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 👍👍
❤2👍1
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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Javascript is everywhere. Millions of webpages are built on JS.
Let’s discuss some of the basic concept of javascript which are important to learn for any Javascript developer.
1 Scope
2 Hoisting
3 Closures
4 Callbacks
5 Promises
6 Async & Await
Let’s discuss some of the basic concept of javascript which are important to learn for any Javascript developer.
1 Scope
2 Hoisting
3 Closures
4 Callbacks
5 Promises
6 Async & Await
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Top 10 Must-Know Coding Concepts every interviewer expects you to know.
Save this. Share this. 👇
*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
If this helped, drop a ❤️ and share with your coding gang.
Programming Resources: 👇 https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
Save this. Share this. 👇
*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
If this helped, drop a ❤️ and share with your coding gang.
Programming Resources: 👇 https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
👍4
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
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
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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
Credits: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
ENJOY LEARNING 👍👍
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
Credits: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
ENJOY LEARNING 👍👍
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