✅ Coding Interview Questions with Answers [Part-2] 💻🚀
11. What is a sliding window algorithm?
A technique for solving problems involving arrays or strings by maintaining a window that slides over data. It helps reduce time complexity by avoiding nested loops.
Example: Finding the max sum of subarrays of size k.
12. Detect cycle in a linked list.
Use Floyd's Cycle Detection Algorithm (Tortoise and Hare).
⦁ Move two pointers at different speeds.
⦁ If they meet, a cycle exists.
⦁ To find the cycle start, reset one pointer to head and move both one step until they meet again.
13. Find the intersection of two arrays.
Use a HashSet to store elements of the first array, then check each element in the second array.
⦁ Time: O(n + m)
⦁ Space: O(min(n, m))
14. Reverse a string or linked list.
⦁ For a string: Use two-pointer swap or Python's slicing.
⦁ For a linked list: Use three pointers (prev, curr, next) and iterate while reversing links.
15. Check if a string is a palindrome.
Use two pointers from start and end, compare characters.
Return false if mismatch, true if all characters match.
16. What are the different sorting algorithms?
⦁ Bubble Sort
⦁ Selection Sort
⦁ Insertion Sort
⦁ Merge Sort
⦁ Quick Sort
⦁ Heap Sort
⦁ Radix Sort
Each has different time and space complexities.
17. Explain quicksort vs. mergesort.
⦁ Quicksort: Divide and conquer, picks a pivot.
⦁ Average: O(n log n), Worst: O(n²), Space: O(log n)
⦁ Mergesort: Always divides array into halves, then merges.
⦁ Time: O(n log n), Space: O(n), Stable sort
18. What is a binary search tree (BST)?
A tree where left child < node < right child.
⦁ Efficient for searching, insertion, deletion: O(log n) if balanced.
⦁ Unbalanced BST can degrade to O(n)
19. Inorder, Preorder, Postorder traversals.
⦁ Inorder (LNR): Sorted order in BST
⦁ Preorder (NLR): Used to copy or serialize tree
⦁ Postorder (LRN): Used to delete tree
20. Implement LRU Cache.
Use a combination of HashMap + Doubly Linked List.
⦁ HashMap stores key-node pairs.
⦁ Linked list maintains access order.
⦁ When cache is full, remove the least recently used node.
Operations (get, put): O(1) time.
💬 Double Tap ♥️ For Part-3!
11. What is a sliding window algorithm?
A technique for solving problems involving arrays or strings by maintaining a window that slides over data. It helps reduce time complexity by avoiding nested loops.
Example: Finding the max sum of subarrays of size k.
12. Detect cycle in a linked list.
Use Floyd's Cycle Detection Algorithm (Tortoise and Hare).
⦁ Move two pointers at different speeds.
⦁ If they meet, a cycle exists.
⦁ To find the cycle start, reset one pointer to head and move both one step until they meet again.
13. Find the intersection of two arrays.
Use a HashSet to store elements of the first array, then check each element in the second array.
⦁ Time: O(n + m)
⦁ Space: O(min(n, m))
14. Reverse a string or linked list.
⦁ For a string: Use two-pointer swap or Python's slicing.
⦁ For a linked list: Use three pointers (prev, curr, next) and iterate while reversing links.
15. Check if a string is a palindrome.
Use two pointers from start and end, compare characters.
Return false if mismatch, true if all characters match.
16. What are the different sorting algorithms?
⦁ Bubble Sort
⦁ Selection Sort
⦁ Insertion Sort
⦁ Merge Sort
⦁ Quick Sort
⦁ Heap Sort
⦁ Radix Sort
Each has different time and space complexities.
17. Explain quicksort vs. mergesort.
⦁ Quicksort: Divide and conquer, picks a pivot.
⦁ Average: O(n log n), Worst: O(n²), Space: O(log n)
⦁ Mergesort: Always divides array into halves, then merges.
⦁ Time: O(n log n), Space: O(n), Stable sort
18. What is a binary search tree (BST)?
A tree where left child < node < right child.
⦁ Efficient for searching, insertion, deletion: O(log n) if balanced.
⦁ Unbalanced BST can degrade to O(n)
19. Inorder, Preorder, Postorder traversals.
⦁ Inorder (LNR): Sorted order in BST
⦁ Preorder (NLR): Used to copy or serialize tree
⦁ Postorder (LRN): Used to delete tree
20. Implement LRU Cache.
Use a combination of HashMap + Doubly Linked List.
