Coding Interview Preparation

Hey, you probably saw in other channels that its my 31st birthday today 🥳

You also maybe saw that I have become a father, so I took vacation to be with my son 👼❤️ and also during this vacation I worked really hard while my boy sleeps to make our channels much more useful, so I am starting with this one.

Starting tomorrow this channel will prepare you for your job interviews. There will posts more often and all posts will be related to each other.
Get ready for coding challenges with hints and full solutions, quizzes that actually check what you learned, SQL and System Design series that build in difficulty week over week, plus real resources - not just tips to scroll past.

I hope you will find it useful,
your @bigdataspecialist 🧡

⏫ Monotonic Stack: Next Greater Element

📖 Core Idea: A monotonic stack keeps elements in strictly increasing or decreasing order. As you iterate, you pop elements that violate the order and use the popped results to answer “next greater/smaller” questions efficiently in a single pass.

🗯Real Interview Scenario: “Next Greater Element”, “Daily Temperatures”, or “Largest Rectangle in Histogram”.

✅ How to shine: Say:

I’ll maintain a monotonic decreasing stack of indices to find the first larger element in O(n) time

Explain why you traverse right-to-left for next greater. Dry-run a small example out loud. Interviewers love this clarity.

💻 Backend Basics Interview Questions – (Node.js)

📍 1. What is Node.js?
Answer: Node.js is a runtime environment that lets you run JavaScript on the server side. It uses Google’s V8 engine and is designed for building scalable network applications.

📍 2. How is Node.js different from traditional server-side platforms?
Answer: Unlike PHP or Java, Node.js is event-driven and non-blocking. This makes it lightweight and efficient for I/O-heavy operations like APIs and real-time apps.

📍 3. What is the role of the package.json file?
Answer: It stores metadata about your project (name, version, scripts) and dependencies. It’s essential for managing and sharing Node.js projects.

📍 4. What are CommonJS modules in Node.js?
Answer: Node uses CommonJS to handle modules. You use require() to import and module.exports to export code between files.

📍 5. What is the Event Loop in Node.js?
Answer: It allows Node.js to handle many connections asynchronously without blocking. It’s the heart of Node’s non-blocking architecture.

📍 6. What is middleware in Node.js (Express)?
Answer: Middleware functions process requests before sending a response. They can be used for logging, auth, validation, etc.

📍 7. What is the difference between process.nextTick(), setTimeout(), and setImmediate()?
Answer:
⦁ process.nextTick() runs after the current operation, before the next event loop.
⦁ setTimeout() runs after a minimum delay.
⦁ setImmediate() runs on the next cycle of the event loop.

📍 8. What is a callback function in Node.js?
Answer: A function passed as an argument to another function, executed after an async task finishes. It’s the core of async programming in Node.

📍 9. What are Streams in Node.js?
Answer: Streams let you read/write data piece-by-piece (chunks), great for handling large files. Types: Readable, Writable, Duplex, Transform.

📍 10. What is the difference between require and import?
Answer:
⦁ require is CommonJS (used in Node.js by default).
⦁ import is ES6 module syntax (used with "type": "module" in package.json).

💰 Greedy Algorithm Mindset

📖 Core Idea: Greedy makes the locally best choice at every step, hoping these choices lead to a global optimum. It works well when the problem has optimal substructure and a greedy choice property that you can prove or justify.

🗯 Real Interview Scenario: “Jump Game”, “Minimum Number of Arrows to Burst Balloons”, or interval scheduling.

✅ How to shine: Share your greedy intuition first, then briefly prove why it works (e.g., “Sorting by end time guarantees we fit maximum activities”). Compare with DP when asked. This shows deeper problem-solving maturity.

✅ 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)

💡Practice on LeetCode, Codeforces, GFG. Use patterns, not memorization.

@coding_interview_preparation

🔄 Recursion & Backtracking Basics

Core Idea: Function calls itself to break big problems into smaller identical ones. Add backtracking to explore all possibilities and undo choices.

