Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
👍9❤1
Theoretical Questions for Coding Interviews on Basic Data Structures
1. What is a Data Structure?
A data structure is a way of organizing and storing data so that it can be accessed and modified efficiently. Common data structures include arrays, linked lists, stacks, queues, and trees.
2. What is an Array?
An array is a collection of elements, each identified by an index. It has a fixed size and stores elements of the same type in contiguous memory locations.
3. What is a Linked List?
A linked list is a linear data structure where elements (nodes) are stored non-contiguously. Each node contains a value and a reference (or link) to the next node. Unlike arrays, linked lists can grow dynamically.
4. What is a Stack?
A stack is a linear data structure that follows the Last In, First Out (LIFO) principle. The most recently added element is the first one to be removed. Common operations include push (add an element) and pop (remove an element).
5. What is a Queue?
A queue is a linear data structure that follows the First In, First Out (FIFO) principle. The first element added is the first one to be removed. Common operations include enqueue (add an element) and dequeue (remove an element).
6. What is a Binary Tree?
A binary tree is a hierarchical data structure where each node has at most two children, usually referred to as the left and right child. It is used for efficient searching and sorting.
7. What is the difference between an array and a linked list?
Array: Fixed size, elements stored in contiguous memory.
Linked List: Dynamic size, elements stored non-contiguously, each node points to the next.
8. What is the time complexity for accessing an element in an array vs. a linked list?
Array: O(1) for direct access by index.
Linked List: O(n) for access, as you must traverse the list from the start to find an element.
9. What is the time complexity for inserting or deleting an element in an array vs. a linked list?
Array:
Insertion/Deletion at the end: O(1).
Insertion/Deletion at the beginning or middle: O(n) because elements must be shifted.
Linked List:
Insertion/Deletion at the beginning: O(1).
Insertion/Deletion in the middle or end: O(n), as you need to traverse the list.
10. What is a HashMap (or Dictionary)?
A HashMap is a data structure that stores key-value pairs. It allows efficient lookups, insertions, and deletions using a hash function to map keys to values. Average time complexity for these operations is O(1).
Coding interview: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
1. What is a Data Structure?
A data structure is a way of organizing and storing data so that it can be accessed and modified efficiently. Common data structures include arrays, linked lists, stacks, queues, and trees.
2. What is an Array?
An array is a collection of elements, each identified by an index. It has a fixed size and stores elements of the same type in contiguous memory locations.
3. What is a Linked List?
A linked list is a linear data structure where elements (nodes) are stored non-contiguously. Each node contains a value and a reference (or link) to the next node. Unlike arrays, linked lists can grow dynamically.
4. What is a Stack?
A stack is a linear data structure that follows the Last In, First Out (LIFO) principle. The most recently added element is the first one to be removed. Common operations include push (add an element) and pop (remove an element).
5. What is a Queue?
A queue is a linear data structure that follows the First In, First Out (FIFO) principle. The first element added is the first one to be removed. Common operations include enqueue (add an element) and dequeue (remove an element).
6. What is a Binary Tree?
A binary tree is a hierarchical data structure where each node has at most two children, usually referred to as the left and right child. It is used for efficient searching and sorting.
7. What is the difference between an array and a linked list?
Array: Fixed size, elements stored in contiguous memory.
Linked List: Dynamic size, elements stored non-contiguously, each node points to the next.
8. What is the time complexity for accessing an element in an array vs. a linked list?
Array: O(1) for direct access by index.
Linked List: O(n) for access, as you must traverse the list from the start to find an element.
9. What is the time complexity for inserting or deleting an element in an array vs. a linked list?
Array:
Insertion/Deletion at the end: O(1).
Insertion/Deletion at the beginning or middle: O(n) because elements must be shifted.
Linked List:
Insertion/Deletion at the beginning: O(1).
Insertion/Deletion in the middle or end: O(n), as you need to traverse the list.
10. What is a HashMap (or Dictionary)?
A HashMap is a data structure that stores key-value pairs. It allows efficient lookups, insertions, and deletions using a hash function to map keys to values. Average time complexity for these operations is O(1).
Coding interview: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
👍5
Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
👍2❤1
Top 7 Must-Prepare Topics for Coding Interviews (2025 Edition)
✅ Arrays & Strings – Master problems on rotation, sliding window, two pointers, etc.
✅ Linked Lists – Practice reversal, cycle detection, and merging lists
✅ Hashing & Maps – Use hash tables for fast lookups and frequency-based problems
✅ Recursion & Backtracking – Solve problems like permutations, subsets, and Sudoku
✅ Dynamic Programming – Understand memoization, tabulation, and classic patterns
✅ Trees & Graphs – Cover traversal (BFS/DFS), shortest paths, and tree operations
✅ Stacks & Queues – Solve problems involving monotonic stacks, parentheses, and sliding windows
These are the essentials to crack FAANG-level interviews or product-based companies.