⦁ HashMap stores key-node pairs.
⦁ Linked list maintains access order.
⦁ When cache is full, remove the least recently used node.
Operations (get, put): O(1) time.
💬 Double Tap ♥️ For Part-3!
❤6
✅ Coding Interview Questions with Answers [Part-3] 💻🚀
21. Find the longest substring without repeating characters
Use a sliding window with a set to track characters.
22. Explain backtracking with N-Queens problem
Backtracking tries placing a queen in each column, then recursively places the next queen if safe. If no safe position is found, it backtracks.
23. What is a trie? Where is it used?
A Trie is a tree-like data structure used for efficient retrieval of strings, especially for autocomplete or prefix matching.
Used in:
- Dictionary lookups
- Search engines
- IP routing
24. Explain bit manipulation tricks
- Check if number is power of 2:
- Count set bits:
- Swap without temp:
25. Kadane’s Algorithm for maximum subarray sum
26. What are heaps and how do they work?
Heap is a binary tree where parent is always smaller (min-heap) or larger (max-heap) than children. Supports O(log n) insert and delete.
Use Python’s heapq for min-heaps.
27. Find kth largest element in an array
28. How to detect cycle in a graph?
Use DFS with visited and recursion stack.
29. Topological sort of a DAG
Used to sort tasks with dependencies.
30. Implement a stack using queues
21. Find the longest substring without repeating characters
Use a sliding window with a set to track characters.
def length_of_longest_substring(s):
seen = set()
left = max_len = 0
for right in range(len(s)):
while s[right] in seen:
seen.remove(s[left])
left += 1
seen.add(s[right])
max_len = max(max_len, right - left + 1)
return max_len
22. Explain backtracking with N-Queens problem
Backtracking tries placing a queen in each column, then recursively places the next queen if safe. If no safe position is found, it backtracks.
def solve_n_queens(n):
result = []
board = [-1]×n
def is_safe(row, col):
for r in range(row):
if board[r] == col or abs(board[r] - col) == abs(r - row):
return False
return True
def backtrack(row=0):
if row == n:
result.append(board[:])
return
for col in range(n):
if is_safe(row, col):
board[row] = col
backtrack(row + 1)
board[row] = -1
backtrack()
return result
23. What is a trie? Where is it used?
A Trie is a tree-like data structure used for efficient retrieval of strings, especially for autocomplete or prefix matching.
Used in:
- Dictionary lookups
- Search engines
- IP routing
24. Explain bit manipulation tricks
- Check if number is power of 2:
n & (n - 1) == 0 - Count set bits:
bin(n).count('1') - Swap without temp:
x = x ^ y; y = x ^ y; x = x ^ y25. Kadane’s Algorithm for maximum subarray sum
def max_subarray(nums):
max_sum = current = nums[0]
for num in nums[1:]:
current = max(num, current + num)
max_sum = max(max_sum, current)
return max_sum
26. What are heaps and how do they work?
Heap is a binary tree where parent is always smaller (min-heap) or larger (max-heap) than children. Supports O(log n) insert and delete.
Use Python’s heapq for min-heaps.
27. Find kth largest element in an array
import heapq
def find_kth_largest(nums, k):
return heapq.nlargest(k, nums)[-1]
28. How to detect cycle in a graph?
Use DFS with visited and recursion stack.
def has_cycle(graph):
visited = set()
rec_stack = set()
def dfs(v):
visited.add(v)
rec_stack.add(v)
for neighbor in graph[v]:
if neighbor not in visited and dfs(neighbor):
return True
elif neighbor in rec_stack:
return True
rec_stack.remove(v)
return False
for node in graph:
if node not in visited and dfs(node):
return True
return False
29. Topological sort of a DAG
Used to sort tasks with dependencies.
