Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses

📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲😍

🚀Upgrade your skills with industry-relevant Data Analytics training at ZERO cost

✅ Beginner-friendly
✅ Certificate on completion
✅ High-demand skill in 2026

𝐋𝐢𝐧𝐤 👇:-

https://pdlink.in/497MMLw

📌 100% FREE – Limited seats available!

❤1👍1

976 views04:48

Coding interview questions with concise answers for software roles:

1️⃣ What happens when you type a URL and hit Enter?
Answer:
- DNS Lookup → IP address
- Browser sends HTTP/HTTPS request
- Server responds with HTML/CSS/JS
- Browser builds DOM, applies styles (CSSOM), runs JS
- Page is rendered

2️⃣ Difference between var, let, and const?
Answer:
- var: function-scoped, hoisted
- let: block-scoped, not hoisted
- const: block-scoped, can’t be reassigned

3️⃣ Reverse a String in JavaScript

function reverseString(str) {
  return str.split('').reverse().join('');
}

4️⃣ Find the max number in an array

const max = Math.max(...arr);

5️⃣ Write a function to check if a number is prime

function isPrime(n) {
  if (n < 2) return false;
  for (let i = 2; i <= Math.sqrt(n); i++) {
    if (n % i === 0) return false;
  }
  return true;
}

6️⃣ What is closure in JavaScript?
Answer:
A function that remembers variables from its outer scope even after the outer function has returned.

7️⃣ What is event delegation?
Answer:
Attaching a single event listener to a parent element to manage events on its children using event.target.

8️⃣ Difference between == and ===
Answer:
- == checks value (with type coercion)
- === checks value + type (strict comparison)

9️⃣ What is the Virtual DOM?
Answer:
A lightweight copy of the real DOM used in React. React updates the virtual DOM first and then applies only the changes to the real DOM for efficiency.

🔟 Write code to remove duplicates from an array

const uniqueArr = [...new Set(arr)];

React ❤️ for more

❤4

1.11K viewsedited 08:15

𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗯𝘆 𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲😍

Deadline: 18th January 2026

Eligibility: Open to everyone
Duration: 6 Months
Program Mode: Online
Taught By: IIT Roorkee Professors

Companies majorly hire candidates having Data Science and Artificial Intelligence knowledge these days.

𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗶𝗻𝗸👇:

https://pdlink.in/4qHVFkI

Only Limited Seats Available!

804 views09:49

❤4

849 viewsedited 12:28

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!

❤1

730 views07:09

✅ 10 Key Programming Differences! 💻🚀

1️⃣ Python 2 vs Python 3
➡️ Python 2: Legacy, no updates
➡️ Python 3: Modern, better syntax support
📌 Always use Python 3 for new projects.

2️⃣ Static vs Dynamic Typing
➡️ Static: Type declared (e.g., Java, C++)
➡️ Dynamic: Type inferred at runtime (e.g., Python, JavaScript)
📌 Static = fewer bugs, Dynamic = faster dev

3️⃣ Abstraction vs Encapsulation
➡️ Abstraction: Hides complexity
➡️ Encapsulation: Hides data
📌 Abstraction = "What", Encapsulation = "How"

4️⃣ REST vs SOAP (APIs)
➡️ REST: Lightweight, uses HTTP
➡️ SOAP: Protocol, strict rules
📌 REST is more common today

5️⃣ SQL vs NoSQL
➡️ SQL: Structured data, tables (e.g., MySQL)
➡️ NoSQL: Unstructured, scalable (e.g., MongoDB)
📌 SQL = Relational, NoSQL = Flexible

6️⃣ For Loop vs While Loop
➡️ For: Known iterations
➡️ While: Unknown, condition-based
📌 Use for when count is known.

7️⃣ Function vs Method
➡️ Function: Independent block
➡️ Method: Function inside class
📌 All methods are functions, not vice versa

8️⃣ Frontend vs Backend
➡️ Frontend: User interface (HTML, CSS, JS)
➡️ Backend: Server logic, DB (Node.js, Python, etc.)
📌 Frontend = what users see

9️⃣ Procedural vs OOP
➡️ Procedural: Functions logic
➡️ OOP: Objects, classes
📌 OOP = more modular reusable

🔟 Null vs Undefined (JavaScript)
➡️ Null: Assigned empty value
➡️ Undefined: Variable declared, not assigned
📌 typeof null is 'object', quirky but true!

💬 Tap ❤️ if you found this helpful!