✅ Arrays & Strings – Master problems on rotation, sliding window, two pointers, etc.
✅ Linked Lists – Practice reversal, cycle detection, and merging lists
✅ Hashing & Maps – Use hash tables for fast lookups and frequency-based problems
✅ Recursion & Backtracking – Solve problems like permutations, subsets, and Sudoku
✅ Dynamic Programming – Understand memoization, tabulation, and classic patterns
✅ Trees & Graphs – Cover traversal (BFS/DFS), shortest paths, and tree operations
✅ Stacks & Queues – Solve problems involving monotonic stacks, parentheses, and sliding windows
These are the essentials to crack FAANG-level interviews or product-based companies.
👍4❤2
Coding Interview Resources
Top 7 Must-Prepare Topics for Coding Interviews (2025 Edition) ✅ Arrays & Strings – Master problems on rotation, sliding window, two pointers, etc. ✅ Linked Lists – Practice reversal, cycle detection, and merging lists ✅ Hashing & Maps – Use hash tables for…
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Tips for solving leetcode codings interview problems
If input array is sorted then
- Binary search
- Two pointers
If asked for all permutations/subsets then
- Backtracking
If given a tree then
- DFS
- BFS
If given a graph then
- DFS
- BFS
If given a linked list then
- Two pointers
If recursion is banned then
- Stack
If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer
If asked for maximum/minimum subarray/subset/options then
- Dynamic programming
If asked for top/least K items then
- Heap
If asked for common strings then
- Map
- Trie
Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space
If input array is sorted then
- Binary search
- Two pointers
If asked for all permutations/subsets then
- Backtracking
If given a tree then
- DFS
- BFS
If given a graph then
- DFS
- BFS
If given a linked list then
- Two pointers
If recursion is banned then
- Stack
If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer
If asked for maximum/minimum subarray/subset/options then
- Dynamic programming
If asked for top/least K items then
- Heap
If asked for common strings then
- Map
- Trie
Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space
❤5👍1
Let's understand how Arrays & Strings can be asked in coding interviews
Arrays and strings are the building blocks of most coding interview problems. They test your logic, optimization skills, and your ability to recognize patterns — and they pop up in everything from system design to algorithm rounds.
*1.1. Rotation*
You may be asked to rotate an array left or right by k positions, in-place and with O(1) space.
Example:
> Rotate [1, 2, 3, 4, 5] right by 2 → Output: [4, 5, 1, 2, 3]
It tests how well you manage array indices and edge cases like k > n.
*1.2. Sliding Window*
Used to reduce brute-force O(n²) solutions to O(n). Interviewers love this for problems around subarrays, substrings, or fixed windows.
*Example* :
> Find the max sum of a subarray of size 3 in [4, 2, 1, 7, 8, 1, 2, 8, 1, 0] → Output: 17
It's commonly used in anagram detection, maximum subarray sum, and longest substring without repeating characters.
*1.3. Two Pointers*
Two indices scanning the array — from start and end or moving in sync. Great for reducing space/time complexity.
Example:
> Given [1, 2, 4, 4] and target = 8, return true if two numbers sum up to target → Output: True (4+4)
Common interview problems:
- Reverse a string/array
- Check for palindrome
- Remove duplicates in-place
- Merge two sorted arrays
*1.4. Prefix Sum*
Precompute cumulative sums to answer range queries in O(1) instead of O(n).
Example:
> For nums = [1, 2, 3, 4, 5], find sum from index 1 to 3 quickly → Output: 9 (2+3+4)
*Popular problems:*
- Subarray sum equals k
- Range sum queries
- Balanced subarrays
React with ❤️ once you're ready for the next concept Linked Lists
Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720
Arrays and strings are the building blocks of most coding interview problems. They test your logic, optimization skills, and your ability to recognize patterns — and they pop up in everything from system design to algorithm rounds.
*1.1. Rotation*
You may be asked to rotate an array left or right by k positions, in-place and with O(1) space.
Example:
> Rotate [1, 2, 3, 4, 5] right by 2 → Output: [4, 5, 1, 2, 3]
It tests how well you manage array indices and edge cases like k > n.
*1.2. Sliding Window*
Used to reduce brute-force O(n²) solutions to O(n). Interviewers love this for problems around subarrays, substrings, or fixed windows.
