Here are some tricky🧩 SQL interview questions!

1. Find the second-highest salary in a table without using LIMIT or TOP.

2. Write a SQL query to find all employees who earn more than their managers.

3. Find the duplicate rows in a table without using GROUP BY.

4. Write a SQL query to find the top 10% of earners in a table.

5. Find the cumulative sum of a column in a table.

6. Write a SQL query to find all employees who have never taken a leave.

7. Find the difference between the current row and the next row in a table.

8. Write a SQL query to find all departments with more than one employee.

9. Find the maximum value of a column for each group without using GROUP BY.

10. Write a SQL query to find all employees who have taken more than 3 leaves in a month.

These questions are designed to test your SQL skills, including your ability to write efficient queries, think creatively, and solve complex problems.

Here are the answers to these questions:

1. SELECT MAX(salary) FROM table WHERE salary NOT IN (SELECT MAX(salary) FROM table)

2. SELECT e1.* FROM employees e1 JOIN employees e2 ON e1.manager_id = (link unavailable) WHERE e1.salary > e2.salary

3. SELECT * FROM table WHERE rowid IN (SELECT rowid FROM table GROUP BY column HAVING COUNT(*) > 1)

4. SELECT * FROM table WHERE salary > (SELECT PERCENTILE_CONT(0.9) WITHIN GROUP (ORDER BY salary) FROM table)

5. SELECT column, SUM(column) OVER (ORDER BY rowid) FROM table

6. SELECT * FROM employees WHERE id NOT IN (SELECT employee_id FROM leaves)

7. SELECT *, column - LEAD(column) OVER (ORDER BY rowid) FROM table

8. SELECT department FROM employees GROUP BY department HAVING COUNT(*) > 1

9. SELECT MAX(column) FROM table WHERE column NOT IN (SELECT MAX(column) FROM table GROUP BY group_column)

Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/mysqldata

Like this post if you need more 👍❤️

Hope it helps :)

✅ Coding Basics You Should Know 👨‍💻

If you're starting your journey in programming, here are the core concepts every beginner must understand. These fundamentals apply across languages and form the building blocks of all code.

1️⃣ What is Coding?
Coding is writing instructions a computer can understand and execute. These instructions, called code, are written in programming languages like Python, JavaScript, or C++. Computers follow them step-by-step to perform tasks, from simple calculations to complex apps.

2️⃣ Programming Languages
Choose based on your goals:
⦁ Python – Beginner-friendly with simple syntax; ideal for automation, data analysis, and AI.
⦁ JavaScript – Powers interactive websites; essential for web development.
⦁ C++ / Java – For performance-critical apps, games, or systems; great for competitive programming.
All share syntax rules, variables, functions, and control flow. Start with one and practice consistently.

3️⃣ Variables & Data Types
Variables store and reuse data; data types define what kind of information they hold.

name = "Alice"  # string (text)
age = 25        # integer (whole number)
height = 5.9    # float (decimal)
is_student = True  # boolean (true/false)

Assign with =; choose types to match your data for efficiency.

4️⃣ Conditions & Loops
Make decisions (conditions) and repeat actions (loops).

# Condition
if age > 18:
    print("Adult")
else:
    print("Minor")

# Loop
for i in range(5):
    print(i)  # Outputs 0 to 4

Use if/else for branches; for/while for iterations to avoid repetitive code.

5️⃣ Functions
Reusable code blocks that perform specific tasks, reducing duplication.

def greet(name):
    return f"Hello, {name}!"

result = greet("Alice")  # Call the function
print(result)  # "Hello, Alice!"

Define with def; pass inputs (parameters) and return outputs.

6️⃣ Data Structures
Organize data for easy access and manipulation.
⦁ Lists / Arrays – Ordered collections: fruits = ["apple", "banana"].
⦁ Dictionaries / Maps – Key-value pairs: person = {"name": "John", "age": 30}.
⦁ Stacks & Queues – For LIFO/FIFO operations (e.g., undo in apps).
⦁ Sets – Unique, unordered items for fast lookups.
Choose based on needs: lists for sequences, dicts for associations.