def topological_sort(graph):
visited, result = set(), []
def dfs(node):
if node in visited:
return
visited.add(node)
for neighbor in graph.get(node, []):
dfs(neighbor)
result.append(node)
for node in graph:
dfs(node)
return result[::-1]
30. Implement a stack using queues
from collections import deque
class Stack:
def init(self):
self.q = deque()
def push(self, x):
self.q.append(x)
for _ in range(len(self.q) - 1):
self.q.append(self.q.popleft())
def pop(self):
return self.q.popleft()
def top(self):
return self.q[0]
def empty(self):
return not self.q
💬 Double Tap ♥️ For Part-4!❤9👍1
✅ DSA Roadmap for Beginners (2025) 🔢🧠
1. Understand What DSA Is
⦁ Data Structures organize data efficiently; Algorithms solve problems step-by-step
⦁ Why learn: Boosts coding interviews, optimizes code for tech jobs
2. Pick a Programming Language
⦁ Start with Python, C++, or Java for syntax basics
⦁ Focus on loops, arrays, functions before diving deep
3. Learn Time & Space Complexity
⦁ Big-O notation: O(1), O(n), O(n²)
⦁ Analyze efficiency to write better code
4. Master Basic Data Structures
⦁ Arrays & Strings: Indexing, manipulation
⦁ Linked Lists: Insertion, deletion, reversal
5. Explore Stacks & Queues
⦁ LIFO (Stack) for undo operations, FIFO (Queue) for tasks
⦁ Applications: Parentheses balancing, BFS
6. Dive into Trees & Graphs
⦁ Binary Trees, BSTs: Traversal (BFS/DFS)
⦁ Graphs: Adjacency lists, shortest paths (Dijkstra)
7. Learn Sorting & Searching
⦁ Algorithms: Bubble, Merge, Quick Sort; Binary Search
⦁ Understand when to use each for efficiency
8. Tackle Recursion & Backtracking
⦁ Base cases, recursive calls
⦁ Problems: Subsets, N-Queens
9. Work on Dynamic Programming
⦁ Memoization, tabulation
⦁ Classics: Fibonacci, Knapsack, LCS
10. Bonus Skills
⦁ Heaps, Tries, Greedy algorithms
⦁ Practice on LeetCode, HackerRank; build projects like pathfinders
💬 Double Tap ♥️ For More
1. Understand What DSA Is
⦁ Data Structures organize data efficiently; Algorithms solve problems step-by-step
⦁ Why learn: Boosts coding interviews, optimizes code for tech jobs
2. Pick a Programming Language
⦁ Start with Python, C++, or Java for syntax basics
⦁ Focus on loops, arrays, functions before diving deep
3. Learn Time & Space Complexity
⦁ Big-O notation: O(1), O(n), O(n²)
⦁ Analyze efficiency to write better code
4. Master Basic Data Structures
⦁ Arrays & Strings: Indexing, manipulation
⦁ Linked Lists: Insertion, deletion, reversal
5. Explore Stacks & Queues
⦁ LIFO (Stack) for undo operations, FIFO (Queue) for tasks
⦁ Applications: Parentheses balancing, BFS
6. Dive into Trees & Graphs
⦁ Binary Trees, BSTs: Traversal (BFS/DFS)
⦁ Graphs: Adjacency lists, shortest paths (Dijkstra)
7. Learn Sorting & Searching
⦁ Algorithms: Bubble, Merge, Quick Sort; Binary Search
⦁ Understand when to use each for efficiency
8. Tackle Recursion & Backtracking
⦁ Base cases, recursive calls
⦁ Problems: Subsets, N-Queens
9. Work on Dynamic Programming
⦁ Memoization, tabulation
⦁ Classics: Fibonacci, Knapsack, LCS
10. Bonus Skills
⦁ Heaps, Tries, Greedy algorithms
⦁ Practice on LeetCode, HackerRank; build projects like pathfinders
💬 Double Tap ♥️ For More
❤2👍1
✅ JavaScript Essentials – Interview Questions with Answers 🧠💻
1️⃣ Q: What is the difference between let, const, and var?
A:
⦁ var: Function-scoped, hoisted, can be redeclared.
⦁ let: Block-scoped, not hoisted like var, can't be redeclared in same scope.
⦁ const: Block-scoped, must be assigned at declaration, cannot be reassigned.
2️⃣ Q: What are JavaScript data types?
A:
⦁ Primitive types: string, number, boolean, null, undefined, symbol, bigint
⦁ Non-primitive: object, array, function
Type coercion: JS automatically converts between types in operations ('5' + 2 → '52')
3️⃣ Q: How does DOM Manipulation work in JS?
A:
The DOM (Document Object Model) represents the HTML structure. JS can access and change elements using:
⦁
⦁
⦁
Example:
4️⃣ Q: What is event handling in JavaScript?
A:
It allows reacting to user actions like clicks or key presses.
Example:
5️⃣ Q: What are arrow functions?
A:
A shorter syntax for functions introduced in ES6.
💬 Double Tap ❤️ For More
1️⃣ Q: What is the difference between let, const, and var?