*Example* :
> Find the max sum of a subarray of size 3 in [4, 2, 1, 7, 8, 1, 2, 8, 1, 0] → Output: 17
It's commonly used in anagram detection, maximum subarray sum, and longest substring without repeating characters.
*1.3. Two Pointers*
Two indices scanning the array — from start and end or moving in sync. Great for reducing space/time complexity.
Example:
> Given [1, 2, 4, 4] and target = 8, return true if two numbers sum up to target → Output: True (4+4)
Common interview problems:
- Reverse a string/array
- Check for palindrome
- Remove duplicates in-place
- Merge two sorted arrays
*1.4. Prefix Sum*
Precompute cumulative sums to answer range queries in O(1) instead of O(n).
Example:
> For nums = [1, 2, 3, 4, 5], find sum from index 1 to 3 quickly → Output: 9 (2+3+4)
*Popular problems:*
- Subarray sum equals k
- Range sum queries
- Balanced subarrays
React with ❤️ once you're ready for the next concept Linked Lists
Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720
👍5❤3
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Coding Interview Resources
Let's understand how Arrays & Strings can be asked in coding interviews Arrays and strings are the building blocks of most coding interview problems. They test your logic, optimization skills, and your ability to recognize patterns — and they pop up in everything…
Let's now move to next important concept asked in coding interviews: Linked Lists:
Linked Lists test your ability to handle pointers, edge cases, and memory efficiency. They show up in both beginner and advanced interview rounds.
2.1. Reverse a Linked List
Example:
Reverse this list:
1 → 2 → 3 → 4
Output: 4 → 3 → 2 → 1
Concept tested:
Rewiring the .next pointers — often asked with follow-ups like iterative vs. recursive solutions.
2.2. Detect Cycle in a Linked List
Example:
In 1 → 2 → 3 → 4 → 2 (back to second node), detect the cycle.
Solution:
Use Floyd’s Cycle Detection Algorithm (fast and slow pointers).
It tests how well you manage infinite loops and pointer traversal without modifying the list.
2.3. Merge Two Sorted Linked Lists
Example:
Merge 1 → 3 → 5 and 2 → 4 → 6
Output: 1 → 2 → 3 → 4 → 5 → 6
Concept tested:
Efficient pointer traversal with dummy nodes or recursion. A classic sub-task in linked list sorting.
2.4. Find the Middle of a Linked List
Example:
In 1 → 2 → 3 → 4 → 5 → 6, the middle node is 4.
Solution:
Fast and slow pointer — when the fast pointer reaches the end, the slow one is at the middle.
2.5. Remove N-th Node from End
Example:
Remove the 2nd node from the end of 1 → 2 → 3 → 4 → 5
Output: 1 → 2 → 3 → 5
Trick:
Create a gap of n between two pointers and move them together — when the first hits the end, the second is at the right spot.
You’ll see linked lists hidden in many real-world structures — like undo-redo functionality, LRU cache, or browser history stacks.
React with ❤️ once you're ready for the next concept Hashing & Maps
Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720
ENJOY LEARNING 👍👍
Linked Lists test your ability to handle pointers, edge cases, and memory efficiency. They show up in both beginner and advanced interview rounds.
2.1. Reverse a Linked List
Example:
Reverse this list:
1 → 2 → 3 → 4
Output: 4 → 3 → 2 → 1
Concept tested:
Rewiring the .next pointers — often asked with follow-ups like iterative vs. recursive solutions.
2.2. Detect Cycle in a Linked List
Example:
In 1 → 2 → 3 → 4 → 2 (back to second node), detect the cycle.
Solution:
Use Floyd’s Cycle Detection Algorithm (fast and slow pointers).
It tests how well you manage infinite loops and pointer traversal without modifying the list.
2.3. Merge Two Sorted Linked Lists
Example:
Merge 1 → 3 → 5 and 2 → 4 → 6
Output: 1 → 2 → 3 → 4 → 5 → 6
Concept tested:
Efficient pointer traversal with dummy nodes or recursion. A classic sub-task in linked list sorting.
2.4. Find the Middle of a Linked List
Example:
In 1 → 2 → 3 → 4 → 5 → 6, the middle node is 4.
Solution:
Fast and slow pointer — when the fast pointer reaches the end, the slow one is at the middle.
2.5. Remove N-th Node from End
Example:
Remove the 2nd node from the end of 1 → 2 → 3 → 4 → 5
Output: 1 → 2 → 3 → 5
Trick:
Create a gap of n between two pointers and move them together — when the first hits the end, the second is at the right spot.
You’ll see linked lists hidden in many real-world structures — like undo-redo functionality, LRU cache, or browser history stacks.