7️⃣ Problem Solving (DSA)
Break problems into steps using Data Structures and Algorithms (DSA).
⦁ Algorithms: Step-by-step solutions like searching (linear/binary) or sorting (bubble/quick).
⦁ Logic & patterns: Identify inputs, processes, outputs; think recursively for trees.
⦁ Efficiency: Measure time/space complexity (Big O) to optimize code.
Practice on platforms like LeetCode to build intuition.

8️⃣ Debugging
Finding and fixing errors in code.
⦁ Use print() statements to check values mid-execution.
⦁ Leverage IDE tools (VS Code debugger, breakpoints) for step-through inspection.
Common bugs: Syntax errors (typos), logic errors (wrong output), runtime errors (crashes). Read error messages—they guide you.

9️⃣ Git & GitHub
Version control for tracking changes and collaborating.

git init          # Start a repo
git add.         # Stage files
git commit -m "Initial code"  # Save snapshot
git push          # Upload to GitHub

GitHub hosts code publicly; use branches for features, pull requests for reviews.

🔟 Build Projects
Apply concepts by creating:
⦁ Calculator – Practice math operations and user input.
⦁ To-Do List – Handle arrays, functions, and persistence.
⦁ Weather App – Fetch APIs with async code.
⦁ Portfolio Website – Combine HTML/CSS/JS for real-world output.
Start small, iterate, and deploy to showcase skills.

💡 Coding is best learned by doing. Practice daily on platforms like LeetCode, HackerRank, or Codewars. Join communities (Reddit's r/learnprogramming) for support.

💬 Tap ❤️ for more!

i think here are my 10 Smart Learning 𝙨𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙚𝙨 that i understand Lately .....really 𝙖𝙥𝙥𝙡𝙮  it✌️

1. Learn in 𝙨𝙢𝙖𝙡𝙡 Blocks

✔️Don't overload your brain. Study 25–40 minutes, then rest 5–10 minutes.
Short sessions boost focus and memory.

2. 𝙤𝙧𝙜𝙖𝙣𝙞𝙯𝙚 What You Learn

Create:
✔ key points
✔ mind maps
✔ bullet summaries
This helps your brain structure information.

3. 𝙧𝙚-𝙩𝙚𝙖𝙘𝙝 What You Know

✔️Explain the topic to someone else—or even to yourself.
If you can explain it simply, you truly understand it.

4.  𝙧𝙚𝙥𝙚𝙩𝙞𝙩𝙞𝙤𝙣

✔️Re-read, review, and revisit information
after 1 day → 1 week → 1 month
This builds long-term memory.

5. Focus on 𝙪𝙣𝙙𝙚𝙧𝙨𝙩𝙖𝙣𝙙𝙞𝙣𝙜, Not Memorizing

Ask yourself:
“Why does this work?”
“How does this connect to what I already know?”

6. Set 𝙢𝙞𝙘𝙧𝙤-Goals

Instead of “learn everything,” say:
           “Today I will learn 3    definitions”
          “I will finish one chapter”

Small daily wins = big results.

✔️ 7. Learn Actively, 𝙣𝙤𝙩 Passively

Avoid only reading or highlighting.
Do this instead:
✔️practice questions
✔️make notes
✔️ solve tasks
✔️ repeat aloud

8. Use 𝙨𝙢𝙖𝙧𝙩 𝙩𝙤𝙤𝙡𝙨

✔️planners
▫️Use smart apps that hulp for planning like NOTION
They save time and accelerate learning.

9. 𝙩𝙖𝙠𝙚 𝙘𝙖𝙧𝙚 of Your Brain

✔ enough sleep
✔ water
✔ movement
✔ good diet
Your brain learns better when your body is fine.