A:
⦁ var: Function-scoped, hoisted, can be redeclared.
⦁ let: Block-scoped, not hoisted like var, can't be redeclared in same scope.
⦁ const: Block-scoped, must be assigned at declaration, cannot be reassigned.
2️⃣ Q: What are JavaScript data types?
A:
⦁ Primitive types: string, number, boolean, null, undefined, symbol, bigint
⦁ Non-primitive: object, array, function
Type coercion: JS automatically converts between types in operations ('5' + 2 → '52')
3️⃣ Q: How does DOM Manipulation work in JS?
A:
The DOM (Document Object Model) represents the HTML structure. JS can access and change elements using:
⦁
document.getElementById()⦁
document.querySelector()⦁
element.innerHTML (sets HTML content), element.textContent (sets text only), element.style (applies CSS) Example:
document.querySelector('p').textContent = 'Updated text!';4️⃣ Q: What is event handling in JavaScript?
A:
It allows reacting to user actions like clicks or key presses.
Example:
document.getElementById("btn").addEventListener("click", () => {
alert("Button clicked!");
});5️⃣ Q: What are arrow functions?
A:
A shorter syntax for functions introduced in ES6.
const add = (a, b) => a + b;
💬 Double Tap ❤️ For More
❤5
👨🎓System Design Topics: Cheat Sheet for Interview Preparation
☑️ Load Balancing
☑️ API Gateway
☑️ Communication Protocols
☑️ CDN (Content Delivery Network)
☑️ Database
☑️ Cache
☑️ Message Queue
☑️ Generating Unique Identifiers
☑️ Scalability
☑️ Availability
☑️ Performance
☑️ Fault Tolerance and Recovery
☑️ Security and much more
☑️ Load Balancing
☑️ API Gateway
☑️ Communication Protocols
☑️ CDN (Content Delivery Network)
☑️ Database
☑️ Cache
☑️ Message Queue
☑️ Generating Unique Identifiers
☑️ Scalability
☑️ Availability
☑️ Performance
☑️ Fault Tolerance and Recovery
☑️ Security and much more
👍6❤3👏2
🚀 COMPLETE ROADMAP: FROM 4 LPA TO 40 LPA (STEP-BY-STEP PLAN)
PHASE 1: STRONG FOUNDATION (0 – 6 Months)
✅ Master DSA + Core Java
Arrays, Linked List, Stack, Queue, Trees, Graphs, DP
Daily problem solving on LeetCode / CodeStudio
Focus on writing clean & optimised code
PHASE 2: BACKEND DEVELOPMENT (6 – 12 Months)
✅ Become Backend Expert
Spring Boot + REST APIs
Microservices Architecture
SQL + NoSQL Databases
Authentication, JWT, Security Concepts
Build real-world scalable projects
PHASE 3: CLOUD & DEVOPS (12 – 15 Months)
✅ Deployment Skills
Docker & Kubernetes
AWS / GCP / Azure
CI/CD Pipelines
System Monitoring & Scaling
PHASE 4: SYSTEM DESIGN (15 – 20 Months)
✅ Crack High-Level Interviews
Low Level Design (LLD)
High Level Design (HLD)
Load Balancing, Caching, Database Scaling
Design systems like Netflix, Uber, WhatsApp
PHASE 5: ADVANCED ENGINEERING (20 – 30 Months)
✅ Become 10x Engineer
Concurrency & Multithreading
Performance Optimisation
Distributed Systems
Message Queues (Kafka, RabbitMQ)
PHASE 6: BRAND + INTERVIEW PREP (30 – 36 Months)
✅ Personal Branding
Strong GitHub Portfolio
Technical Blogs
LinkedIn Optimisation
Mock Interviews + DSA Revision
PHASE 7: TARGET HIGH-PAY COMPANIES (3 – 5 Years)
✅ Apply For:
MAANG Companies
Product-Based Giants
Remote International Roles
High Paying Startups
🎯 RESULT: 4 LPA ➝ 40 LPA CAREER TRANSFORMATION
Consistency + Smart Learning + Real Projects = High Salary Growth 💰
Reality Check : 👇
₹40 LPA is not magic. 🪄
It’s built with:
Deep skills
Smart job switches
High-leverage projects
Strategic career moves
React ❤️ For More
PHASE 1: STRONG FOUNDATION (0 – 6 Months)