React with ❤️ once you're ready for the next concept Hashing & Maps
Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720
ENJOY LEARNING 👍👍
❤4
How to get job as python fresher?
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
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📩 Correct Way to Mail a Resume
📩 Message to a Recruiter After Seeing Their Job Posting
✉️ Warm Networking DM
Subject: Application For The [Role] at [Company Name]
Dear [Hiring Manager’s Name],
I hope you’re doing great. I came across the [Position Title] role at [Company Name] and was really excited about the opportunity to apply. With my experience in [mention key relevant experience], I believe I could bring value to your team.
I’ve attached my Resume for your review. I trust my background aligns with what you’re looking for, I’d love the chance to discuss how I can contribute to your team. Looking forward to hearing your thoughts!
Best regards,
[Your Name]
[Link To Linkedin]
[Link To Resume]
📩 Message to a Recruiter After Seeing Their Job Posting
Subject: Excited to Apply for [Position Title] at [Company Name]
Hi [Recruiter’s Name],
I trust you have a awesome day today 🙂
I just saw your post about the [Position Title] opening at [Company Name], and I couldn’t wait to reach out! I’ve been following [Company Name]
for a while now, and I truly admire [mention something specific—company’s projects, culture, values, recent achievements].
With my expertise in [mention relevant skills/experience], I believe I’d be a great fit for this role. I’ve attached my Resume for your review, and I’d love the chance to discuss how my experience can contribute to your team.
Would you be open to a quick chat?
Looking forward to your thoughts!
[Your Resume]
✉️ Warm Networking DM
Subject: Exploring Opportunities at [Company Name]
Hi [First Name],
I believe you have a wonderful day today 😊
I’m a [Your Role] specializing in [mention key skills]. I’ve been following [Company Name] for a while and love [mention something specific about their work, culture, or achievements].
With experience in [mention a key project or skill], I believe I could bring value to your team. If you’re open to it, I’d love to chat about any opportunities, where my skills could be a great fit.
I know you must get a ton of messages, so I really appreciate your time. Looking forward to hearing from you!
Warm,
[Your Name]
[Your Resume]
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Coding Interview Resources
Let's now move to next important concept asked in coding interviews: Linked Lists: Linked Lists test your ability to handle pointers, edge cases, and memory efficiency. They show up in both beginner and advanced interview rounds. 2.1. Reverse a Linked List…
Let’s dive into the frequently asked coding interview topic now: Hashing & Maps
Hashing helps us access data quickly, making it a critical topic in coding interviews. HashMaps (or dictionaries) provide constant-time access and are used to solve problems like counting elements, finding duplicates, and mapping data efficiently.
3.1. Use Hash Maps for Fast Lookups
Example:
Check if two strings are anagrams.
Given s1 = "listen", s2 = "silent", are they anagrams?
Solution:
Use a hash map to count the frequency of characters in both strings and compare the counts.
Concept tested:
Efficient searching and counting with O(1) average time complexity.
3.2. Count Frequency of Elements
Example:
Given an array nums = [1, 2, 2, 3, 3, 3, 4], count the frequency of each element.
Solution:
Use a hash map to store the counts:
{1: 1, 2: 2, 3: 3, 4: 1}
It tests how well you can group data efficiently and manage it using hash-based structures.
3.3. Find Duplicates
Example:
Given arr = [4, 5, 6, 7, 5, 8], find the first duplicate.
Solution:
Use a hash map to track seen elements as you traverse the array. If you encounter an element already in the map, it’s a duplicate.
It tests your ability to solve problems with constant time lookups for duplicates.
3.4. Two Sum Problem
Example:
Given nums = [2, 7, 11, 15] and target = 9, return indices of the two numbers that add up to the target.
Solution:
Use a hash map to track the difference between the target and the current number. When you find a match, return the indices.
Concept tested:
Efficient search for pairs in a single pass with O(n) time complexity.
3.5. Implement LRU Cache
Example:
Design a cache that stores the most recently used items, evicting the least recently used when it exceeds its capacity.
Solution:
Use a hash map to store the cache and a doubly linked list to keep track of the order of usage. Combine both to make retrieval and eviction O(1).
This tests your ability to combine hash maps with other data structures like linked lists, and to implement efficient solutions with constraints.
Hash maps are frequently used in problems involving counting, grouping, and mapping, especially when you need to reduce time complexity from quadratic to linear.
React with ❤️ once you're ready for the next topic: Recursion & Backtracking
Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720
Top 7 Python Concepts: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1452
ENJOY LEARNING 👍👍
Hashing helps us access data quickly, making it a critical topic in coding interviews. HashMaps (or dictionaries) provide constant-time access and are used to solve problems like counting elements, finding duplicates, and mapping data efficiently.
