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Stop revising Python randomly before interviews. Jumping from one concept to another. Solving random questions without direction. Watching multiple videos and still feeling unprepared. That’s not revision. That’s panic. When interviews are close, you don’t need more content. You need structured problem solving. So I created a Last Minute Python Interview Prep video. No long explanations. No unnecessary theory. No distractions. Just: • Handpicked problems • Step-by-step thinking • Clear approach to solve them • Patterns that actually get asked Because in interviews, it’s not about how much you know. It’s about how you think. If your interview is in the next few days, this will help you bring everything together. Link to the video in comments 👇

One of my favorite interview questions is simple on the surface: - Why do we use context managers in Python? Almost instantly, the answer comes: So we don’t have to close connections manually. It auto-closes them. Fair. But then I take it one step further: Why do we need to close connections at all? Why not just leave them open? That’s where things get interesting. Some candidates pause. Some say: It’s not a good practice to leave things open. And I push again: But why is it not a good practice? Because there has to be a reason… right? Only a handful go deeper and talk about: - Limited system resources - File descriptors - Connection/socket limits managed by the OS And that’s the moment you can clearly see the difference. 💡 Context managers (`with` statement) are not just about cleaner syntax. They ensure deterministic resource management, releasing critical resources like files, sockets, and locks as soon as they’re no longer needed. Sometimes, it’s not about what you know… but how far you can go when someone asks “why?” Follow Hitesh Garg for more 🫡 #python #interviews #backend

🚨 Interview Experience Today 👇 Problem: Input: [0,1,0,3,12] Output: [1,3,12,0,0] ⚠️ Constraint: Do not create new/another list ⸻ 💭 My attempt (under time pressure): print(f”[{nums[1]}, {nums[3]}, {nums[4]}, {nums[0]}, {nums[0]}]”) Output: [1, 3, 12, 0, 0] ❌ Rejected ⸻ 💡 Approach shared after: nums = [num for num in nums if num != 0] + [0] * nums.count(0) print(nums) 🤔 I pointed out that this still creates a new list ⸻ 🧠 My Question: 👉 Would this still be considered valid given the constraint? 👉 Or should the expectation strictly be an in-place solution? ⸻ 💬 Curious to hear how you would approach this! #InterviewExperience #Python #CodingInterview #DataStructures #Learning #BackendDevelopment #SoftwareEngineering

Nested loops aren’t inherently “bad,” but in LeetCode they’re often a red flag because they usually increase time complexity to O(n²), O(n³), etc., which doesn’t scale well. So the issue is: They can cause Time Limit Exceeded (TLE) on large inputs They often indicate an opportunity for a better approach (hash map, two pointers, sorting, prefix sums, etc.) Interviewers usually expect you to optimize beyond brute force In short: nested loops = usually brute force → likely inefficient → can be optimized.

Most array interview questions come down to just 5 techniques. 🧩 I've seen hundreds of developers struggle on LeetCode - not because they lack intelligence, but because no one taught them the patterns. Here are the 5 array techniques that show up in 80% of interviews: 1️⃣ HashMap / Frequency Count → Turn O(n²) brute force into O(n) with a dictionary → Use for: duplicates, anagrams, top-K elements 2️⃣ Two Pointers → Left & right pointers moving inward on a sorted array → Use for: pair sums, palindromes, reversing in-place 3️⃣ Sliding Window → Maintain a subarray and slide it across → Use for: max/min subarray of size k, longest subarray with condition 4️⃣ Prefix Sum → Precompute cumulative sums for O(1) range queries → Use for: multiple range sum queries, subarray sum = target 5️⃣ Binary Search → Halve the search space each step - O(log n) → Use for: sorted/rotated arrays and search on answer problems The secret? You don't need to memorize 500 LeetCode problems. You need to recognize which pattern fits. At AlgoNur, we teach these patterns with step-by-step animated visualizations so you actually SEE what's happening, not just memorize code. 🔗 Free. No login required. Try it at https://lnkd.in/dF-zfi4r Swipe through the carousel above to see each technique with code examples. ↑ ♻️ Repost if this helps someone you know preparing for interviews. #DSA #LeetCode #CodingInterview #DataStructures #Algorithms #SoftwareEngineering #Programming #AlgoNur

🚀 Last-minute interview prep? I found something actually useful. I was looking for quick revision content before interviews (without going into 10 different resources)… and came across this playlist by Ankit Bansal Honestly, it's precisely what most of us need at the last moment. It covers: 📊 SQL (real interview-focused) 🏗️ Data Warehousing & Modeling ⚡ Spark basics 🐍 Python concepts No unnecessary theory. No over-explaining. Just the kind of questions and concepts that actually come up in interviews. 💡 What stood out to me: → You can revise fast without feeling overwhelmed → Content is practical, not textbook-style → Good for connecting scattered concepts quickly Most of the time, we overprepare or keep jumping between resources. But before interviews, what really helps is: clarity + focused revision. This is one of those resources. If you’re in the same phase, it might help you too. Don’t aim to learn everything. Aim to revise what matters. #DataAnalytics #SQL #Python #DataEngineering #InterviewPrep #JobPreparation #CareerGrowth #Upskilling #DataAnalyst

