scale
• Compare multiple solutions
• Avoid slow logic
Example
• Linear search on n items takes O(n)
• Binary search takes O(log n)
3. What is Space Complexity
Space complexity measures extra memory used by an algorithm
Includes variables, data structures, recursion stack
Example
• Simple loop uses O(1) space
• Recursive Fibonacci uses O(n) stack space
Interview focus
• Faster code with lower memory wins
4. Big O Notation with Examples
Big O describes worst-case performance
Common ones
• O(1): Constant time Example: Access array index
• O(n): Linear time Example: Loop through array
• O(log n): Logarithmic time Example: Binary search
• O(n²): Quadratic time Example: Nested loops
Rule
• Smaller Big O equals better scalability
5. Difference between Array and Linked List
Array
• Fixed size
• Fast index access O(1)
• Slow insertion and deletion
Linked list
• Dynamic size
• Slow access O(n)
• Fast insertion and deletion
Interview rule
• Use arrays for read-heavy tasks
• Use linked lists for frequent inserts
6. What is a Stack? Real Use Cases
Stack follows LIFO Last In, First Out
Operations
• Push
• Pop
• Peek
Real use cases
• Undo and redo
• Function calls
• Browser back button
• Expression evaluation
7. What is a Queue? Types of Queues
Queue follows FIFO First In, First Out
Operations
• Enqueue
• Dequeue
Types
• Simple queue
• Circular queue
• Priority queue
• Deque
Use cases
• Task scheduling
• CPU processes
• Print queues
8. Difference between Stack and Queue
Stack
• LIFO
• One end access
• Used in recursion and undo
Queue
• FIFO
• Two end access
• Used in scheduling and buffering
Memory trick
• Stack equals plates
• Queue equals line
9. What is Recursion? When to Avoid it
Recursion means a function calls itself
Each call waits on the stack
Used when
• Problem breaks into smaller identical subproblems
• Tree and graph traversal
Avoid when
• Deep recursion causes stack overflow
• Iteration works better
10. Difference between Recursion and Iteration
Recursion
• Uses function calls
• More readable
• Higher memory usage
Iteration
• Uses loops
• Faster execution
• Lower memory usage
• Prefer iteration for performance
• Use recursion for clarity
Double Tap ♥️ For Part-2
1. Difference between Compiled and Interpreted Languages
Compiled languages
• Code converts into machine code before execution
• Execution runs faster
• Errors appear at compile time
• Examples: C, C++, Java
Interpreted languages
• Code runs line by line
• Execution runs slower
• Errors appear during runtime
• Examples: Python, JavaScript
Interview tip
• Compiled equals speed
• Interpreted equals flexibility
2. What is Time Complexity? Why it Matters
Time complexity measures how runtime grows with input size
It ignores hardware and focuses on algorithm behavior
Why interviewers care
• Predict performance at scale
• Compare multiple solutions
• Avoid slow logic
Example
• Linear search on n items takes O(n)
• Binary search takes O(log n)
3. What is Space Complexity
Space complexity measures extra memory used by an algorithm
Includes variables, data structures, recursion stack
Example
• Simple loop uses O(1) space
• Recursive Fibonacci uses O(n) stack space
Interview focus
• Faster code with lower memory wins
4. Big O Notation with Examples
Big O describes worst-case performance
Common ones
• O(1): Constant time Example: Access array index
• O(n): Linear time Example: Loop through array
• O(log n): Logarithmic time Example: Binary search
• O(n²): Quadratic time Example: Nested loops
Rule
• Smaller Big O equals better scalability
5. Difference between Array and Linked List
Array
• Fixed size
• Fast index access O(1)
• Slow insertion and deletion
Linked list
• Dynamic size
• Slow access O(n)
• Fast insertion and deletion
Interview rule
• Use arrays for read-heavy tasks
• Use linked lists for frequent inserts
6. What is a Stack? Real Use Cases
Stack follows LIFO Last In, First Out
Operations
• Push
• Pop
• Peek