✅ Master DSA + Core Java
Arrays, Linked List, Stack, Queue, Trees, Graphs, DP
Daily problem solving on LeetCode / CodeStudio
Focus on writing clean & optimised code
PHASE 2: BACKEND DEVELOPMENT (6 – 12 Months)
✅ Become Backend Expert
Spring Boot + REST APIs
Microservices Architecture
SQL + NoSQL Databases
Authentication, JWT, Security Concepts
Build real-world scalable projects
PHASE 3: CLOUD & DEVOPS (12 – 15 Months)
✅ Deployment Skills
Docker & Kubernetes
AWS / GCP / Azure
CI/CD Pipelines
System Monitoring & Scaling
PHASE 4: SYSTEM DESIGN (15 – 20 Months)
✅ Crack High-Level Interviews
Low Level Design (LLD)
High Level Design (HLD)
Load Balancing, Caching, Database Scaling
Design systems like Netflix, Uber, WhatsApp
PHASE 5: ADVANCED ENGINEERING (20 – 30 Months)
✅ Become 10x Engineer
Concurrency & Multithreading
Performance Optimisation
Distributed Systems
Message Queues (Kafka, RabbitMQ)
PHASE 6: BRAND + INTERVIEW PREP (30 – 36 Months)
✅ Personal Branding
Strong GitHub Portfolio
Technical Blogs
LinkedIn Optimisation
Mock Interviews + DSA Revision
PHASE 7: TARGET HIGH-PAY COMPANIES (3 – 5 Years)
✅ Apply For:
MAANG Companies
Product-Based Giants
Remote International Roles
High Paying Startups
🎯 RESULT: 4 LPA ➝ 40 LPA CAREER TRANSFORMATION
Consistency + Smart Learning + Real Projects = High Salary Growth 💰
Reality Check : 👇
₹40 LPA is not magic. 🪄
It’s built with:
Deep skills
Smart job switches
High-leverage projects
Strategic career moves
React ❤️ For More
❤4
✅ Programming Language Fun Facts 🧠💻
1️⃣ Python 🐍
⦁ Created by Guido van Rossum in 1991
⦁ Known for readability and simplicity
⦁ Tops 2025 charts in AI, data science, and automation
2️⃣ JavaScript 🌐
⦁ Invented in just 10 days by Brendan Eich (1995)
⦁ Runs in every modern web browser
⦁ Powers 95%+ of websites
3️⃣ C 🖥️
⦁ Developed by Dennis Ritchie between 1969-73
⦁ Backbone of OS kernels and embedded systems
⦁ Foundation for C++, C#, Objective-C
4️⃣ Java ☕
⦁ Released by Sun Microsystems in 1995
⦁ “Write once, run anywhere” mantra
⦁ Powers Android apps and enterprise software
5️⃣ Rust 🦀
⦁ Launched by Mozilla in 2010
⦁ Focuses on memory safety without a garbage collector
⦁ Popular for system-level programming
6️⃣ Go (Golang) 🐹
⦁ Created at Google in 2009
⦁ Designed for simplicity and performance
⦁ Great for backend and microservices
7️⃣ TypeScript 🔷
⦁ Microsoft’s superset of JavaScript (2012)
⦁ Adds static typing
⦁ Hot in large frontend projects
💬 Tap ❤️ for more!
1️⃣ Python 🐍
⦁ Created by Guido van Rossum in 1991
⦁ Known for readability and simplicity
⦁ Tops 2025 charts in AI, data science, and automation
2️⃣ JavaScript 🌐
⦁ Invented in just 10 days by Brendan Eich (1995)
⦁ Runs in every modern web browser
⦁ Powers 95%+ of websites
3️⃣ C 🖥️
⦁ Developed by Dennis Ritchie between 1969-73
⦁ Backbone of OS kernels and embedded systems
⦁ Foundation for C++, C#, Objective-C
4️⃣ Java ☕
⦁ Released by Sun Microsystems in 1995
⦁ “Write once, run anywhere” mantra
⦁ Powers Android apps and enterprise software
5️⃣ Rust 🦀
⦁ Launched by Mozilla in 2010
⦁ Focuses on memory safety without a garbage collector
⦁ Popular for system-level programming
6️⃣ Go (Golang) 🐹
⦁ Created at Google in 2009
⦁ Designed for simplicity and performance
⦁ Great for backend and microservices
7️⃣ TypeScript 🔷
⦁ Microsoft’s superset of JavaScript (2012)
⦁ Adds static typing
⦁ Hot in large frontend projects
💬 Tap ❤️ for more!