10. Make Learning 𝙛𝙪𝙣

Connect learning with interest:
✔️ gamify your progress
✔️explore beyond the standard
✔️ reward yourself

✅ Essential Programming Acronyms You Should Know 💻🧠

API → Application Programming Interface
IDE → Integrated Development Environment
OOP → Object-Oriented Programming
HTML → HyperText Markup Language
CSS → Cascading Style Sheets
SQL → Structured Query Language
JSON → JavaScript Object Notation
DOM → Document Object Model
CRUD → Create, Read, Update, Delete
SDK → Software Development Kit
UI → User Interface
UX → User Experience
CLI → Command Line Interface
HTTP → HyperText Transfer Protocol
REST → Representational State Transfer

💬 Tap ❤️ for more!

Have you joined the Whatsapp Channel yet? 😍 Get quick job updates :)

https://whatsapp.com/channel/0029VaxngnVInlqV6xJhDs3m

🗄️ SQL Developer Roadmap

📂 SQL Basics (SELECT, WHERE, ORDER BY)
∟📂 Joins (INNER, LEFT, RIGHT, FULL)
∟📂 Aggregate Functions (COUNT, SUM, AVG)
∟📂 Grouping Data (GROUP BY, HAVING)
∟📂 Subqueries & Nested Queries
∟📂 Data Modification (INSERT, UPDATE, DELETE)
∟📂 Database Design (Normalization, Keys)
∟📂 Indexing & Query Optimization
∟📂 Stored Procedures & Functions
∟📂 Transactions & Locks
∟📂 Views & Triggers
∟📂 Backup & Restore
∟📂 Working with NoSQL basics (optional)
∟📂 Real Projects & Practice
∟✅ Apply for SQL Dev Roles

❤️ React for More!

✅ Top Coding Platforms for Practice Growth 🚀💻

If you want to get better at programming, these platforms will boost your learning and problem-solving:

1️⃣ LeetCode
Best for interview preparation, DSA, and company-specific problems.

2️⃣ HackerRank
Great for beginners and intermediate coders to practice problems by domains like Python, SQL, etc.

3️⃣ Codeforces
Competitive programming platform. Good for contests and improving speed.

4️⃣ GeeksforGeeks
Complete tutorials, coding problems, and interview experiences.

5️⃣ CodeChef
Coding contests, problem sets, and beginner-friendly learning paths.

6️⃣ AtCoder / HackerEarth
Great for regular contests and practice problems.

7️⃣ Codewars
Solve challenges (kata) and improve code style and efficiency.

8️⃣ Coderbyte
Good for interview prep, real coding assessments, and company mock rounds.

9️⃣ TopCoder
Advanced competitive programming and challenges with rankings.

🔟 Exercism
Community-driven platform focused on improving your code through mentorship.

💬 Tip: Choose one platform, practice daily, and track your progress.

Coding Interview Resources: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

🔥 Double Tap ❤️ if you found this helpful!

🧩 Core Computer Science Concepts

🧠 Big-O Notation
🗂️ Data Structures
🔁 Recursion
🧵 Concurrency vs Parallelism
📦 Memory Management
🔒 Race Conditions
🌐 Networking Basics
⚙️ Operating Systems
🧪 Testing Strategies
📐 System Design

React ❤️ for more like this

OnSpace Mobile App builder: Build AI Apps in minutes

👉https://www.onspace.ai/agentic-app-builder?via=tg_pg

With OnSpace, you can build AI Mobile Apps by chatting with AI, and publish to PlayStore or AppStore.

What will you get:
- Create app by chatting with AI;
- Integrate with Any top AI power just by giving order (like Sora2, Nanobanan Pro & Gemini 3 Pro);
- Download APK,AAB file, publish to AppStore.
- Add payments and monetize like in-app-purchase and Stripe.
- Functional login & signup.
- Database + dashboard in minutes.
- Full tutorial on YouTube and within 1 day customer service

Kandinsky 5.0 Video Lite and Kandinsky 5.0 Video Pro generative models on the global text-to-video landscape

🔘Pro is currently the #1 open-source model worldwide
🔘Lite (2B parameters) outperforms Sora v1.
🔘Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro — these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ±21.