When to use: Subsets, permutations, combinations, or maze/path problems.

🗯Real Interview Scenario:

Generate all subsets

or “Word Search” in a grid.

✅ How to shine: Explain:

I’ll use recursion with backtracking to try choices and undo them.

Draw the recursion tree verbally. Always define clear base case first. Watch stack depth for large inputs.

Practice 4-5 problems. Once comfortable, you’ll handle many medium/hard questions confidently.

HTTP status codes: Quick cheat sheet

✅ 200 OK: request succeeded
🆕 201 Created: new resource saved
📝 204 No Content: success, nothing to return
🔀 301 Moved Permanently: use new URL
↪️ 302 Found: temporary redirect
🧾 304 Not Modified: use cached version

🙅 400 Bad Request: invalid input
🪪 401 Unauthorized: missing/invalid auth
🚫 403 Forbidden: authenticated but not allowed
❓ 404 Not Found: resource doesn’t exist
⏳ 408 Request Timeout: client took too long
🧯 409 Conflict: state/version clash

💥 500 Internal Server Error: server crashed
🛠 502 Bad Gateway: upstream failed
🕸 503 Service Unavailable: overloaded/maintenance
⌛️ 504 Gateway Timeout: upstream too slow

✔️ Tips
• return precise codes; don’t default to 200/500
• include a machine-readable error body (code, message, details)
• never leak stack traces in production
• pair 304 with ETag/If-None-Match for caching

⭐️ Behavioral: Nail Any “Tell Me About a Time” Question

Use STAR method (Amazon/Google favorite):
Situation: Short context
Task: Your responsibility
Action: What you did (focus here)
Result: Quantify outcome (numbers = gold)

Example Question:

Tell me about a challenging project.

❌ Bad: Ramble story.
✅Good: 60-sec STAR answer ending with “...resulted in 25% faster processing.”

👉 Pro Move: Prepare 3-4 stories (failure, leadership, teamwork, conflict). Practice out loud. End with what you learned.
This decides “culture fit”, prepare as hard as coding!

SQL Interview Questions with Answers

1. How to change a table name in SQL?

This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.


2. How to use LIKE in SQL?

The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.


3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?

Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.


4. Explain SQL Constraints.

SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY

@coding_interview_preparation

🌳 DFS vs BFS : Choose Right in 10 Seconds

👉 DFS (Stack/Recursion): Goes deep first. Great for path existence, cycle detection, or "any valid path".
👉 BFS (Queue): Level by level. Best for shortest path in unweighted graph or "minimum steps".

🗯 Interview Encounter: "Number of islands" or "Shortest path in maze" → BFS wins. "Validate BST" or "Clone graph" → DFS is natural.

✅ Pro Tip: Tell interviewer: “I’ll use BFS for shortest, DFS for space efficiency.” Always mention visited set to avoid cycles. Dry-run small example verbally.

✅ Top JavaScript Interview Questions & Answers 💻

📍 1. What is JavaScript and why is it important?
Answer: JavaScript is a dynamic, interpreted programming language that makes web pages interactive. It runs in browsers and on servers (Node.js), enabling features like animations, form validation, and API calls.

📍 2. Explain the difference between var, let, and const.
Answer: var has function scope and is hoisted; let and const have block scope. const defines constants and cannot be reassigned.

📍 3. What are closures in JavaScript?
Answer: Closures occur when a function remembers and accesses variables from its outer scope even after that outer function has finished executing.

📍 4. What is the Event Loop?
Answer: The Event Loop manages asynchronous callbacks by pulling tasks from the callback queue and executing them after the call stack is empty, enabling non-blocking code.

📍 5. What are Promises and how do they help?
Answer: Promises represent the eventual completion or failure of an asynchronous operation, allowing cleaner async code with .then(), .catch(), and async/await.

📍 6. Explain 'this' keyword in JavaScript.
Answer: this refers to the context object in which the current function is executed — it varies in global, object, class, or arrow function contexts.

