Top 10 Must-Know Coding Concepts every interviewer expects you to know.

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*1. Arrays & Strings – The Basics That Build Everything*

Arrays are ordered collections. Strings are just arrays of characters.

You’ll use them in 90% of coding problems.

Beginner Example: Find the max number in an array, reverse a string, check if it’s a palindrome.

Start with: Leetcode Easy Array Problems


*2. Hashing – Remember Stuff Fast*

What it is: Like a super-efficient locker room. You store and find things instantly using keys.

Real Use-case: Count frequencies, detect duplicates, group similar data.

Example: Check if two strings are anagrams.

Use: HashMap or Dictionary in Python


*3. Recursion – When Functions Call Themselves*

What it is: A function solving a smaller version of the same problem.

Looks Scary? It’s not. Think of solving a puzzle by solving one piece at a time.

Example: Factorial, Fibonacci numbers.

Golden Rule: Always define a base case, or it loops forever!


*4. Backtracking – Trial & Error, Smartly Done*

What it is: Try all possible options, but drop paths that don’t work early.

Real World Analogy: Like navigating a maze – go back if you hit a wall.

Example: Sudoku Solver, N-Queens Problem


*5. Dynamic Programming (DP) – Avoid Repeating Work*

What it is: Break problems into smaller parts and store the result so you don’t repeat it.

Example: Fibonacci using DP instead of recursion (faster!)


*6. Sliding Window – Efficient Way to Check Patterns in a Row*

What it is: Instead of checking every combination, move a “window” across the array to find answers.

Example: Find max sum of subarray of size K.

Great for string and array problems.


*7. Trees – Hierarchical Data You Must Understand*

What it is: Like a family tree. Each node can have children.

Key Terms: Root, Leaf, Binary Tree, BST

Why it’s asked: Real apps like file systems, websites use trees.

Example: Inorder/Preorder/Postorder Traversals


*8. Graphs – Networks of Connections*

What it is: Nodes connected by edges. Can go in any direction.

Examples: Maps, social media friends, recommendation engines

Learn: BFS (Breadth-First Search), DFS (Depth-First Search)


*9. Greedy – Pick Best at Every Step (Fast but Risky)*

What it is: Make the best local choice hoping it leads to the global best.

Good For Simple optimization problems

Example: Activity Selection, Coin Change (with greedy strategy)


*10. Bit Manipulation – Play with 0s and 1s*

What it is: Perform operations directly on binary representations. It’s super fast and memory-efficient

Example: Check if a number is a power of 2, find the only non-repeating element


What to Do Next (Action Plan):

- Start with Arrays, then move to Hashing

- Try Recursion + Backtracking next

- Once comfy, go into DP, Graphs, and Trees

- Use platforms like Leetcode (easy → medium), GeeksforGeeks, or Neetcode

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Here are 10 popular programming languages based on versatile, widely-used, and in-demand languages:
1. Python – Ideal for beginners and professionals; used in web development, data analysis, AI, and more.

2. Java – A classic language for building enterprise applications, Android apps, and large-scale systems.

3. C – The foundation for many other languages; great for understanding low-level programming concepts.

4. C++ – Popular for game development, competitive programming, and performance-critical applications.

5. C# – Widely used for Windows applications, game development (Unity), and enterprise software.

6. Go (Golang) – A modern language designed for performance and scalability, popular in cloud services.

7. Rust – Known for its safety and performance, ideal for system-level programming.

8. Kotlin – The preferred language for Android development with modern features.

9. Swift – Used for developing iOS and macOS applications with simplicity and power.

10. PHP – A staple for web development, powering many websites and applications

When to Use Which Programming Language?

C ➝ OS Development, Embedded Systems, Game Engines
C++ ➝ Game Dev, High-Performance Apps, Finance
Java ➝ Enterprise Apps, Android, Backend
C# ➝ Unity Games, Windows Apps
Python ➝ AI/ML, Data, Automation, Web Dev
JavaScript ➝ Frontend, Full-Stack, Web Games
Golang ➝ Cloud Services, APIs, Networking
Swift ➝ iOS/macOS Apps
Kotlin ➝ Android, Backend
PHP ➝ Web Dev (WordPress, Laravel)
Ruby ➝ Web Dev (Rails), Prototypes
Rust ➝ System Apps, Blockchain, HPC
Lua ➝ Game Scripting (Roblox, WoW)
R ➝ Stats, Data Science, Bioinformatics
SQL ➝ Data Analysis, DB Management
TypeScript ➝ Scalable Web Apps
Node.js ➝ Backend, Real-Time Apps
React ➝ Modern Web UIs
Vue ➝ Lightweight SPAs
Django ➝ AI/ML Backend, Web Dev
Laravel ➝ Full-Stack PHP
Blazor ➝ Web with .NET
Spring Boot ➝ Microservices, Java Enterprise
Ruby on Rails ➝ MVPs, Startups
HTML/CSS ➝ UI/UX, Web Design
Git ➝ Version Control
Linux ➝ Server, Security, DevOps
DevOps ➝ Infra Automation, CI/CD
CI/CD ➝ Testing + Deployment
Docker ➝ Containerization
Kubernetes ➝ Cloud Orchestration
Microservices ➝ Scalable Backends
Selenium ➝ Web Testing
Playwright ➝ Modern Web Automation

