Coding Case Studies and Interactive Problem Solving

In the last 15 years, I have interviewed 800+ Software Engineers across Google, Paytm, Amazon & various startups. Here are the most actionable tips I can give you on how to approach  solving coding problems in Interviews  (My DMs are always flooded with this particular question) 1. Use a Heap for K Elements      - When finding the top K largest or smallest elements, heaps are your best tool.      - They efficiently handle priority-based problems with O(log K) operations.      - Example: Find the 3 largest numbers in an array.   2. Binary Search or Two Pointers for Sorted Inputs      - Sorted arrays often point to Binary Search or Two Pointer techniques.      - These methods drastically reduce time complexity to O(log n) or O(n).      - Example: Find two numbers in a sorted array that add up to a target.   3. Backtracking    - Use Backtracking to explore all combinations or permutations.      - They’re great for generating subsets or solving puzzles.      - Example: Generate all possible subsets of a given set.   4. BFS or DFS for Trees and Graphs      - Trees and graphs are often solved using BFS for shortest paths or DFS for traversals.      - BFS is best for level-order traversal, while DFS is useful for exploring paths.      - Example: Find the shortest path in a graph.   5. Convert Recursion to Iteration with a Stack      - Recursive algorithms can be converted to iterative ones using a stack.      - This approach provides more control over memory and avoids stack overflow.      - Example: Iterative in-order traversal of a binary tree.   6. Optimize Arrays with HashMaps or Sorting      - Replace nested loops with HashMaps for O(n) solutions or sorting for O(n log n).      - HashMaps are perfect for lookups, while sorting simplifies comparisons.      - Example: Find duplicates in an array.   7. Use Dynamic Programming for Optimization Problems      - DP breaks problems into smaller overlapping sub-problems for optimization.      - It's often used for maximization, minimization, or counting paths.      - Example: Solve the 0/1 knapsack problem.   8. HashMap or Trie for Common Substrings      - Use HashMaps or Tries for substring searches and prefix matching.      - They efficiently handle string patterns and reduce redundant checks.      - Example: Find the longest common prefix among multiple strings.   9. Trie for String Search and Manipulation      - Tries store strings in a tree-like structure, enabling fast lookups.      - They’re ideal for autocomplete or spell-check features.      - Example: Implement an autocomplete system.   10. Fast and Slow Pointers for Linked Lists      - Use two pointers moving at different speeds to detect cycles or find midpoints.      - This approach avoids extra memory usage and works in O(n) time.      - Example: Detect if a linked list has a loop.   💡 Save this for your next interview prep!

I have some observations and an anecdote for those who are currently invested in studying or learning, at any stage of their life. Learning is an order of magnitude more effective if you have a clear purpose or use case for which you need the knowledge/skill. This is one core reason why many of us quickly lose knowledge that we gain during academic study, and it is also a reason why some packaged 'certifications' can be ineffective at ensuring you retain knowledge and skills. The right purpose/use case needs to be a problem you are excited about and highly incentivised to solve, and often you can come up with these for yourself. Let me give you an example. Early in 2020, my 11 year old daughter and I were watching an episode of Friends, when she started talking about all the relationships between the characters and how complex they became as each season progressed. At the time, I was keen to build my technical skills in Network Analysis, and I had extra time on my hands because of the pandemic. I realized that my daughter's observation would be a great data science and data visualization project. Over the next few weeks I decided to build a codebase for this project, including: 1. Learning how to scrape character appearances from scripts of Friends episodes in R 2. Determining how to turn the data into a network edgelist using R 3. Constructing and analyzing graphs and graph metrics from the edgelist using R 4. Using iterative and functional programming to automate these processes across multiple seasons and episodes of Friends. 5. Building out an interactive visualization of how the network complexity increased with each season (below) using D3 and Javascript. Later I built out the equivalent codebase in Python also, and it became one of the learning components of my textbook on graph theory and network analysis. Because of the experience I had in building a project like this from nothing, and in bringing the truth of data to my daughter's observations, this became a permanent knowledge and skill foundation for me which I still make great use of today in my job and in my teaching. If you are interested in exploring the original codebase of this work, you can find it here ---> https://lnkd.in/dhNcm8E. The final visualization is here --> https://lnkd.in/dKdJXj3. For a more in-depth tutorial and treatment of this work, you can consult my textbook here --> https://ona-book.org #analytics #learning #datascience #rstats #python #peopleanalytics #networks #technology