Real use cases
• Undo and redo
• Function calls
• Browser back button
• Expression evaluation
7. What is a Queue? Types of Queues
Queue follows FIFO First In, First Out
Operations
• Enqueue
• Dequeue
Types
• Simple queue
• Circular queue
• Priority queue
• Deque
Use cases
• Task scheduling
• CPU processes
• Print queues
8. Difference between Stack and Queue
Stack
• LIFO
• One end access
• Used in recursion and undo
Queue
• FIFO
• Two end access
• Used in scheduling and buffering
Memory trick
• Stack equals plates
• Queue equals line
9. What is Recursion? When to Avoid it
Recursion means a function calls itself
Each call waits on the stack
Used when
• Problem breaks into smaller identical subproblems
• Tree and graph traversal
Avoid when
• Deep recursion causes stack overflow
• Iteration works better
10. Difference between Recursion and Iteration
Recursion
• Uses function calls
• More readable
• Higher memory usage
Iteration
• Uses loops
• Faster execution
• Lower memory usage
• Prefer iteration for performance
• Use recursion for clarity
Double Tap ♥️ For Part-2
❤11
𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗵𝗶𝗴𝗵-𝗱𝗲𝗺𝗮𝗻𝗱 𝘀𝗸𝗶𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟲😍
Join FREE Masterclass In Hyderabad/Pune/Noida Cities
𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗲𝘀:-
- 500+ Hiring Partners
- 60+ Hiring Drives
- 100% Placement Assistance
𝗕𝗼𝗼𝗸 𝗮 𝗙𝗥𝗘𝗘 𝗱𝗲𝗺𝗼👇:-
🔹 Hyderabad :- https://pdlink.in/4cJUWtx
🔹 Pune :- https://pdlink.in/3YA32zi
🔹 Noida :- https://linkpd.in/NoidaFSD
Hurry Up 🏃♂️! Limited seats are available
Join FREE Masterclass In Hyderabad/Pune/Noida Cities
𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗲𝘀:-
- 500+ Hiring Partners
- 60+ Hiring Drives
- 100% Placement Assistance
𝗕𝗼𝗼𝗸 𝗮 𝗙𝗥𝗘𝗘 𝗱𝗲𝗺𝗼👇:-
🔹 Hyderabad :- https://pdlink.in/4cJUWtx
🔹 Pune :- https://pdlink.in/3YA32zi
🔹 Noida :- https://linkpd.in/NoidaFSD
Hurry Up 🏃♂️! Limited seats are available
✅ Step-by-Step Approach to Learn Programming 💻🚀
➊ Pick a Programming Language
Start with beginner-friendly languages that are widely used and have lots of resources.
✔ Python – Great for beginners, versatile (web, data, automation)
✔ JavaScript – Perfect for web development
✔ C++ / Java – Ideal if you're targeting DSA or competitive programming
Goal: Be comfortable with syntax, writing small programs, and using an IDE.
➋ Learn Basic Programming Concepts
Understand the foundational building blocks of coding:
✔ Variables, data types
✔ Input/output
✔ Loops (for, while)
✔ Conditional statements (if/else)
✔ Functions and scope
✔ Error handling
Tip: Use visual platforms like W3Schools, freeCodeCamp, or Sololearn.
➌ Understand Data Structures & Algorithms (DSA)
✔ Arrays, Strings
✔ Linked Lists, Stacks, Queues
✔ Hash Maps, Sets
✔ Trees, Graphs
✔ Sorting & Searching
✔ Recursion, Greedy, Backtracking
✔ Dynamic Programming
Use GeeksforGeeks, NeetCode, or Striver's DSA Sheet.
➍ Practice Problem Solving Daily
✔ LeetCode (real interview Qs)
✔ HackerRank (step-by-step)
✔ Codeforces / AtCoder (competitive)
Goal: Focus on logic, not just solutions.
➎ Build Mini Projects
✔ Calculator
✔ To-do list app
✔ Weather app (using APIs)
✔ Quiz app
✔ Rock-paper-scissors game
Projects solidify your concepts.
➏ Learn Git & GitHub
✔ Initialize a repo
✔ Commit & push code
✔ Branch and merge
✔ Host projects on GitHub
Must-have for collaboration.
➐ Learn Web Development Basics
✔ HTML – Structure
✔ CSS – Styling
✔ JavaScript – Interactivity
Then explore:
✔ React.js
✔ Node.js + Express
✔ MongoDB / MySQL
➑ Choose Your Career Path
✔ Web Dev (Frontend, Backend, Full Stack)
✔ App Dev (Flutter, Android)
✔ Data Science / ML
✔ DevOps / Cloud (AWS, Docker)
➒ Work on Real Projects & Internships
✔ Build a portfolio
✔ Clone real apps (Netflix UI, Amazon clone)
✔ Join hackathons
✔ Freelance or open source
✔ Apply for internships
➓ Stay Updated & Keep Improving
✔ Follow GitHub trends
✔ Dev YouTube channels (Fireship, etc.)