Day15 #100 days of leetcode One of the most asked string problems in interviews — and a great way to sharpen your problem-solving skills. 🔍 Problem Given a string, find the longest substring that reads the same forward and backward. 💡 Approach Used: Expand Around Center Instead of checking every substring (which is expensive), we treat each character as a center and expand outward. #DataStructures #Algorithms #Java #CodingInterview #LeetCode #ProblemSolving #TechCareers

🧠 Tricky Interview Questions (Coding Traps + DSA 🔥) This is where many candidates struggle not because questions are hard, but because they miss the hidden traps 👇 💡 1. “Find first non-repeating character in a string” 👉 Trap: Don’t use nested loops → use hashmap 💡 2. “Reverse a linked list” 👉 Trap: Iterative vs recursive approach 💡 3. “Detect cycle in linked list” 👉 Trap: Use Floyd’s Cycle Detection (fast & slow pointer) 💡 4. “Two Sum problem” 👉 Trap: Don’t sort → use hashmap for O(n) 💡 5. “Longest substring without repeating characters” 👉 Trap: Sliding window technique 💡 6. “Merge overlapping intervals” 👉 Trap: Sort first, then merge 💡 7. “Find kth largest element” 👉 Trap: Heap vs quickselect 💡 8. “Debounce function implementation” 👉 Trap: Closures + timing 💡 9. “Flatten nested array” 👉 Trap: Recursion vs iterative stack 💡 10. “Implement LRU Cache” 👉 Trap: Use HashMap + Doubly Linked List 🔥 What Interviewers Are Testing ✔️ Problem-solving approach ✔️ Time & space complexity ✔️ Clean code ⚠️ Common Mistakes ❌ Jumping to code without thinking ❌ Ignoring edge cases ❌ Not explaining approach 🚀 Pro Tip: Always follow this approach: 👉 Understand → Optimize → Code → Test #DSA #CodingInterview #InterviewPrep #MERNStack #Algorithms #Developers #TechCareers #ProblemSolving

One SQL mistake can change the entire interview. Not tools. Not experience. Just this: 👉 SQL 👉 Python 👉 How you think under pressure That’s what Data Engineering interviews really test. Mistakes in interviews are costly. Once you lose an opportunity, it may become someone else’s opportunity. Be prepared before you attend. Don’t attend interviews just to test your luck. We spend months chasing tools… But fundamentals are what actually decide the result. Are we preparing the right way? #DataEngineering #SQL #Python #InterviewPrep #TechCareers #Learning #CareerGrowth #Engineers #RealTalk

“Stop Googling DSA Before Interviews. This Cheat Sheet Has Everything.” 🚀📘 Let’s be real — When interviews are close, nobody has time to reread full DSA books or watch 10-hour playlists. That’s exactly why this DSA Cheat Sheet PDF exists. This is not theory-heavy content. It’s a last-minute, high-impact revision guide covering the core concepts, patterns, and logic interviewers expect you to recall instantly. 💡 What this DSA Cheat Sheet helps you do: ✅ Revise key DSA topics fast — without overwhelm ✅ Recall patterns instead of memorizing code ✅ Strengthen problem-solving intuition ✅ Walk into interviews with confidence, not confusion 📌 What’s covered inside: • Core Data Structures & their use cases • Algorithm patterns you see again and again • Time & space complexity clarity • Interview-focused explanations (no fluff) • Perfect for quick revision before interviews, tests, or coding rounds 🔥 Why this matters: Interview pressure doesn’t test how much you studied It tests how quickly you can recall and apply concepts And that’s exactly what a cheat sheet is for. 📈 Best way to use it: 🔹 Read once end-to-end 🔹 Keep it bookmarked 🔹 Revise before interviews 🔹 Use it as a mental map while solving problems 📌 Save this post — your interview self will thank you 👀 Share with someone preparing for DSA right now Follow SphereX for more !!! LinkedIn LinkedIn for Marketing LinkedIn Learning LinkedIn News Mohit Jaryal Aniket Singh #SphereX #DSACheatSheet #DataStructures #Algorithms #DSARevision #CodingInterviews #Python #InterviewPreparation #SoftwareEngineering #ComputerScience #ProblemSolving #CodingLife #TechCareers #Placements #CompetitiveProgramming #LeetCode #DSARoadmap #BackendDeveloper #FresherLife #CareerGrowth #Java #DeveloperCommunity #InterviewReady