A crucial problem-solving technique for coding interviews is the "Case Analysis". With hard problems, you often don't know where to start. But what you CAN do is sort the inputs into cases and tackle each case one by one. Take the problem of finding the minimum in a sorted, rotated array with duplicates (a LeetCode hard, linked below). The array could look like [4, 5, 6, 1, 3, 3, 4], and we should return 1. My first thought was to apply binary search (given the trigger: sorted array), but the presence of duplicates gave me pause. Imagine we check the midpoint, and we find... [4, ?, ?, 4, ?, ?, 4] It's unclear whether we should go left or right. To get unstuck, I did a Case Analysis. My intuition was that the relationship between the first and last elements is important, so I classified the inputs based on that: ➤ Case 1: first < last ➤ Case 2: first > last ➤ Case 3: first = last Tackling each of these cases individually is a lot less daunting. ➤ Case 1: first < last We have something like [1, ?, ..., ?, 10]. We must realize that the array is not rotated at all; the elements after the rotation point can never be greater than the first element (an array like [1, 2, 3, 0, 10] is impossible). So, for Case 1, we simply return the first element in O(1) time. ✅ ➤ Case 2: first > last This allows us to binary search for the rotation point. We can define the 'before' region as elements >= first, and the 'after' region as elements < first. Then we binary search for the transition point and return the first element in the 'after' region. This binary search takes O(log n) time. ✅ (Blog post about finding transition points linked below.) ➤ Case 3: first = last Zoning in on this Case led me to come up with a worst-case input: Imagine that the sorted array, before the rotation, is this: [0, 1, 1, 1, 1, 1, ...., 1]. After the rotation, this ends up looking like an array with all 1's, except a 0 at a random position. You can't binary search for that! For this input, NO algorithm can do better than a O(n) linear scan. ❌ We could then pivot the conversation to perhaps improving the average runtime for Case 3. LeetCode is not all about memorizing answers. Interviewers want to see strategic problem-solving like this.

► Interviewer:  You’re tasked with building a simplified ride-sharing application where users can either offer or book rides. The focus is on implementing functionality like user onboarding, ride offering, and ride booking without any UI. Questions like this are commonly asked in machine coding rounds at companies like Flipkart, Ola, and Swiggy to test your ability to design modular systems, handle edge cases, and write clean, scalable code. ► Approach to Solve the Problem 1. Understand the Problem Statement      - The application lets users either offer a ride or consume a ride.      - Users can search for available rides based on source and destination.      - The focus is on functionality, code quality, and modularity, not UI.  2. Break Down the Features      - User Onboarding: Write a method to add users (`add_user(user_detail)`), including basic details like name, age, and gender.      - Add Vehicle: Let users register their vehicle(s) using `add_vehicle`.      - Offer Rides: Create a method (`offer_ride`) to let users share a ride, including details like source, destination, seats, and timing.      - Search Rides: Implement a way to find available rides based on selection strategy (e.g., shortest time or earliest end time).      - Select Ride: Allow users to book a ride for themselves using `select_ride`.  3. Plan Your Implementation      - Start with a basic class structure:        - `User`: To manage user details.        - `Vehicle`: To handle vehicle-related data.        - `Ride`: To manage ride-sharing operations.      - Use data structures like lists and dictionaries for in-memory storage of users, vehicles, and rides.  4. Prioritize Code Quality      - Ensure modular design: Keep each functionality in its own method or class.      - Handle edge cases like duplicate users, invalid ride selections, or exceeding seat capacity.      - Use clear variable names and add comments for clarity.  5. Test Your Code      - Follow the sample test cases provided in the problem.      - Write additional test cases to cover edge cases.      - Example:        - Add a user → Add a vehicle → Offer a ride → Search and select a ride.  6. Focus on Bonus Features (Optional)      - Implement concurrency handling if there’s time.      - Add logic to calculate the total fuel saved if multiple users share a ride.  ► Sample Execution Plan   1. Add a user:      `add_user("Rohan", M, 36)`   2. Register their vehicle:      `add_vehicle("Rohan", "Swift", KA-01-12345)`   3. Offer a ride:      `offer_ride("Rohan", "Bangalore", "Mysore", "Swift", 3, "9 AM")`   4. Search for available rides:      `select_ride("Shashank", "Bangalore", "Mysore", "fastest_ride")`  ► Tips for Machine Coding Rounds   - Start Small: Write a working solution for the main functionality before handling edge cases or bonus features.   - Keep It Simple: Don’t over-engineer; focus on readability and modularity.   - Think Out Loud: Communicate your thought process while coding.  

