✅ Day 90 of 100 Days LeetCode Challenge Problem: 🔹 #476 – Number Complement 🔗 https://lnkd.in/gzE6gM7d Learning Journey: 🔹 Today’s problem focused on finding the complement of a number by flipping its binary bits. 🔹 I first converted the integer to its binary representation using bin(num)[2:]. 🔹 Then, I created a helper function to flip each bit: • '0' → '1' • '1' → '0' 🔹 After generating the flipped binary string, I converted it back to an integer using int(..., 2). 🔹 Returned the final complemented value. Concepts Used: 🔹 Binary Representation 🔹 Bit Manipulation 🔹 String Traversal 🔹 Base Conversion Key Insight: 🔹 The complement operation is essentially a bitwise NOT, but only within the significant bits of the number (ignoring leading zeros). 🔹 Converting to binary simplifies the flipping logic for beginners. Complexity: 🔹 Time: O(log n) 🔹 Space: O(log n) #LeetCode #Algorithms #DataStructures #CodingInterview #100DaysOfCode #Python #ProblemSolving #LearningInPublic #TechCareers

✅ Day 92 of 100 Days LeetCode Challenge Problem: 🔹 #2011 – Final Value of Variable After Performing Operations 🔗 https://lnkd.in/gX-JQNUJ Learning Journey: 🔹 Today’s problem was about evaluating a sequence of increment and decrement operations. 🔹 I initialized a variable ans = 0 to track the value. 🔹 Used a hashmap to map each operation to its effect: • "++X" and "X++" → +1 • "--X" and "X--" → -1 🔹 Iterated through the operations and updated ans accordingly. 🔹 Returned the final computed value. Concepts Used: 🔹 HashMap / Dictionary 🔹 String Matching 🔹 Simple Simulation Key Insight: 🔹 Instead of using multiple condition checks, mapping operations to values simplifies logic and improves readability. Complexity: 🔹 Time: O(n) 🔹 Space: O(1) #LeetCode #Algorithms #DataStructures #CodingInterview #100DaysOfCode #Python #ProblemSolving #LearningInPublic #TechCareers

🚀 Day 12 & 13 – Consistency is the Key! Still going strong on my Python learning journey, and these two days were all about revision + real application 💻 🔁 Quick Revision: Revisited core concepts like loops, functions, and conditionals — because strong basics = strong foundation. 💡 Mini Project: Bill Generator Built a simple yet practical Python project using: ✔️ if-elif-else statements ✔️ Operators (arithmetic & logical) ✔️ User inputs for dynamic calculations 🔹 Features included: - Item selection & pricing - Quantity-based calculations - Discount logic - Final bill generation 🧠 What I Improved: - Better problem-solving approach - Writing cleaner, more readable code - Debugging with more confidence - Thinking in a more structured, logical way Every small project is making me more confident and bringing me one step closer to becoming a skilled data professional 📈 🙏 Special thanks to Anurag Srivastava and the Data Engineering Bootcamp for the constant guidance and support! #Python #LearningJourney #100DaysOfCode #DataEngineering #Coding #BeginnerToPro #Consistency

🚀 𝐏𝐲𝐭𝐡𝐨𝐧 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬 – 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥𝐬 & 𝐋𝐨𝐨𝐩𝐬 Continuing my Python learning journey 🐍 by strengthening core programming concepts that are essential for data science, AI, and problem-solving. 📚 𝐖𝐡𝐚𝐭 𝐈 𝐞𝐱𝐩𝐥𝐨𝐫𝐞𝐝: 🔀 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬 • if, elif, else for decision making • Writing logic based on real-world conditions • Example: weather check, grading system 🔁 𝐋𝐨𝐨𝐩𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 ➡️ 𝐅𝐎𝐑 𝐋𝐨𝐨𝐩 • Used when number of iterations is known • Efficient for repeating tasks ➡️ 𝐖𝐇𝐈𝐋𝐄 𝐋𝐨𝐨𝐩 • Runs until a condition becomes false • Useful for dynamic and condition-based tasks 💡 𝐊𝐞𝐲 𝐋𝐞𝐬𝐬𝐨𝐧: Programming is all about logic and repetition. Mastering these basics helps build strong foundations for advanced coding and real-world applications. 📈 Every small step in learning brings you closer to becoming a better developer and problem solver. #Python #Programming #DataScience #AI #LearningJourney #Coding #TechSkills