Useful links
🔘Full leaderboard: LM Arena
🔘Kandinsky 5.0 details: technical report
🔘Open-source Kandinsky 5.0: GitHub and Hugging Face

JavaScript is a versatile, high-level programming language primarily used for web development. It allows developers to create dynamic and interactive web pages. Here’s a comprehensive overview of JavaScript:

▎1. What is JavaScript?

• Definition: A noscripting language that enables interactive web pages. It is an essential part of web applications and is often used alongside HTML and CSS.
• History: Developed by Brendan Eich in 1995, JavaScript has evolved significantly and is now standardized under ECMAScript.

▎2. Key Features of JavaScript

• Client-Side Scripting: Runs in the user's browser, allowing for real-time interaction without needing to reload the page.
• Dynamic Typing: Variables can hold data of any type, and types can change at runtime.
• Prototype-Based Object Orientation: Uses prototypes rather than classes for inheritance.
• Event-Driven Programming: Responds to user events like clicks, key presses, and mouse movements.

▎3. Core Concepts

• Variables: Used to store data values. Declared using var, let, or const.

  let name = "John";
  const age = 30;
  

• Data Types: Includes:
– Primitive Types: Number, String, Boolean, Null, Undefined, Symbol (ES6).
– Reference Types: Objects, Arrays, Functions.

• Functions: Blocks of code designed to perform a particular task.

  function greet() {
      console.log("Hello, World!");
  }
  

• Control Structures: Includes conditional statements (if, else, switch) and loops (for, while).

▎4. Working with the DOM

JavaScript can manipulate the Document Object Model (DOM), allowing developers to change the document structure, style, and content.

document.getElementById("myElement").innerHTML = "New Content";

▎5. JavaScript Frameworks and Libraries

• Frameworks: Provide a structure for building applications (e.g., Angular, Vue.js).
• Libraries: Simplify specific tasks (e.g., jQuery for DOM manipulation, D3.js for data visualization).

▎6. Asynchronous JavaScript

JavaScript supports asynchronous programming through:
• Callbacks: Functions passed as arguments to other functions.
• Promises: Objects representing the eventual completion (or failure) of an asynchronous operation.
• Async/Await: Syntactic sugar over promises that makes asynchronous code easier to read.

async function fetchData() {
    const response = await fetch('https://api.example.com/data');
    const data = await response.json();
    console.log(data);
}

▎7. Error Handling

JavaScript uses try, catch, and finally blocks to handle errors gracefully.

try {
    // Code that may throw an error
} catch (error) {
    console.error("An error occurred:", error);
} finally {
    // Code that runs regardless of success or failure
}

▎8. Modern JavaScript (ES6 and Beyond)

ES6 (ECMAScript 2015) introduced many new features:
• Arrow Functions:

  const add = (a, b) => a + b;
  

• Template Literals:

  const greeting = Hello, ${name}!;
  

• Destructuring:

  const person = { name: "Alice", age: 25 };
  const { name, age } = person;
  

▎9. Resources for Learning JavaScript

• Online Courses: Codecademy, freeCodeCamp, Udemy.
• Books: "You Don’t Know JS" series by Kyle Simpson, "Eloquent JavaScript" by Marijn Haverbeke.
• Documentation: MDN Web Docs (Mozilla Developer Network) is an excellent resource for JavaScript documentation.

▎10. Best Practices

• Write clean and readable code.
• Use meaningful variable and function names.
• Comment your code appropriately.
• Keep functions small and focused on a single task.
• Use version control (e.g., Git) for managing changes.