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ENJOY LEARNING 👍👍

𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝘀𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁

𝟭. 𝗮𝗽𝗽𝗲𝗻𝗱( ) – Adds an element to the end of the list.
𝟮. 𝗰𝗼𝘂𝗻𝘁( ) – Returns the number of occurrences of a specific element.
𝟯. 𝗰𝗼𝗽𝘆( ) – Creates a duplicate of the list.
𝟰. 𝗶𝗻𝗱𝗲𝘅( ) – Returns the position of the first occurrence of an element.
𝟱. 𝗶𝗻𝘀𝗲𝗿𝘁(𝟭, ) – Inserts an element at a specified index.
𝟲. 𝗿𝗲𝘃𝗲𝗿𝘀𝗲( ) – Reverses the order of elements in the list.
𝟳. 𝗽𝗼𝗽( ) – Removes and returns the last element.
𝟴. 𝗰𝗹𝗲𝗮𝗿( ) – Removes all elements from the list.
𝟵. 𝗽𝗼𝗽(𝟭) – Removes and returns the element at index 1.

Master these list methods to handle Python lists efficiently! 🚀

Top 10 Python Concepts
Variables & Data Types

Understand integers, floats, strings, booleans, lists, tuples, sets, and dictionaries.

Control Flow (if, else, elif)
Write logic-based programs using conditional statements.

Loops (for & while)
Automate tasks and iterate over data efficiently.

Functions
Build reusable code blocks with def, understand parameters, return values, and scope.

List Comprehensions
Create and transform lists concisely:
[x*2 for x in range(10) if x % 2 == 0]

Modules & Packages
Import built-in, third-party, or custom modules to structure your code.

Exception Handling
Handle errors using try, except, finally for robust programs.

Object-Oriented Programming (OOP)
Learn classes, objects, inheritance, encapsulation, and polymorphism.

File Handling
Open, read, write, and manage files using open(), read(), write().

Working with Libraries
Use powerful libraries like:
- NumPy for numerical operations
- Pandas for data analysis
- Matplotlib/Seaborn for visualization
- Requests for API calls
- JSON for data parsing

#python

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1.Core:
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• git commit
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• git diff
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• git show
• git tag
• git push
• git pull

2.Branching:
• git branch
• git checkout -b
• git merge
• git rebase
• git branch --set-upstream-to
• git branch --unset-upstream
• git cherry-pick

3.Merging:
• git merge
• git rebase

4.Stashing:
• git stash
• git stash pop
• git stash list
• git stash apply
• git stash drop

5.Remotes:
• git remote
• git remote add
• git remote remove
• git fetch
• git pull
• git push
• git clone --mirror

6.Configuration:
• git config
• git global config
• git reset config

7. Plumbing:
• git cat-file
• git checkout-index
• git commit-tree
• git diff-tree
• git for-each-ref
• git hash-object
• git ls-files
• git ls-remote
• git merge-tree
• git read-tree
• git rev-parse
• git show-branch
• git show-ref
• git symbolic-ref
• git tag --list
• git update-ref

8.Porcelain:
• git blame
• git bisect
• git checkout
• git commit
• git diff
• git fetch
• git grep
• git log
• git merge
• git push
• git rebase
• git reset
• git show
• git tag

9.Alias:
• git config --global alias.<alias> <command>

10.Hook:
• git config --local core.hooksPath <path>

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✅Meta interview questions : Most asked in last 30 days

1. 1249. Minimum Remove to Make Valid Parentheses

2. 408. Valid Word Abbreviation

3. 215. Kth Largest Element in an Array

4. 314. Binary Tree Vertical Order Traversal

5. 88. Merge Sorted Array

6. 339. Nested List Weight Sum

7. 680. Valid Palindrome II

8. 973. K Closest Points to Origin

9. 1650. Lowest Common Ancestor of a Binary Tree III

10. 1. Two Sum

11. 791. Custom Sort String

12. 56. Merge Intervals

13. 528. Random Pick with Weight

14. 1570. Dot Product of Two Sparse Vectors

15. 50. Pow(x, n)