Ready to solve case questions with Python in your next interview? Here is an example of how to tackle such questions using pandas: 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Imagine you’re working with a retail company that wants to analyze customer purchase behavior. You’re given a dataset with customer IDs, purchase dates, and purchase amounts. How would you calculate the total revenue for each customer and then identify the top 5 customers by revenue? 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 𝗦𝘁𝗲𝗽 𝟭: Load the dataset into a Pandas DataFrame. 𝘪𝘮𝘱𝘰𝘳𝘵 pandas 𝘢𝘴 pd customer_purchases_df = 𝘱𝘥.𝘳𝘦𝘢𝘥_𝘤𝘴𝘷('customer_purchases.csv') 𝗦𝘁𝗲𝗽 𝟮: Group the data by customer_id and calculate the revenue for each customer. customer_revenue = customer_purchases_df.𝘨𝘳𝘰𝘶𝘱𝘣𝘺(      'customer_id'['purchase_amount'] ).𝘴𝘶𝘮().𝘳𝘦𝘴𝘦𝘵_𝘪𝘯𝘥𝘦𝘹() 𝗦𝘁𝗲𝗽 𝟯: Sort the customers by revenue in descending order to find the top 5. top_customers = customer_revenue.𝘴𝘰𝘳𝘵_𝘷𝘢𝘭𝘶𝘦𝘴(     𝘣𝘺='purchase_amount', 𝘢𝘴𝘤𝘦𝘯𝘥𝘪𝘯𝘨=False ).𝘩𝘦𝘢𝘥(5) 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗧𝗶𝗽𝘀: • 𝗕𝗿𝗲𝗮𝗸 𝗗𝗼𝘄𝗻 𝘁𝗵𝗲 𝗧𝗮𝘀𝗸: Identify the key steps like loading the data, grouping by customer, summing purchase amounts, and sorting. Splitting the problem into small steps helps to avoid errors and ensures a clear structure.    • 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗣𝗮𝗻𝗱𝗮𝘀 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Pandas has built-in functions like groupby(), sum(), and sort_values() that simplify complex operations. Knowing these functions can  reduce your coding time and increase efficiency.    • 𝗥𝗲𝗹𝗮𝘁𝗲 𝘁𝗼 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗺𝗽𝗮𝗰𝘁: In interviews, highlight the business relevance of your solution. For example, understanding customer behavior through revenue analysis can help the company target high-value customers with personalized offers.    • 𝗧𝗲𝘀𝘁 𝘄𝗶𝘁𝗵 𝗘𝗱𝗴𝗲 𝗖𝗮𝘀𝗲𝘀: Consider what happens if the dataset is empty, if a customer has no purchases, or if there are multiple customers with the same amount of revenue. Being able to handle business-relevant scenarios with Python will increase your chance of leaving a good impression on the hiring manager and prepare you for future tasks on the job. How do you approach solving case-based coding challenges? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #python #interviewquestions #careergrowth  

