Optimize Data Storage with Binary Trees

Day 111 Backtracking is starting to feel consistent now. #Day111 🧩 39. Combination Sum How today went: • Used backtracking with index i • Two choices: → stay at i (reuse the same element) → move to i + 1 (try next element) • Track the current total • If total == target → add result • If total > target → stop that path What I realized: This problem is about: → controlling index movement → managing the running total Same pattern, different control. Revision is making it clearer. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

A single train-test split can make a weak model look strong. Cross-validation solves this by evaluating performance across multiple time windows. You train up to a cutoff, forecast forward, shift the cutoff, and repeat. But doing this for multiple models means writing separate loops, managing cutoffs, and combining outputs manually. TimeCopilot removes that overhead. With one method call, you get predictions from every model across every fold. Statistical models, foundation models, naive baselines. All evaluated together without separate pipelines. 🚀 Learn how to use cross-validation to compare foundation models in this example: https://lnkd.in/gx6QmA4S #TimeSeries #Forecasting #Python #CrossValidation

DSA Tip: Hash Table If you’re searching through data one by one… there’s a faster way. Use a Hash Table. It uses a key -> index mapping to store and retrieve data instantly. No full scanning. No long searches. Just direct access. Used in: Dictionaries Caching Databases Insight: The right key can turn searching into instant access. Quick Challenge: What happens when two keys map to the same index? Drop your answer, I’ll review the best ones. FOLLOW FOR MORE DSA TIPS & INSIGHTS #DSA #HashTable #Python #CodingTips #LearnToCode

Day 106 Some problems feel simple when the pattern clicks. #Day106 🧩 17. Letter Combinations of a Phone Number How today went: • Used a digit → letters map • Built combinations using backtracking • Maintained a string path at each step • One recursive call per choice — no need for complex state handling What I realized: Once you understand the pattern: → choose a letter → move to next digit → build the path Backtracking becomes very natural. Simple problem, but great for building confidence. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

Nobody Cares How You Built It 🐍 Spent days on an analysis. Custom functions. Optimized queries. Clean, modular code. Every edge case handled. Then dashboard is presented. The stakeholder looked at the output for 30 seconds and asked one question: "So, what does this mean for next quarter?" Not a single question about the method. Not a word about the code. They didn't care how it’s built. They never do. What they care about is the answer. The implication. What happens next. 👉 The analysis is not the deliverable. The decision it enables is. 👉 Lead with the answer. Save the method for when someone asks. #DataAnalytics #Python #AnalyticsThinking #LearningInPublic

Day 13/100 – Understanding Before Solving Today’s problem: Reverse String At first glance, it looks simple—but the real goal is to understand how to solve it efficiently. The challenge is to reverse the string in-place, meaning without using extra memory. Approach Explained: We use the two-pointer technique: One pointer starts from the beginning One from the end Swap characters and move inward This avoids creating a new array and keeps the solution optimal. Time Complexity: O(n) Space Complexity: O(1) Key Learning: Writing code is important, but understanding why it works is what truly builds strong problem-solving skills. One step closer to mastering DSA. #100DaysOfCode #DSA #Learning #ProblemSolving #Python #LeetCode #GrowthMindset

Day 109 Backtracking patterns are repeating again — and that’s a good sign. #Day109 🧩 78. Subsets How today went: • Used recursion to explore all elements • At each step, decide to include or skip the current element • Append current subset → explore → then pop to backtrack • Move to the next index and repeat What I’m noticing: Subsets is one of the cleanest backtracking patterns: → choose → explore → undo Another revision day, but clarity is improving. Consistency continues. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

INSTEAD OF WASTING TIME AND TRYING TO GET FIGURES. WHY NOT USING CODE?? Sometimes, lecturers or organizations need to generate different sets of questions for multiple candidates, especially when working with matrices. However, this often requires a lot of manual effort and can be time-consuming. Why not simplify the process using NumPy in Python? With just a few lines of code, you can easily generate multiple variations of matrix-based questions efficiently and save valuable time. #randint is an inbuilt function of the random module of numpy #Syntax: np.random.randint(start, stop (rows, columns)) a=np.random.randint(2,30, (3,3)) b=np.random.randint(2,30, (3,3)) c=np.random.randint(2,30, (3,3)) d=np.random.randint(2,30, (3,3)) e=np.random.randint(2,30, (3,3)) #DataScience #Python #NumPy #Education #Automation

𝗬𝗼𝘂𝗿 𝗰𝗼𝗹𝘂𝗺𝗻 𝗻𝗮𝗺𝗲𝘀 𝗺𝗶𝗴𝗵𝘁 𝗯𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝘀𝗼𝗻 𝘁𝗵𝗶𝗻𝗴𝘀 𝗳𝗲𝗲𝗹 𝗺𝗲𝘀𝘀𝘆 I didn’t realize this at first. I had columns like: UserName, User name, USER_NAME Everything worked… until it didn’t. Small mistakes. Confusing errors. Wasted time. Then I started doing one simple thing: Normalize column names. lowercase snake_case consistent user_name → simple, clear, reliable. It feels small. But it makes your work much easier. Have you faced this problem before? 👇 #DataEngineering #DataScience #Python #Pandas #DataAnalytics

📅 7 April 2026 Solved Median Finder (Two Heaps) problem today on LeetCode. Initially, I was getting wrong answers even though the logic looked correct. The mistake? Comparing directly with a max heap stored as negative values. 🔍 Key Learning: When using a max heap in Python (via negative values), always convert back before comparison. ❌ Wrong: num < self.left[0] ✅ Correct: num <= -self.left[0] 💡 Approach: - Use two heaps: • Max Heap (left) → stores smaller half • Min Heap (right) → stores larger half - Balance both heaps after every insertion - Median depends on heap sizes 📈 Time Complexity: - addNum → O(log n) - findMedian → O(1) Small bug, big learning 🚀 Consistency in DSA is what builds clarity. #leetcode #dsa #python #heaps #codingjourney #problemSolving