16. 65. Valid Number

17. 227. Basic Calculator II

18. 560. Subarray Sum Equals K

19. 71. Simplify Path

20. 200. Number of Islands

21. 236. Lowest Common Ancestor of a Binary Tree

22. 347. Top K Frequent Elements

23. 498. Diagonal Traverse

24. 543. Diameter of Binary Tree

25. 1768. Merge Strings Alternately

26. 2. Add Two Numbers

27. 4. Median of Two Sorted Arrays

28. 7. Reverse Integer

29. 31. Next Permutation

30. 34. Find First and Last Position of Element in Sorted Array

31. 84. Largest Rectangle in Histogram

32. 146. LRU Cache

33. 162. Find Peak Element

34. 199. Binary Tree Right Side View

35. 938. Range Sum of BST

36. 17. Letter Combinations of a Phone Number
37. 125. Valid Palindrome

38. 153. Find Minimum in Rotated Sorted Array

39. 283. Move Zeroes

40. 523. Continuous Subarray Sum

41. 658. Find K Closest Elements

42. 670. Maximum Swap

43. 827. Making A Large Island

44. 987. Vertical Order Traversal of a Binary Tree

45. 1757. Recyclable and Low Fat Products

46. 1762. Buildings With an Ocean View

47. 2667. Create Hello World Function

48. 5. Longest Palindromic Substring

49. 15. 3Sum

50. 19. Remove Nth Node From End of List

51. 70. Climbing Stairs

52. 80. Remove Duplicates from Sorted Array II

53. 113. Path Sum II

54. 121. Best Time to Buy and Sell Stock

55. 127. Word Ladder

56. 128. Longest Consecutive Sequence

57. 133. Clone Graph

58. 138. Copy List with Random Pointer

59. 140. Word Break II

60. 142. Linked List Cycle II

61. 145. Binary Tree Postorder Traversal

62. 173. Binary Search Tree Iterator

63. 206. Reverse Linked List

64. 207. Course Schedule

65. 394. Decode String

66. 415. Add Strings

67. 437. Path Sum III

68. 468. Validate IP Address

70. 691. Stickers to Spell Word

71. 725. Split Linked List in Parts

72. 766. Toeplitz Matrix

73. 708. Insert into a Sorted Circular Linked List

74. 1091. Shortest Path in Binary Matrix

75. 1514. Path with Maximum Probability

76. 1609. Even Odd Tree

77. 1868. Product of Two Run-Length Encoded Arrays

78. 2022. Convert 1D Array Into 2D Array

DSA Interview Preparation Resources: https://topmate.io/coding/886874

ENJOY LEARNING 👍👍

Beginner’s Roadmap to Learn Data Structures & Algorithms

1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.

2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.

3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.

4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.

5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.

6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.

7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.

8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.

9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.

10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.

Best DSA RESOURCES: https://topmate.io/coding/886874

All the best 👍👍

Guys, Big Announcement!

We’ve officially crossed 4 Lakh followers on this journey together — and it’s time to step up now! ❤️

I’m launching a Coding Interview Prep Series — designed for everyone from beginners to those polishing their skills for FAANG-level interviews.

This will be a structured, step-by-step journey — with short explanations, real coding examples, and mini-challenges after every topic to build real muscle memory.

Here’s what’s coming in the next few weeks:

Week 1: The Very Basics

- What is an Algorithm?

- What is Data Structure?

- Understanding Time Complexity (Big O Notation - made simple!)

- Basic Math for Coding Interviews

- Problem Solving Approach (How to break down a question)


Week 2: Arrays & Strings — Your Building Blocks

- Introduction to Arrays and Strings

- Common Operations (Insert, Delete, Search)

- Two Pointer Techniques (Easy to Medium problems)

- Sliding Window Problems (Optimization techniques)

- String Manipulation Tricks for Interviews


Week 3: Hashing & Recursion

- HashMaps and HashSets (Power tools for coders!)

- Solving Problems using Hashing

- Introduction to Recursion

- Base Case and Recursive Case (Explained like a 5-year-old)

- Classic Recursion Problems


Week 4: Linked Lists, Stacks & Queues

- Singly vs Doubly Linked List

- Stack Operations and Problems (Valid Parentheses, Min Stack)

- Queue and Deque Concepts (with real examples)

- When to Use Stack vs Queue in Interviews


Week 5: Trees & Graphs Essentials

- Binary Trees and BST Basics

- Tree Traversals (Inorder, Preorder, Postorder)

- Graph Representations (Adjacency List, Matrix)

- Breadth-First Search (BFS) and Depth-First Search (DFS) explained simply


Week 6: Sorting, Searching & Interview Patterns

- Core Sorting Algorithms (Selection, Bubble, Insertion)

- Advanced Sorting (Merge Sort, Quick Sort)

- Binary Search Patterns (Find First, Last Occurrence, etc.)

- Mastering Interview Patterns (Two Sum, Three Sum, Subarray Sum, etc.)


Week 7: Dynamic Programming & Advanced Problem Solving

- What is Dynamic Programming (DP)?

- Top-Down vs Bottom-Up Approach

- Memoization and Tabulation Explained

- Classic DP Problems (Fibonacci, 0/1 Knapsack, Longest Subsequence)


Week 8: Real-World Mock Interviews

- Solving Medium to Hard Problems

- Tackling FAANG-level Interview Questions

- Tips to Handle Pressure in Coding Rounds

- Building the Right Mindset for Success


React with ❤️ if you're ready for this new coding series


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