3.1. Use Hash Maps for Fast Lookups
Example:
Check if two strings are anagrams.
Given s1 = "listen", s2 = "silent", are they anagrams?
Solution:
Use a hash map to count the frequency of characters in both strings and compare the counts.
Concept tested:
Efficient searching and counting with O(1) average time complexity.
3.2. Count Frequency of Elements
Example:
Given an array nums = [1, 2, 2, 3, 3, 3, 4], count the frequency of each element.
Solution:
Use a hash map to store the counts:
{1: 1, 2: 2, 3: 3, 4: 1}
It tests how well you can group data efficiently and manage it using hash-based structures.
3.3. Find Duplicates
Example:
Given arr = [4, 5, 6, 7, 5, 8], find the first duplicate.
Solution:
Use a hash map to track seen elements as you traverse the array. If you encounter an element already in the map, it’s a duplicate.
It tests your ability to solve problems with constant time lookups for duplicates.
3.4. Two Sum Problem
Example:
Given nums = [2, 7, 11, 15] and target = 9, return indices of the two numbers that add up to the target.
Solution:
Use a hash map to track the difference between the target and the current number. When you find a match, return the indices.
Concept tested:
Efficient search for pairs in a single pass with O(n) time complexity.
3.5. Implement LRU Cache
Example:
Design a cache that stores the most recently used items, evicting the least recently used when it exceeds its capacity.
Solution:
Use a hash map to store the cache and a doubly linked list to keep track of the order of usage. Combine both to make retrieval and eviction O(1).
This tests your ability to combine hash maps with other data structures like linked lists, and to implement efficient solutions with constraints.
Hash maps are frequently used in problems involving counting, grouping, and mapping, especially when you need to reduce time complexity from quadratic to linear.
React with ❤️ once you're ready for the next topic: Recursion & Backtracking
Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720
Top 7 Python Concepts: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1452
ENJOY LEARNING 👍👍
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General tips for coding interviews
Always validate input first. Check for inputs that are invalid, empty, negative, or different. Never assume you are given the valid parameters. Alternatively, clarify with the interviewer whether you can assume valid input (usually yes), which can save you time from writing code that does input validation.
Are there any time and space complexities requirements or constraints?
Check for off-by-one errors.
In languages where there are no automatic type coercion, check that concatenation of values are of the same type: int,str, and list.
After you finish your code, use a few example inputs to test your solution.
Is the algorithm supposed to run multiple times, perhaps on a web server? If yes, the input can likely be pre-processed to improve the efficiency in each API call.
Use a mix of functional and imperative programming paradigms:
🔹 Write pure functions as often as possible.
🔹 Use pure functions because they are easier to reason with and can help reduce bugs in your implementation.
🔹 Avoid mutating the parameters passed into your function, especially if they are passed by reference, unless you are sure of what you are doing.
🔹 Achieve a balance between accuracy and efficiency. Use the right amount of functional and imperative code where appropriate. Functional programming is usually expensive in terms of space complexity because of non-mutation and the repeated allocation of new objects. On the other hand, imperative code is faster because you operate on existing objects.
🔹 Avoid relying on mutating global variables. Global variables introduce state.
🔹 Make sure that you do not accidentally mutate global variables, especially if you have to rely on them.
Always validate input first. Check for inputs that are invalid, empty, negative, or different. Never assume you are given the valid parameters. Alternatively, clarify with the interviewer whether you can assume valid input (usually yes), which can save you time from writing code that does input validation.
Are there any time and space complexities requirements or constraints?
Check for off-by-one errors.
In languages where there are no automatic type coercion, check that concatenation of values are of the same type: int,str, and list.
After you finish your code, use a few example inputs to test your solution.
Is the algorithm supposed to run multiple times, perhaps on a web server? If yes, the input can likely be pre-processed to improve the efficiency in each API call.
Use a mix of functional and imperative programming paradigms:
🔹 Write pure functions as often as possible.
🔹 Use pure functions because they are easier to reason with and can help reduce bugs in your implementation.
🔹 Avoid mutating the parameters passed into your function, especially if they are passed by reference, unless you are sure of what you are doing.
🔹 Achieve a balance between accuracy and efficiency. Use the right amount of functional and imperative code where appropriate. Functional programming is usually expensive in terms of space complexity because of non-mutation and the repeated allocation of new objects. On the other hand, imperative code is faster because you operate on existing objects.
🔹 Avoid relying on mutating global variables. Global variables introduce state.
🔹 Make sure that you do not accidentally mutate global variables, especially if you have to rely on them.
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