🚀 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 😍

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- Tech & Non-Tech Career Path
- Interview Preparation Tips
- Live Q&A

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https://pdlink.in/3Ltb3CE

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

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗯𝘆 𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲😍

Deadline: 11th January 2026

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

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Only Limited Seats Available!

✅ 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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https://pdlink.in/4sw5Ev8

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

𝗛𝗶𝗴𝗵 𝗗𝗲𝗺𝗮𝗻𝗱𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗪𝗶𝘁𝗵 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲😍

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✅ DSA Part 5 – Linked Lists: Single, Double & Reverse 🔁🔗📚

Linked Lists are dynamic data structures ideal for scenarios requiring frequent insertions and deletions. Unlike arrays, they don’t need contiguous memory and offer flexible memory usage.

1️⃣ What is a Linked List?
A Linked List is a linear data structure where each element (node) contains:
- Data
- Pointer to the next node (and optionally the previous node)

Types:
- Singly Linked List: Each node points to the next
- Doubly Linked List: Nodes point to both next and previous
- Circular Linked List: Last node points back to the head

2️⃣ Singly Linked List – Basic Structure

Python

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

Java

class Node {
    int data;
    Node next;
    Node(int data) {
        this.data = data;
        this.next = null;
    }
}

C++

struct Node {
    int data;
    Node* next;
    Node(int data): data(data), next(nullptr) {}
};

3️⃣ Insert at Head (Singly)

Python

def insert_head(head, data):
    new_node = Node(data)
    new_node.next = head
    return new_node

Java

Node insertHead(Node head, int data) {
    Node newNode = new Node(data);
    newNode.next = head;
    return newNode;
}

C++

Node* insertHead(Node* head, int data) {
    Node* newNode = new Node(data);
    newNode->next = head;
    return newNode;
}

4️⃣ Doubly Linked List – Bi-directional Pointers

Python

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

Java

class DNode {
    int data;
    DNode prev, next;
    DNode(int data) {
        this.data = data;
    }
}

C++

struct DNode {
    int data;
    DNode* prev;
    DNode* next;
    DNode(int data): data(data), prev(nullptr), next(nullptr) {}
};

5️⃣ Insert at Head (Doubly)

Python

def insert_head(head, data):
    new_node = DNode(data)
    new_node.next = head
    if head:
        head.prev = new_node
    return new_node

Java

DNode insertHead(DNode head, int data) {
    DNode newNode = new DNode(data);
    newNode.next = head;
    if (head != null) head.prev = newNode;
    return newNode;
}

C++

DNode* insertHead(DNode* head, int data) {
    DNode* newNode = new DNode(data);
    newNode->next = head;
    if (head) head->prev = newNode;
    return newNode;
}

6️⃣ Reversing a Singly Linked List

Python

def reverse_list(head):
    prev = None
    current = head
    while current:
        next_node = current.next
        current.next = prev
        prev = current
        current = next_node
    return prev

Java

Node reverseList(Node head) {
    Node prev = null, current = head;
    while (current != null) {
        Node next = current.next;
        current.next = prev;
        prev = current;
        current = next;
    }
    return prev;
}

C++

Node* reverseList(Node* head) {
    Node* prev = nullptr;
    Node* current = head;
    while (current) {
        Node* next = current->next;
        current->next = prev;
        prev = current;
        current = next;
    }
    return prev;
}

7️⃣ Why Use Linked Lists?
✅ Dynamic memory allocation
✅ Efficient insert/delete (O(1) at head/tail)
❌ Slower access (O(n) for random access)
✅ Great for implementing stacks, queues, hash maps, etc.

8️⃣ Practice Tasks
✅ Implement singly linked list with insert/delete
✅ Implement doubly linked list with insert at tail
✅ Reverse a singly linked list