❤5

780 views10:34

🎁❗️TODAY FREE❗️🎁

Entry to our VIP channel is completely free today. Tomorrow it will cost $500! 🔥

JOIN 👇

https://t.iss.one/+49f4gRT_WB9mMDli
https://t.iss.one/+49f4gRT_WB9mMDli
https://t.iss.one/+49f4gRT_WB9mMDli

❤2

858 views13:59

🔰 Python data types with example

❤1

783 views16:53

✅ Core Data Structures Part 2 – Stacks Queues 📚📥📤

Stacks and queues are fundamental linear data structures used in many algorithms and real-world applications like undo operations, task scheduling, and more.

1️⃣ What is a Stack?
A Stack is a Last-In-First-Out (LIFO) structure.
Think of a stack of plates: you add (push) and remove (pop) from the top.

Operations:
• push(item) – Add item to the top
• pop() – Remove item from the top
• peek() – View top item without removing
• is_empty() – Check if stack is empty

Python Implementation (Using List)

stack = []

# Push
stack.append(10)
stack.append(20)

# Pop
print(stack.pop())  # 20

# Peek
print(stack[-1])    # 10

# Check empty
print(len(stack) == 0)

2️⃣ What is a Queue?
A Queue is a First-In-First-Out (FIFO) structure.
Think of a line at a ticket counter: first come, first served.

Operations:
• enqueue(item) – Add item to the rear
• dequeue() – Remove item from the front
• peek() – View front item
• is_empty() – Check if queue is empty

Python Implementation (Using collections.deque)

from collections import deque

queue = deque()

# Enqueue
queue.append(10)
queue.append(20)

# Dequeue
print(queue.popleft())  # 10

# Peek
print(queue[0])         # 20

# Check empty
print(len(queue) == 0)

3️⃣ Stack Using Linked List (Python)

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

class Stack:
    def __init__(self):
        self.top = None

    def push(self, data):
        node = Node(data)
        node.next = self.top
        self.top = node

    def pop(self):
        if not self.top:
            return None
        data = self.top.data
        self.top = self.top.next
        return data

4️⃣ Queue Using Linked List (Python)

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

class Queue:
    def __init__(self):
        self.front = self.rear = None

    def enqueue(self, data):
        node = Node(data)
        if not self.rear:
            self.front = self.rear = node
        else:
            self.rear.next = node
            self.rear = node

    def dequeue(self):
        if not self.front:
            return None
        data = self.front.data
        self.front = self.front.next
        if not self.front:
            self.rear = None
        return data

📝 Practice Tasks
1. Implement a stack using a list
2. Implement a queue using a list
3. Reverse a string using a stack
4. Check for balanced parentheses using a stack
5. Simulate a queue using two stacks

Double Tap ♥️ For More

❤4

686 views09:08

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 )

❤2

336 views11:07

𝟱 𝗖𝗼𝗱𝗶𝗻𝗴 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗧𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗠𝗮𝘁𝘁𝗲𝗿 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 💻

You don’t need to be a LeetCode grandmaster.
But data science interviews still test your problem-solving mindset—and these 5 types of challenges are the ones that actually matter.

Here’s what to focus on (with examples) 👇

🔹 1. String Manipulation (Common in Data Cleaning)

✅ Parse messy columns (e.g., split “Name_Age_City”)
✅ Regex to extract phone numbers, emails, URLs
✅ Remove stopwords or HTML tags in text data

Example: Clean up a scraped dataset from LinkedIn bias

🔹 2. GroupBy and Aggregation with Pandas

✅ Group sales data by product/region
✅ Calculate avg, sum, count using .groupby()
✅ Handle missing values smartly

Example: “What’s the top-selling product in each region?”

🔹 3. SQL Join + Window Functions

✅ INNER JOIN, LEFT JOIN to merge tables
✅ ROW_NUMBER(), RANK(), LEAD(), LAG() for trends
✅ Use CTEs to break complex queries

Example: “Get 2nd highest salary in each department”

🔹 4. Data Structures: Lists, Dicts, Sets in Python

✅ Use dictionaries to map, filter, and count
✅ Remove duplicates with sets
✅ List comprehensions for clean solutions

Example: “Count frequency of hashtags in tweets”

🔹 5. Basic Algorithms (Not DP or Graphs)

✅ Sliding window for moving averages
✅ Two pointers for duplicate detection
✅ Binary search in sorted arrays

Example: “Detect if a pair of values sum to 100”

🎯 Tip: Practice challenges that feel like real-world data work, not textbook CS exams.

Use platforms like:

StrataScratch
Hackerrank (SQL + Python)
Kaggle Code

I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Like if you need similar content 😄👍

❤1

163 views17:30

About

Blog

Apps

Platform