Solving 500 or even 1000 Leetcode problems doesn’t guarantee that you can clear coding interviews…. Even after ratings of 2000+ on Codeforces & Codechef, I failed 30+ coding interviews because it was way different than solving problems at home. Here are the lessons I learned the hard way: 1/ The Right Mindset  I often panicked for no reason, went blank, and felt overwhelmed under pressure.  🔹 Interviews are tough by design you’re not expected to have a perfect answer immediately.   🔹 The interviewer is more interested in how you think, not just the final solution.  💡 What to do?   ✔ Focus on iterating towards an optimized solution.   ✔ Think out loud and explain your reasoning, trade-offs, and approach.   ✔ Stay calm. Interviewers are usually friendly when you communicate well.  📝 Prepare a 90-120-second intro about yourself and start the interview confidently.   2/ Never Jump Straight Into Coding  🔹 Take time to understand the problem:   - Read carefully & don’t assume anything.   - Ask clarifying questions, interviewers expect this.   - Analyze constraints, time, space, input size.  🔹 Example Question:   👉 Given an array of integers, return the indices of two numbers that add up to a specific target.  💡 Instead of jumping to code, ask:   ✔ Can the array contain duplicate numbers?   ✔ Is there always exactly one solution?   ✔ What should I return if no two numbers add up to the target?  🔹 Why does this help?   ✔ It saves debugging time by avoiding wrong assumptions.   ✔ It shows structured thinking, which interviewers love.  3/ Plan Before You Code  🔹 Never start coding without a plan.  For the above example, a brute-force approach:   ✅ Check every pair of numbers → O(n²) time complexity  Now, iterate on this:   ✔ Can I optimize? (Yes! Use a hash map for O(n) solution.)   ✔ Am I repeating unnecessary work?   ✔ Can I leverage problem constraints?  💡 Think of small optimizations:   ✔ Sorted input? → Use Binary Search instead of Linear Search.   ✔ Repeated calculations? → Use Hash Maps or Precomputations.  It’s not just about solving problems but how well you solve them in an interview setting.  🔹 Interviews test:   ✅ Your problem-solving approach   ✅ Your ability to communicate your thought process   ✅ Your adaptability under pressure  So next time, don’t just start coding, pause, plan, and think. That’s what makes a difference. – P.S: If you’re currently preparing for DSA, HLD, and LLD. Check out my one-stop resource guide on Topmate: → https://lnkd.in/eYHSjbys ( 380+ students are already using it) (Running on 25% discount for the first 25 people, I am celebrating hitting 100k) This guide will help you with: - DSA, HLD, and LLD for interviews - good resources that I used personally - lots of problems and case studies for DSA and system design

Recently, a friend of mine interviewed for SDE2 at Atlassian, and one of the problems he was asked really stood out to me. It wasn’t just about coding - it was about problem solving under pressure. The interviewer gave him this problem: You are installing a billboard and want two steel supports of equal height using given rods. Each rod can be welded together. Find the largest possible equal height for both supports. At first glance, it looks like a subset problem. But it’s deeper than that. You need to: Think in terms of difference between two supports Use dynamic programming Optimize for constraints (N ≤ 20, sum ≤ 5000) Handle state transitions carefully What impressed me was not just solving it - but explaining the approach clearly: Instead of trying all subsets directly, he modeled it as: 👉 Track difference = leftHeight - rightHeight 👉 Store the maximum possible smaller height for each difference 👉 Finally, when difference becomes 0, that’s your answer This is the kind of problem that tests: DP intuition State compression thinking Clean implementation Communication clarity It reminded me that SDE2 interviews are less about syntax and more about: Structured thinking Trade-off discussion Explaining why your approach works Big takeaway: If you're preparing for strong product companies, don’t just practice problems - practice explaining your thought process like you're teaching someone. Curious - have you faced a DP question that completely changed the way you think about problem solving? https://lnkd.in/gbKWyWhg #InterviewExperience #SDE2 #Atlassian #DynamicProgramming #CodingInterviews #SoftwareEngineering