✔ Tech blogs (Dev.to, Medium)
✔ Communities (Discord, Reddit, X)
🎯 Remember:
• Consistency > Intensity
• Learn by building
• Debugging is learning
• Track progress weekly
Useful WhatsApp Channels to Learn Programming Languages
Python Programming: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
JavaScript: https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
C++ Programming: https://whatsapp.com/channel/0029VbBAimF4dTnJLn3Vkd3M
Java Programming: https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
👍 React ♥️ for more
➊ Pick a Programming Language
Start with beginner-friendly languages that are widely used and have lots of resources.
✔ Python – Great for beginners, versatile (web, data, automation)
✔ JavaScript – Perfect for web development
✔ C++ / Java – Ideal if you're targeting DSA or competitive programming
Goal: Be comfortable with syntax, writing small programs, and using an IDE.
➋ Learn Basic Programming Concepts
Understand the foundational building blocks of coding:
✔ Variables, data types
✔ Input/output
✔ Loops (for, while)
✔ Conditional statements (if/else)
✔ Functions and scope
✔ Error handling
Tip: Use visual platforms like W3Schools, freeCodeCamp, or Sololearn.
➌ Understand Data Structures & Algorithms (DSA)
✔ Arrays, Strings
✔ Linked Lists, Stacks, Queues
✔ Hash Maps, Sets
✔ Trees, Graphs
✔ Sorting & Searching
✔ Recursion, Greedy, Backtracking
✔ Dynamic Programming
Use GeeksforGeeks, NeetCode, or Striver's DSA Sheet.
➍ Practice Problem Solving Daily
✔ LeetCode (real interview Qs)
✔ HackerRank (step-by-step)
✔ Codeforces / AtCoder (competitive)
Goal: Focus on logic, not just solutions.
➎ Build Mini Projects
✔ Calculator
✔ To-do list app
✔ Weather app (using APIs)
✔ Quiz app
✔ Rock-paper-scissors game
Projects solidify your concepts.
➏ Learn Git & GitHub
✔ Initialize a repo
✔ Commit & push code
✔ Branch and merge
✔ Host projects on GitHub
Must-have for collaboration.
➐ Learn Web Development Basics
✔ HTML – Structure
✔ CSS – Styling
✔ JavaScript – Interactivity
Then explore:
✔ React.js
✔ Node.js + Express
✔ MongoDB / MySQL
➑ Choose Your Career Path
✔ Web Dev (Frontend, Backend, Full Stack)
✔ App Dev (Flutter, Android)
✔ Data Science / ML
✔ DevOps / Cloud (AWS, Docker)
➒ Work on Real Projects & Internships
✔ Build a portfolio
✔ Clone real apps (Netflix UI, Amazon clone)
✔ Join hackathons
✔ Freelance or open source
✔ Apply for internships
➓ Stay Updated & Keep Improving
✔ Follow GitHub trends
✔ Dev YouTube channels (Fireship, etc.)
✔ Tech blogs (Dev.to, Medium)
✔ Communities (Discord, Reddit, X)
🎯 Remember:
• Consistency > Intensity
• Learn by building
• Debugging is learning
• Track progress weekly
Useful WhatsApp Channels to Learn Programming Languages
Python Programming: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
JavaScript: https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
C++ Programming: https://whatsapp.com/channel/0029VbBAimF4dTnJLn3Vkd3M
Java Programming: https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
👍 React ♥️ for more
❤3
💡 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗻-𝗱𝗲𝗺𝗮𝗻𝗱 𝘀𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟲!
Start learning ML for FREE and boost your resume with a certification 🏆
📊 Hands-on learning
🎓 Certificate included
🚀 Career-ready skills
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
https://pdlink.in/4bhetTu
👉 Don’t miss this opportunity
Start learning ML for FREE and boost your resume with a certification 🏆
📊 Hands-on learning
🎓 Certificate included
🚀 Career-ready skills
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
https://pdlink.in/4bhetTu
👉 Don’t miss this opportunity
❤2
How to send follow up email to a recruiter 👇👇
(Tap to copy)
Dear [Recruiter’s Name],
I hope this email finds you doing well. I wanted to take a moment to express my sincere gratitude for the time and consideration you have given me throughout the recruitment process for the [position] role at [company].