📍 7. What is prototypal inheritance?
Answer: Objects inherit properties and methods from a prototype object, allowing reuse and shared behavior in JavaScript.

📍 8. Difference between == and === operators?
Answer: == compares values after type coercion; === compares both value and type strictly.

📍 9. How do you handle errors in JavaScript?
Answer: Using try...catch blocks for synchronous code and .catch() or try-catch with async/await for asynchronous errors.

📍 🔟 What are modules in JavaScript and their benefits?
Answer: Modules split code into reusable files with import and export. They improve maintainability and scope management.

💡 Pro Tip: Complement your answers with simple code snippets and real project scenarios if/when possible.

🛠 Two Pointers Pattern: Spot It & Solve Fast

When to use: Sorted arrays, find pairs, or remove duplicates.
Simple Template:

left, right = 0, len(arr)-1
while left < right:
    if condition(arr[left], arr[right]):
        # found or move both
        left += 1
        right -= 1
    elif too_small:
        left += 1
    else:
        right -= 1

Interviewer:

Tell me if two numbers in a sorted array sum to target" (Two Sum II) or "Container With Most Water.

How to Answer:

Array is sorted, so two pointers from ends should work in O(n)

Show brute force first, then optimize. Practice: 3Sum, Remove Duplicates.

🔥 You will solve these in <15 mins next interview!

🔥 Coding Interview Acronyms You MUST Know 💻

DSA → Data Structures & Algorithms
CPU → Central Processing Unit
RAM → Random Access Memory
DBMS → Database Management System
RDBMS → Relational Database Management System
ACID → Atomicity, Consistency, Isolation, Durability
OLTP → Online Transaction Processing
OLAP → Online Analytical Processing
TCP → Transmission Control Protocol
IP → Internet Protocol
DNS → Domain Name System
MVC → Model View Controller
MVVM → Model View ViewModel
SDLC → Software Development Life Cycle
CI/CD → Continuous Integration / Continuous Deployment
JWT → JSON Web Token
ORM → Object Relational Mapping
API → Application Programming Interface
REST → Representational State Transfer
SOAP → Simple Object Access Protocol
Big O → Time & Space Complexity Notation
FIFO → First In First Out
LIFO → Last In First Out

@coding_interview_preparation

✅ 90 Data Science Interview Questions

Data Science Basics
1. What is data science and how is it different from data analytics?
2. What are the key steps in a data science lifecycle?
3. What types of problems does data science solve?
4. What skills does a data scientist need in real projects?
5. What is the difference between structured and unstructured data?
6. What is exploratory data analysis and why do you do it first?
7. What are common data sources in real companies?
8. What is feature engineering?
9. What is the difference between supervised and unsupervised learning?
10. What is bias in data and how does it affect models?

Statistics and Probability
11. What is the difference between mean, median, and mode?
12. What is standard deviation and variance?
13. What is probability distribution?
14. What is normal distribution and where is it used?
15. What is skewness and kurtosis?
16. What is correlation vs causation?
17. What is hypothesis testing?
18. What are Type I and Type II errors?
19. What is p-value?
20. What is confidence interval?

Data Cleaning and Preprocessing
21. How do you handle missing values?
22. How do you treat outliers?
23. What is data normalization and standardization?
24. When do you use Min-Max scaling vs Z-score?
25. How do you handle imbalanced datasets?
26. What is one-hot encoding?
27. What is label encoding?
28. How do you detect data leakage?
29. What is duplicate data and how do you handle it?
30. How do you validate data quality?

Python for Data Science
31. Why is Python popular in data science?
32. Difference between list, tuple, set, and dictionary?
33. What is NumPy and why is it fast?
34. What is Pandas and where do you use it?
35. Difference between loc and iloc?
36. What are vectorized operations?
37. What is lambda function?
38. What is list comprehension?
39. How do you handle large datasets in Python?
40. What are common Python libraries used in data science?