❤2
✅ Top YouTube Channels to Learn Coding 📺💻
1️⃣ freeCodeCamp.org
– Full courses on Python, JavaScript, web dev, and data science
– No ads, no fluff – just solid content
2️⃣ Apna College
– Great for beginners in C++, DSA, web dev
– Taught in Hinglish (English + Hindi)
3️⃣ Tech With Tim
– Python tutorials, projects, and game dev with Pygame
– Covers beginner to intermediate topics
4️⃣ The Net Ninja
– Modern web development (React, Node.js, Firebase)
– Clean playlists and short, easy-to-follow videos
5️⃣ CodeWithHarry
– Hindi tutorials for web dev, Python, Java, C++
– Beginner-friendly and practical
6️⃣ Traversy Media
– Covers web technologies, APIs, crash courses
– Ideal for frontend & backend devs
7️⃣ CS50 by Harvard (David Malan)
– World-famous computer science course
– Deep programming foundations
8️⃣ Programming with Mosh
– High-quality tutorials on Python, React, Node, etc.
– Great explanations with visuals
9️⃣ Anuj Bhaiya
– DSA, system design, placement help
– College-friendly Hindi content
🔟 Fireship
– Fast-paced dev content in under 100 seconds
– Great for exploring trending tech/tools
💬 Tap ❤️ for more!
1️⃣ freeCodeCamp.org
– Full courses on Python, JavaScript, web dev, and data science
– No ads, no fluff – just solid content
2️⃣ Apna College
– Great for beginners in C++, DSA, web dev
– Taught in Hinglish (English + Hindi)
3️⃣ Tech With Tim
– Python tutorials, projects, and game dev with Pygame
– Covers beginner to intermediate topics
4️⃣ The Net Ninja
– Modern web development (React, Node.js, Firebase)
– Clean playlists and short, easy-to-follow videos
5️⃣ CodeWithHarry
– Hindi tutorials for web dev, Python, Java, C++
– Beginner-friendly and practical
6️⃣ Traversy Media
– Covers web technologies, APIs, crash courses
– Ideal for frontend & backend devs
7️⃣ CS50 by Harvard (David Malan)
– World-famous computer science course
– Deep programming foundations
8️⃣ Programming with Mosh
– High-quality tutorials on Python, React, Node, etc.
– Great explanations with visuals
9️⃣ Anuj Bhaiya
– DSA, system design, placement help
– College-friendly Hindi content
🔟 Fireship
– Fast-paced dev content in under 100 seconds
– Great for exploring trending tech/tools
💬 Tap ❤️ for more!
❤8😁1
Developers who grow fast in tech tend to combine strong thinking habits with consistent practice and learning routines. These habits compound over time and turn average effort into outsized career progress.
✏ Core thinking habits
⦁ Ask “why” before “how” to understand trade-offs, not just syntax or steps.
⦁ Break big tasks into smaller pieces (input, process, output, edge cases) instead of jumping straight into code.
⦁ Write down approach or pseudocode first so you debug logic, not just syntax.
✏ Learning and experimentation
⦁ Treat learning as a daily routine: small coding sessions, experiments, or reading, even 30–60 minutes a day.
⦁ Stay curious about tools, frameworks, and patterns outside your current stack to avoid getting boxed in.
⦁ Use docs, official guides, and community threads as first-class learning sources, not just copy–paste fixes.
✏ Code quality and best practices
⦁ Write DRY, modular, and testable code so features are easier to change and debug later.
⦁ Read others’ code (PRs, open source, senior teammates) to pick up naming, structure, and patterns.
⦁ Add small tests and logs early to catch issues fast instead of relying only on manual clicking.
✏ Feedback, failure, and growth
⦁ Ship early, accept bugs as feedback, and fix quickly instead of hiding unfinished work.
⦁ Ask for code reviews and genuinely act on feedback rather than defending every decision.
⦁ Keep a simple “learning log” of mistakes, insights, and patterns so lessons aren’t forgotten.
✏ Communication and teaching
⦁ Explain your approach simply in standups, comments, or design docs; clear thinking shows in clear communication.
⦁ Teach what you know via mentoring, posts, or short demos—explaining forces you to close gaps in understanding.
⦁ Stay plugged into blogs, talks, and communities to see how others solve real-world problems.
✏ Core thinking habits
⦁ Ask “why” before “how” to understand trade-offs, not just syntax or steps.