🧩 Core Computer Science Concepts

🧠 Big-O Notation
🗂️ Data Structures
🔁 Recursion
🧵 Concurrency vs Parallelism
📦 Memory Management
🔒 Race Conditions
🌐 Networking Basics
⚙️ Operating Systems
🧪 Testing Strategies
📐 System Design

React ❤️ for more like this

🚀 Roadmap to Master C++ in 50 Days! 💻🧠

📅 Week 1–2: Basics Syntax
🔹 Day 1–5: C++ setup, input/output, variables, data types
🔹 Day 6–10: Operators, conditionals (if/else), loops (for, while)

📅 Week 3–4: Functions Arrays
🔹 Day 11–15: Functions, scope, pass by value/reference
🔹 Day 16–20: Arrays, strings, 2D arrays, basic problems

📅 Week 5–6: OOP STL
🔹 Day 21–25: Classes, objects, constructors, inheritance
🔹 Day 26–30: Polymorphism, encapsulation, abstraction
🔹 Day 31–35: Standard Template Library (vector, stack, queue, map)

📅 Week 7–8: Advanced Concepts
🔹 Day 36–40: Pointers, dynamic memory, references
🔹 Day 41–45: File handling, exception handling

🎯 Final Stretch: DSA Projects
🔹 Day 46–48: Sorting, searching, recursion, linked lists
🔹 Day 49–50: Mini projects like calculator, student DB, or simple game

💬 Tap ❤️ for more!

𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗕𝘆 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 😍

Roadmap to land your dream job in top product-based companies

𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗲𝘀:-
- 90-Day Placement Plan
- Tech & Non-Tech Career Path
- Interview Preparation Tips
- Live Q&A

𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 

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Date & Time:- 06th January 2026 , 7PM

🔖 40 NumPy methods that cover 95% of tasks

A convenient cheat sheet for those who work with data analysis and ML.

Here are collected the main functions for:

▶️ Creating and modifying arrays;
▶️ Mathematical operations;
▶️ Working with matrices and vectors;
▶️ Sorting and searching for values.

Save it for yourself — it will come in handy when working with NumPy.

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𝗢𝘁𝗵𝗲𝗿 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀:- https://pdlink.in/4qgtrxU

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✅ DSA Roadmap: Part 1 – Time & Space Complexity ⏱️📊

Understanding time and space complexity is crucial for writing efficient code. It helps you estimate how your algorithm will perform as input size grows.

1️⃣ What is Time Complexity? 
Time complexity tells us how fast an algorithm runs based on input size (n). It doesn't measure time in seconds — it measures growth rate.

Example (Python):

for i in range(n):
    print(i)

Runs n times → O(n) time

Example (Java):

for (int i = 0; i < n; i++) {
    System.out.println(i);
}

Example (C++):

for (int i = 0; i < n; i++) {
    cout << i << endl;
}

2️⃣ Common Time Complexities (Best to Worst): 
O(1) – Constant (e.g., array access) 
O(log n) – Logarithmic (e.g., binary search) 
O(n) – Linear (e.g., single loop) 
O(n log n) – Efficient sorting (e.g., merge sort) 
O(n²) – Quadratic (e.g., nested loops) 
O(2ⁿ), O(n!) – Very slow (e.g., recursive brute force)

3️⃣ What is Space Complexity? 
It tells us how much extra memory your code uses depending on input size.

Example:

arr = [0] * n  # O(n) space

If no extra structures are used → O(1) space

4️⃣ Why It Matters 
• Handles large inputs without crashing 
• Crucial in coding interviews 
• Essential for scalable systems

5️⃣ Practice Task – Guess the Complexity

a) Nested loop

for (int i = 0; i < n; i++) {
    for (int j = 0; j < n; j++) {
        System.out.println(i + ", " + j);
    }
}

// O(n²)

b) Binary search

while (low <= high) {
    int mid = (low + high) / 2;
    if (arr[mid] == target) break;
}

// O(log n)

c) Recursive Fibonacci

def fib(n):
    if n <= 1:
        return n
    return fib(n-1) + fib(n-2)

// O(2^n)

Takeaway: 
Always analyze two things before solving any problem: 
– How many steps will this take? (Time) 
– How much memory does it use? (Space)

💬 Tap ❤️ for more

✅ DSA Part 2 – Recursion 🔁🧠

Recursion is when a function calls itself to solve smaller subproblems. It's powerful but needs a base case to avoid infinite loops.

1️⃣ What is Recursion?
A recursive function solves a part of the problem and calls itself on the remaining part.