I understand that you must be extremely busy and receive countless applications, so I wanted to reach out and follow up on the status of my application. If it’s not too much trouble, could you kindly provide me with any updates or feedback you may have?
I want to assure you that I remain genuinely interested in the opportunity to join the team at [company] and I would be honored to discuss my qualifications further. If there are any additional materials or information you require from me, please don’t hesitate to let me know.
Thank you for your time and consideration. I appreciate the effort you put into recruiting and look forward to hearing from you soon.Warmest regards,(Tap to copy)
❤1👍1
✅ 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
📍 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
❤2
✅ Top 50 JavaScript Interview Questions 💻✨
1. What are the key features of JavaScript?
2. Difference between var, let, and const
3. What is hoisting?
4. Explain closures with an example
5. What is the difference between == and ===?
6. What is event bubbling and capturing?
7. What is the DOM?
8. Difference between null and undefined
9. What are arrow functions?
10. Explain callback functions
11. What is a promise in JS?
12. Explain async/await
13. What is the difference between call, apply, and bind?
14. What is a prototype?
15. What is prototypal inheritance?
16. What is the use of ‘this’ keyword in JS?
17. Explain the concept of scope in JS
18. What is lexical scope?
19. What are higher-order functions?
20. What is a pure function?
21. What is the event loop in JS?
22. Explain microtask vs. macrotask queue
23. What is JSON and how is it used?
24. What are IIFEs (Immediately Invoked Function Expressions)?
25. What is the difference between synchronous and asynchronous code?
26. How does JavaScript handle memory management?
27. What is a JavaScript engine?
28. Difference between deep copy and shallow copy in JS
29. What is destructuring in ES6?
30. What is a spread operator?
31. What is a rest parameter?
32. What are template literals?
33. What is a module in JS?
34. Difference between default export and named export
35. How do you handle errors in JavaScript?
36. What is the use of try...catch?
37. What is a service worker?
38. What is localStorage vs. sessionStorage?
39. What is debounce and throttle?
40. Explain the fetch API
41. What are async generators?
42. How to create and dispatch custom events?
43. What is CORS in JS?
44. What is memory leak and how to prevent it in JS?
45. How do arrow functions differ from regular functions?
46. What are Map and Set in JavaScript?
47. Explain WeakMap and WeakSet
48. What are symbols in JS?
49. What is functional programming in JS?
50. How do you debug JavaScript code?
💬 Tap ❤️ for more!
1. What are the key features of JavaScript?
2. Difference between var, let, and const
3. What is hoisting?
4. Explain closures with an example
5. What is the difference between == and ===?
6. What is event bubbling and capturing?
7. What is the DOM?
8. Difference between null and undefined
9. What are arrow functions?
10. Explain callback functions
11. What is a promise in JS?
12. Explain async/await
13. What is the difference between call, apply, and bind?
14. What is a prototype?
15. What is prototypal inheritance?
16. What is the use of ‘this’ keyword in JS?
17. Explain the concept of scope in JS
18. What is lexical scope?
19. What are higher-order functions?
20. What is a pure function?
21. What is the event loop in JS?
22. Explain microtask vs. macrotask queue
23. What is JSON and how is it used?
24. What are IIFEs (Immediately Invoked Function Expressions)?
25. What is the difference between synchronous and asynchronous code?
26. How does JavaScript handle memory management?
27. What is a JavaScript engine?
28. Difference between deep copy and shallow copy in JS
29. What is destructuring in ES6?
30. What is a spread operator?
31. What is a rest parameter?
32. What are template literals?
33. What is a module in JS?
34. Difference between default export and named export
35. How do you handle errors in JavaScript?
36. What is the use of try...catch?
37. What is a service worker?
38. What is localStorage vs. sessionStorage?
39. What is debounce and throttle?
40. Explain the fetch API
41. What are async generators?
42. How to create and dispatch custom events?
43. What is CORS in JS?
44. What is memory leak and how to prevent it in JS?
45. How do arrow functions differ from regular functions?
46. What are Map and Set in JavaScript?
47. Explain WeakMap and WeakSet
48. What are symbols in JS?
49. What is functional programming in JS?
50. How do you debug JavaScript code?
💬 Tap ❤️ for more!