⦁ Break big tasks into smaller pieces (input, process, output, edge cases) instead of jumping straight into code.
⦁ Write down approach or pseudocode first so you debug logic, not just syntax.
✏ Learning and experimentation
⦁ Treat learning as a daily routine: small coding sessions, experiments, or reading, even 30–60 minutes a day.
⦁ Stay curious about tools, frameworks, and patterns outside your current stack to avoid getting boxed in.
⦁ Use docs, official guides, and community threads as first-class learning sources, not just copy–paste fixes.
✏ Code quality and best practices
⦁ Write DRY, modular, and testable code so features are easier to change and debug later.
⦁ Read others’ code (PRs, open source, senior teammates) to pick up naming, structure, and patterns.
⦁ Add small tests and logs early to catch issues fast instead of relying only on manual clicking.
✏ Feedback, failure, and growth
⦁ Ship early, accept bugs as feedback, and fix quickly instead of hiding unfinished work.
⦁ Ask for code reviews and genuinely act on feedback rather than defending every decision.
⦁ Keep a simple “learning log” of mistakes, insights, and patterns so lessons aren’t forgotten.
✏ Communication and teaching
⦁ Explain your approach simply in standups, comments, or design docs; clear thinking shows in clear communication.
⦁ Teach what you know via mentoring, posts, or short demos—explaining forces you to close gaps in understanding.
⦁ Stay plugged into blogs, talks, and communities to see how others solve real-world problems.
❤4
Free Resources to learn SQL
👇🏻👇🏻
Modesql: https://mode.com/sql-tutorial/introduction-to-sql
kudavenkat playlist (first 16): https://www.youtube.com/playlist?list=PL08903FB7ACA1C2FB
SQL Cheatsheet
https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf
khanacademy SQL course: https://www.khanacademy.org/computing/computer-programming/sql
SQL Book for Beginners
https://news.1rj.ru/str/learndataanalysis/36
Practice sql (Very Important)
https://leetcode.com/problemset/database/
https://www.hackerrank.com/domains/sql
https://www.windowfunctions.com/
https://www.sql-practice.com/
ENJOY LEARNING 👍👍
👇🏻👇🏻
Modesql: https://mode.com/sql-tutorial/introduction-to-sql
kudavenkat playlist (first 16): https://www.youtube.com/playlist?list=PL08903FB7ACA1C2FB
SQL Cheatsheet
https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf
khanacademy SQL course: https://www.khanacademy.org/computing/computer-programming/sql
SQL Book for Beginners
https://news.1rj.ru/str/learndataanalysis/36
Practice sql (Very Important)
https://leetcode.com/problemset/database/
https://www.hackerrank.com/domains/sql
https://www.windowfunctions.com/
https://www.sql-practice.com/
ENJOY LEARNING 👍👍
❤2
✅ DSA Roadmap for Coding Interviews 🧠💻
1️⃣ Start with the Basics
– Learn Time & Space Complexity
– Understand Big O notation
2️⃣ Master Arrays & Strings
– Sliding window, Two pointers, Prefix sum
– Practice problems like: Two Sum, Move Zeroes
3️⃣ Dive into Hashing
– Use HashMap/HashSet for fast lookups
– Problems: Longest Substring Without Repeat, Group Anagrams
4️⃣ Linked Lists
– Learn traversal, reversal, cycle detection
– Key problems: Detect Cycle, Merge Two Sorted Lists
5️⃣ Stacks & Queues
– Infix to postfix, parentheses validation, monotonic stack
– Problems: Valid Parentheses, Next Greater Element
6️⃣ Recursion & Backtracking
– Subsets, Permutations, N-Queens
– Key skill: build solution tree and backtrack correctly
7️⃣ Binary Search & Search Problems
– Classic problems: Search in Rotated Array, Koko Eating Bananas
– Understand upper/lower bounds
8️⃣ Trees & Binary Trees
– DFS, BFS, Inorder/Preorder/Postorder
– Problems: Lowest Common Ancestor, Diameter of Tree
9️⃣ Heaps & Priority Queues
– Top K elements, Min/Max heap use cases
🔟 Graphs
– BFS, DFS, Union-Find, Dijkstra’s
– Practice shortest path, connected components, cycle detection
1️⃣1️⃣ Dynamic Programming (DP)
– Start with 1D DP (Fibonacci, Climbing Stairs)
– Move to 2D DP (Knapsack, LCS, Grid Paths)
💡 Tip: Practice on LeetCode, Codeforces, GFG. Use patterns, not memorization.