Basic Python Example:

def countdown(n):
    if n == 0:
        print("Done!")
        return
    print(n)
    countdown(n - 1)

▶️ Counts down from n to 0

2️⃣ Key Parts of Recursion:
• Base case – Stops recursion
• Recursive case – Function calls itself

Java Example – Factorial:

int factorial(int n) {
    if (n == 0) return 1;
    return n * factorial(n - 1);
}

C++ Example – Sum of Array:

int sum(int arr[], int n) {
    if (n == 0) return 0;
    return arr[n - 1] + sum(arr, n - 1);
}

3️⃣ Why Use Recursion?
• Breaks complex problems into simpler ones
• Great for trees, graphs, backtracking, divide conquer

4️⃣ When Not to Use It?
• Large inputs can cause stack overflow
• Use loops if recursion is too deep or inefficient

5️⃣ Practice Task:
✅ Write a recursive function to calculate power (a^b)
✅ Write a function to reverse a string recursively
✅ Try basic Fibonacci using recursion

👇 Solution for Practice Task

✅ 1. Recursive Power Function (a^b)

Python:

def power(a, b):
    if b == 0:
        return 1
    return a * power(a, b - 1)

print(power(2, 3))  # Output: 8

C++:

int power(int a, int b) {
    if (b == 0) return 1;
    return a * power(a, b - 1);
}
// Example: cout << power(2, 3); // Output: 8

Java:

int power(int a, int b) {
    if (b == 0) return 1;
    return a * power(a, b - 1);
}
// Example: System.out.println(power(2, 3)); // Output: 8

✅ 2. Reverse String Recursively

Python:

def reverse(s):
    if len(s) == 0:
        return ""
    return reverse(s[1:]) + s[0]

print(reverse("hello"))  # Output: "olleh"

C++:

string reverse(string s) {
    if (s.length() == 0) return "";
    return reverse(s.substr(1)) + s[0];
}
// Example: cout << reverse("hello"); // Output: "olleh"

Java:

String reverse(String s) {
    if (s.isEmpty()) return "";
    return reverse(s.substring(1)) + s.charAt(0);
}
// Example: System.out.println(reverse("hello")); // Output: "olleh"

✅ 3. Fibonacci Using Recursion

Python:

def fib(n):
    if n <= 1:
        return n
    return fib(n - 1) + fib(n - 2)

print(fib(6))  # Output: 8

C++:

int fib(int n) {
    if (n <= 1) return n;
    return fib(n - 1) + fib(n - 2);
}
// Example: cout << fib(6); // Output: 8

Java:

int fib(int n) {
    if (n <= 1) return n;
    return fib(n - 1) + fib(n - 2);
}
// Example: System.out.println(fib(6)); // Output: 8

*Double Tap ♥️ For More*

✅ DSA Part 3 – Arrays & Sliding Window 📊🧠

Arrays are the foundation of data structures. Mastering them unlocks many advanced topics like sorting, searching, and dynamic programming.

1️⃣ What is an Array?
An array is a collection of elements stored at contiguous memory locations. All elements are of the same data type.

Python Example:

arr = [10, 20, 30, 40]
print(arr[2])  # Output: 30

C++ Example:

int arr[] = {10, 20, 30, 40};
cout << arr[2];  // Output: 30

Java Example:

int[] arr = {10, 20, 30, 40};
System.out.println(arr[2]);  // Output: 30

2️⃣ Basic Array Operations:
• Insert
• Delete
• Traverse
• Search
• Update

Python – Traversal:

for i in arr:
    print(i)

C++ – Search:

for (int i = 0; i < n; i++) {
    if (arr[i] == key) {
        // Found
    }
}

Java – Update:

arr[1] = 99;  // Updates second element

3️⃣ Sliding Window Technique 🪟
Used to reduce time complexity in problems involving subarrays or substrings.