🚀 Day 11/111 — Diving Deeper into NumPy Today I explored array indexing, slicing, and data types in NumPy, and things are starting to feel much more powerful and precise 📊 🔹 What I learned: • How to access specific elements using indexing • How slicing works to extract parts of arrays • Understanding different NumPy data types (int, float, etc.) • How data type affects memory and performance 💡 Key takeaway: Indexing and slicing make it possible to work with exact portions of data instead of the whole dataset, which is super useful for real-world data analysis. Also, learning about data types showed me that even small details like choosing int vs float can impact efficiency and behavior. It’s getting clearer how NumPy is not just about storing data, but about working with it intelligently, appreciating the help, w3schools.com 🙏 Still learning step by step, but it feels like things are connecting more now. On to the next one 🚀 Code for Change #111daysoflearningforchange #day11 #python #codeforchange

Merge In Between Linked Lists — and got it Accepted ✅ This problem really tested my understanding of: 🔹 Linked List traversal 🔹 Pointer manipulation 🔹 Edge case handling One small mistake in pointer connection... and everything breaks. 😅 But that’s where real learning happens. 💡 Key takeaway: In linked lists, it’s not about values—it's about how you connect nodes. Step by step, I’m getting stronger in data structures & algorithms and building the problem-solving mindset needed for top tech roles. 🔥 Consistency is the real game changer. #LeetCode #DSA #ProblemSolving #Python #CodingJourney #SoftwareDeveloper #FullStackDeveloper #KeepLearning

This week I spent 2 hours debugging a pipeline that broke because of a subtle mutable default argument. Last week I finished DataCamp's "Intermediate Python for Developers" - and guess what chapter was in there. Funny how that works sometimes. A few takeaways that'll stick with me: • Mutable defaults are a trap, even for people who "know Python" • Decorators aren't magic - they're just functions returning functions (but the mental model matters) • Comprehensions > loops, until they don't fit on one screen anymore Working with Python daily on dbt models, and data transformations, it's easy to get comfortable in a narrow slice of the language. Stepping back to revisit the fundamentals consistently makes my production code cleaner. What's your approach - do you block time for structured learning, or learn purely on the job? #Python #DataEngineering #LearningInPublic

💡 From idea → execution 🚀 Ever wondered how chatbots fetch real-time data? I built one! Introducing my Python Weather Chatbot 🌤️ It takes user input and instantly responds with live weather updates. 🔧 What I used: Python + Weather API 🎯 What I learned: Real-world problem solving & API integration Small project. Big learning. 💯 Let’s connect and grow together 🤝 #PythonDeveloper #Projects #Chatbot #LearningByDoing #TechJourney

🚀 Day 7 of #30DaysOfLeetCode Challenge Continuing my consistency journey as a Python Developer, with a strong focus on Data Science! ✅ Today’s Problem: Roman to Integer 🔍 Platform: LeetCode 💡 Approach: Solved this problem using a right-to-left traversal approach. Stored Roman values in a dictionary and iterated through the string in reverse. If the current value is smaller than the previous value, it is subtracted; otherwise, it is added. 👉 Simple Explanation: We read the string from right to left. If a smaller numeral appears before a larger one (like IV), we subtract it; otherwise, we add it. This way, we can convert the entire Roman number into an integer. ⏱️ Time Complexity: O(n) 📌 Key Learning: Recognizing patterns and choosing the right traversal direction makes problem solving easier. Using a dictionary keeps the code efficient and clean! Consistency is making me better every day 🚀 #Python #DataScience #LeetCode #ProblemSolving #CodingJourney #30DaysOfCode

Day 9 of #100DaysOfCode – Mastering Lists in Python 🐍 Today’s focus was completely on one powerful concept: 👉 Lists – the backbone of data handling in Python Instead of jumping between topics, I went deep into list operations and logic building 💻🧠 ✨ What I practiced today (Programs 101–115): 🔹 Core list operations ✔️ Sum, product, count of elements ✔️ Finding largest & smallest (without built-ins) ✔️ Second largest & second smallest 🔹 Logical problem solving ✔️ Count even & odd numbers ✔️ Separate positive & negative values ✔️ Find indices of elements 🔹 Real-world list handling ✔️ Remove duplicates (without set) ✔️ Reverse list using loop ✔️ Copy list manually ✔️ Rotate list 💡 Key Learning: Lists are not just collections… They are the foundation for solving real-world problems Today helped me understand: 👉 How to think without built-in shortcuts 👉 How logic works behind the scenes 🔥 The more I practice, the more confident I feel in problem solving 🙏 Special thanks to Global Quest Technologies (GQT) for continuous support and guidance throughout this journey 💬 One step closer to becoming a better developer every day Global Quest Technologies ✨ #100DaysOfCode #Day9 #Python #PythonProgramming #CodingJourney #LearnPython #DataStructures #ListsInPython #ProblemSolving #DeveloperMindset #TechSkills #SoftwareDevelopment #Consistency #GlobalQuestTechnologies #GQT