❤7
𝗙𝗥𝗘𝗘 𝗖𝗮𝗿𝗲𝗲𝗿 𝗖𝗮𝗿𝗻𝗶𝘃𝗮𝗹 𝗯𝘆 𝗛𝗖𝗟 𝗚𝗨𝗩𝗜😍
Prove your skills in an online hackathon, clear tech interviews, and get hired faster
Highlightes:-
- 21+ Hiring Companies & 100+ Open Positions to Grab
- Get hired for roles in AI, Full Stack, & more
Experience the biggest online job fair with Career Carnival by HCL GUVI
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
https://pdlink.in/4bQP5Ee
Hurry Up🏃♂️.....Limited Slots Available
Prove your skills in an online hackathon, clear tech interviews, and get hired faster
Highlightes:-
- 21+ Hiring Companies & 100+ Open Positions to Grab
- Get hired for roles in AI, Full Stack, & more
Experience the biggest online job fair with Career Carnival by HCL GUVI
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
https://pdlink.in/4bQP5Ee
Hurry Up🏃♂️.....Limited Slots Available
❤2
✅ Python for Data Science – Part 1: NumPy Interview Q&A 📊
🔹 1. What is NumPy and why is it important?
NumPy (Numerical Python) is a powerful Python library for numerical computing. It supports fast array operations, broadcasting, linear algebra, and random number generation. It’s the backbone of many data science libraries like Pandas and Scikit-learn.
🔹 2. Difference between Python list and NumPy array
Python lists can store mixed data types and are slower for numerical operations. NumPy arrays are faster, use less memory, and support vectorized operations, making them ideal for numerical tasks.
🔹 3. How to create a NumPy array
🔹 4. What is broadcasting in NumPy?
Broadcasting lets you perform operations on arrays of different shapes. For example, adding a scalar to an array applies the operation to each element.
🔹 5. How to generate random numbers
Use
🔹 6. How to reshape an array
Use
Example:
🔹 7. Basic statistical operations
Use functions like
🔹 8. Difference between zeros(), ones(), and empty()
🔹 9. Handling missing values
Use
Example:
🔹 10. Element-wise operations
NumPy supports element-wise addition, subtraction, multiplication, and division.
Example:
💡 Pro Tip: NumPy is all about speed and efficiency. Mastering it gives you a huge edge in data manipulation and model building.
Double Tap ❤️ For More
🔹 1. What is NumPy and why is it important?
NumPy (Numerical Python) is a powerful Python library for numerical computing. It supports fast array operations, broadcasting, linear algebra, and random number generation. It’s the backbone of many data science libraries like Pandas and Scikit-learn.
🔹 2. Difference between Python list and NumPy array
Python lists can store mixed data types and are slower for numerical operations. NumPy arrays are faster, use less memory, and support vectorized operations, making them ideal for numerical tasks.
🔹 3. How to create a NumPy array
import numpy as np
arr = np.array([1, 2, 3])
🔹 4. What is broadcasting in NumPy?
Broadcasting lets you perform operations on arrays of different shapes. For example, adding a scalar to an array applies the operation to each element.
🔹 5. How to generate random numbers
Use
np.random.rand() for uniform distribution, np.random.randn() for normal distribution, and np.random.randint() for random integers.🔹 6. How to reshape an array
Use
.reshape() to change the shape of an array without changing its data. Example:
arr.reshape(2, 3) turns a 1D array of 6 elements into a 2x3 matrix.🔹 7. Basic statistical operations
Use functions like
mean(), std(), var(), sum(), min(), and max() to get quick stats from your data.🔹 8. Difference between zeros(), ones(), and empty()
np.zeros() creates an array filled with 0s, np.ones() with 1s, and np.empty() creates an array without initializing values (faster but unpredictable).🔹 9. Handling missing values
Use
np.nan to represent missing values and np.isnan() to detect them. Example:
arr = np.array([1, 2, np.nan])
np.isnan(arr) # Output: [False False True]
🔹 10. Element-wise operations
NumPy supports element-wise addition, subtraction, multiplication, and division.
Example:
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
a + b # Output: [5 7 9]
💡 Pro Tip: NumPy is all about speed and efficiency. Mastering it gives you a huge edge in data manipulation and model building.
Double Tap ❤️ For More
❤4