▶️ Fixed-size window:
Find max sum of subarray of size k
▶️ Variable-size window:
Find longest substring with unique characters

4️⃣ Sliding Window – Max Sum Subarray (Size k)

Python:

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, 4, 2, 10, 2, 3], 3))  # Output: 16

5️⃣ Practice Tasks:
✅ Find the second largest element in an array
✅ Implement sliding window to find max sum subarray
✅ Try variable-size window: longest substring without repeating characters

👇 Solution for Practice Tasks

✅ 1. Find the Second Largest Element in an Array

Python:

def second_largest(arr):
    first = second = float('-inf')
    for num in arr:
        if num > first:
            second = first
            first = num
        elif first > num > second:
            second = num
    return second if second != float('-inf') else None

print(second_largest([10, 20, 4, 45, 99]))  # Output: 45

✅ 2. Max Sum Subarray (Fixed-size Sliding Window)

Python:

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, 4, 2, 10, 2, 3, 1, 0, 20], 4))  # Output: 24

✅ 3. Longest Substring Without Repeating Characters (Variable-size Sliding Window)

Python:

def longest_unique_substring(s):
    seen = {}
    left = max_len = 0
    for right in range(len(s)):
        if s[right] in seen and seen[s[right]] >= left:
            left = seen[s[right]] + 1
        seen[s[right]] = right
        max_len = max(max_len, right - left + 1)
    return max_len

print(longest_unique_substring("abcabcbb"))  # Output: 3 ("abc")

Double Tap ♥️ For Part-4

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Date :- 11th January 2026

✅ DSA Part 4 – Strings: Patterns, Hashing & Two Pointers 🔤🧩⚡

Strings are everywhere—from passwords to DNA sequences. Mastering string manipulation unlocks powerful algorithms in pattern matching, text processing, and optimization.

1️⃣ What is a String?
A string is a sequence of characters. In most languages, strings are immutable and indexed like arrays.

Python Example:

s = "hello"
print(s[1])  # Output: 'e'

C++ Example:

string s = "hello";
cout << s[1];  // Output: 'e'

Java Example:

String s = "hello";
System.out.println(s.charAt(1));  // Output: 'e'

2️⃣ Common String Operations:
• Concatenation
• Substring
• Comparison
• Reversal
• Search
• Replace

Python – Reversal:

s = "hello"
print(s[::-1])  # Output: 'olleh'

C++ – Substring:

string s = "hello";
cout << s.substr(1, 3);  // Output: 'ell'

Java – Replace:

String s = "hello";
System.out.println(s.replace("l", "x"));  // Output: 'hexxo'

3️⃣ Pattern Matching – Naive vs Efficient
Naive Approach: Check every substring
Efficient: Use hashing or KMP (Knuth-Morris-Pratt)

Python – Naive Pattern Search:

def search(text, pattern):
    for i in range(len(text) - len(pattern) + 1):
        if text[i:i+len(pattern)] == pattern:
            print(f"Found at index {i}")

search("abracadabra", "abra")  # Output: Found at index 0, 7

4️⃣ Hashing for Fast Lookup
Use hash maps to store character counts, frequencies, or indices.

Python – First Unique Character:

from collections import Counter

def first_unique_char(s):
    count = Counter(s)
    for i, ch in enumerate(s):
        if count[ch] == 1:
            return i
    return -1

print(first_unique_char("leetcode"))  # Output: 0

5️⃣ Two Pointers Technique
Used for problems like palindromes, anagrams, or substring windows.

Python – Valid Palindrome:

def is_palindrome(s):
    s = ''.join(filter(str.isalnum, s)).lower()
    left, right = 0, len(s) - 1
    while left < right:
        if s[left] != s[right]:
            return False
        left += 1
        right -= 1
    return True

print(is_palindrome("A man, a plan, a canal: Panama"))  # Output: True

6️⃣ Practice Tasks:
✅ Implement pattern search (naive)
✅ Find first non-repeating character
✅ Check if a string is a palindrome
✅ Use two pointers to reverse vowels in a string
✅ Try Rabin-Karp or KMP for pattern matching

💬 Double Tap ❤️ for Part-5