Data Visualization
41. Why is data visualization important?
42. Difference between bar chart and histogram?
43. When do you use box plots?
44. What does a scatter plot show?
45. What are common mistakes in data visualization?
46. Difference between Seaborn and Matplotlib?
47. What is a heatmap used for?
48. How do you visualize distributions?
49. What is dashboarding?
50. How do you choose the right chart?

Machine Learning Basics
51. What is machine learning?
52. Difference between regression and classification?
53. What is overfitting and underfitting?
54. What is train-test split?
55. What is cross-validation?
56. What is bias-variance tradeoff?
57. What is feature selection?
58. What is model evaluation?
59. What is baseline model?
60. How do you choose a model?

Supervised Learning
61. How does linear regression work?
62. Assumptions of linear regression?
63. What is logistic regression?
64. What is decision tree?
65. What is random forest?
66. What is KNN and when do you use it?
67. What is SVM?
68. How does Naive Bayes work?
69. What are ensemble methods?
70. How do you tune hyperparameters?

Unsupervised Learning
71. What is clustering?
72. Difference between K-means and hierarchical clustering?
73. How do you choose value of K?
74. What is PCA?
75. Why is dimensionality reduction needed?
76. What is anomaly detection?
77. What is association rule mining?
78. What is DBSCAN?
79. What is cosine similarity?
80. Where is unsupervised learning used?

Model Evaluation Metrics
81. What is accuracy and when is it misleading?
82. What is precision and recall?
83. What is F1 score?
84. What is ROC curve?
85. What is AUC?
86. Difference between confusion matrix metrics?
87. What is log loss?
88. What is RMSE?
89. What metric do you use for imbalanced data?
90. How do business metrics link to ML metrics?

🔥 Dynamic Programming (DP)

Dynamic Programming is one of the most important and slightly advanced topics in coding interviews.

📌 What is Dynamic Programming?

Dynamic Programming is a technique used to solve complex problems by breaking them into smaller subproblems and storing their results.

👉 Instead of solving the same problem again and again, we reuse previously computed results.

🧠 Why DP is Needed?

Some problems have:
• Overlapping subproblems (same calculation repeated)
• Optimal substructure (solution built from smaller solutions)

DP helps to:
• reduce time complexity
• avoid redundant calculations

⚙️ Two Approaches in DP

1️⃣ Memoization (Top-Down)
Uses recursion
Stores results in memory (cache)
Avoids repeated calculations

👉 Think: solve first, store later

2️⃣ Tabulation (Bottom-Up)
Uses iteration
Builds solution step by step
No recursion

👉 Think: build from smallest to largest

🔁 Example Concept: Fibonacci

Normal recursion:
Repeats same calculations → slow

Dynamic Programming:
Store results → faster

👉 This reduces complexity from O(2ⁿ) to O(n)

🧠 Key DP Patterns

1️⃣ 1D DP
Example:
• Fibonacci
• Climbing stairs

2️⃣ 2D DP
Example:
• Grid problems
• Longest Common Subsequence

3️⃣ Knapsack Pattern
Example:
• Max value with limited weight

4️⃣ Subsequence Problems
Example:
• Longest Increasing Subsequence

⚡️ When to Use DP

Look for:
• Repeated subproblems
• Need for optimization
• Recursive solution possible
• “Find maximum/minimum ways”

⚠️ Common Mistakes

❌ Not identifying overlapping subproblems
❌ Using recursion without memoization
❌ Wrong state definition
❌ Not understanding transitions

🎯 Interview Questions

• What is Dynamic Programming?
• Difference between DP and recursion
• Memoization vs Tabulation
• Fibonacci using DP
• Knapsack problem
• Longest Common Subsequence

⭐️The Main Point:
DP is not about memorizing problems.
It’s about identifying patterns like:

👉 “Can I reuse previous results?”

💡 Simple Thought Process

1. Can I break problem into smaller parts?
2. Are subproblems repeating?
3. Can I store results?

👉 If yes → Use DP

@coding_interview_preparation