💬 Tap ❤️ for more!
1️⃣ Start with the Basics
– Learn Time & Space Complexity
– Understand Big O notation
2️⃣ Master Arrays & Strings
– Sliding window, Two pointers, Prefix sum
– Practice problems like: Two Sum, Move Zeroes
3️⃣ Dive into Hashing
– Use HashMap/HashSet for fast lookups
– Problems: Longest Substring Without Repeat, Group Anagrams
4️⃣ Linked Lists
– Learn traversal, reversal, cycle detection
– Key problems: Detect Cycle, Merge Two Sorted Lists
5️⃣ Stacks & Queues
– Infix to postfix, parentheses validation, monotonic stack
– Problems: Valid Parentheses, Next Greater Element
6️⃣ Recursion & Backtracking
– Subsets, Permutations, N-Queens
– Key skill: build solution tree and backtrack correctly
7️⃣ Binary Search & Search Problems
– Classic problems: Search in Rotated Array, Koko Eating Bananas
– Understand upper/lower bounds
8️⃣ Trees & Binary Trees
– DFS, BFS, Inorder/Preorder/Postorder
– Problems: Lowest Common Ancestor, Diameter of Tree
9️⃣ Heaps & Priority Queues
– Top K elements, Min/Max heap use cases
🔟 Graphs
– BFS, DFS, Union-Find, Dijkstra’s
– Practice shortest path, connected components, cycle detection
1️⃣1️⃣ Dynamic Programming (DP)
– Start with 1D DP (Fibonacci, Climbing Stairs)
– Move to 2D DP (Knapsack, LCS, Grid Paths)
💡 Tip: Practice on LeetCode, Codeforces, GFG. Use patterns, not memorization.
💬 Tap ❤️ for more!
❤5
Every job interview has a "Do you have any questions?" part at the end.
But almost every candidate says "No questions from me" or doesn't ask good ones.
Here are 5 interview questions that you can ask to stand out and land offers:
1. I saw you did (XYZ) before joining
(COMPANY). What's your "why", and why did you decide to pursue a career here?
This question takes the interviewer out of the "interviewing" mindset and let's them talk more about their own journey. You can then mention how you relate to their journey, which creates empathy and a stronger connection with the interviewer.
2. If you looked back on this role a year later, what outcomes would indicate that this hire was successful?
This question allows you to see what they're looking for someone to accomplish in the role, and you can then answer their answer by showcasing how you'd do your best to achieve those outcomes.
3. What is the vision of the team in the next 6-12 months, and how would I contribute to having that vision come to life?
This question shows that you're forward-thinking and "in it to win it". You're also getting a deeper understanding of the goals your team has and how you'll play a part in achieving them.
4. What do you believe is the most important skill someone must have in this position?
This question allows you to identify the skill the interviewer wants to see in the candidate, and then you can turn the question back on them and share why you have that skills from your experiences.
5. What are next steps in the interview process, and when should I be hearing back?
This question gives you a timeline of how the interview process will go (if not shared already) and when you should follow-up if you don't hear back from the interviewer.
But almost every candidate says "No questions from me" or doesn't ask good ones.
Here are 5 interview questions that you can ask to stand out and land offers:
1. I saw you did (XYZ) before joining
(COMPANY). What's your "why", and why did you decide to pursue a career here?
This question takes the interviewer out of the "interviewing" mindset and let's them talk more about their own journey. You can then mention how you relate to their journey, which creates empathy and a stronger connection with the interviewer.
2. If you looked back on this role a year later, what outcomes would indicate that this hire was successful?
This question allows you to see what they're looking for someone to accomplish in the role, and you can then answer their answer by showcasing how you'd do your best to achieve those outcomes.
3. What is the vision of the team in the next 6-12 months, and how would I contribute to having that vision come to life?
This question shows that you're forward-thinking and "in it to win it". You're also getting a deeper understanding of the goals your team has and how you'll play a part in achieving them.
4. What do you believe is the most important skill someone must have in this position?
This question allows you to identify the skill the interviewer wants to see in the candidate, and then you can turn the question back on them and share why you have that skills from your experiences.
5. What are next steps in the interview process, and when should I be hearing back?
This question gives you a timeline of how the interview process will go (if not shared already) and when you should follow-up if you don't hear back from the interviewer.
❤4