Two Sum Problem Solved - Data Structures & Algorithms Practice

Imagine trying to explain a complex 3D object using just one photo. Pick the wrong angle, and you might lose critical detail. The same thing happens when you try to simplify high-dimensional data. If you just drop a random axis to make it 2D, you could throw away crucial information and end up with a tangled mess. Enter Principal Component Analysis (PCA)! Instead of randomly dropping data, PCA rotates your entire coordinate system to find the absolute "best camera angle". Watch our quick 60-second visual breakdown below! 👇 if you want to dive deeper into the math behind the magic and get the Python code, Watch the full tutorial here: https://lnkd.in/gdGkEw8r #PCA #MachineLearning #DataScience #DataVisualization #Schovia #Shorts

𝗜 𝗳𝗼𝘂𝗻𝗱 𝗮 𝟰𝟰× 𝘀𝗽𝗲𝗲𝗱 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗶𝗻 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝘀𝘂𝗺() 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 🚀 I benchmarked three ways of summing 100,000 numbers: • Manual for loop → ~11.4 ms • Built-in 𝘀𝘂𝗺() → ~8.27 ms • 𝗻𝗽.𝘀𝘂𝗺() → ~0.259 ms 𝗡𝘂𝗺𝗣𝘆 𝘄𝗮𝘀 ~𝟰𝟰× 𝗳𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗮 𝗣𝘆𝘁𝗵𝗼𝗻 𝗹𝗼𝗼𝗽 ⚡ The real insight isn’t that “NumPy is faster.” It’s about execution layers. A Python loop runs inside the interpreter with dynamic checks every iteration. 𝘀𝘂𝗺() shifts the work into C. 𝗻𝗽.𝘀𝘂𝗺() operates on contiguous memory using optimized low-level code, avoiding Python-level iteration entirely. Same computation. Different execution layer. Massive performance gap. #Python #NumPy #DataScience #LearningInPublic

𝐓𝐨𝐩 𝐒𝐞𝐚𝐛𝐨𝐫𝐧 𝐏𝐥𝐨𝐭𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐌𝐮𝐬𝐭 𝐊𝐧𝐨𝐰 𝐢𝐧 𝟐𝟎𝟐𝟔 Data analysts rely heavily on visualizations to understand patterns hidden inside datasets. Python’s Seaborn library simplifies statistical visualization and helps analysts create clear, attractive charts with minimal code. This guide explains the most important Seaborn plots every data analyst should know in 2026. From scatter plots to heatmaps, these visualizations help uncover trends, correlations, and patterns quickly. #DataAnalytics #PythonVisualization #SeabornPlots #DataScience #PythonProgramming #analyticsinsight #analyticsinsightmagazine Read More 👇 https://zurl.co/mvmNa

Don't flatten what naturally has structure. It's tempting to model everything in a single class. Easy to write, easy to read, at least until your data grows. This is where most codebases start, with just one model. But with model composition, each model has a single responsibility. And Pydantic handles nested validation automatically. Structure your models the way your domain is actually structured. The code gets cleaner, the errors get clearer, and reuse becomes obvious. This and other real-world modelling patterns are covered in Practical Pydantic: 👉 https://lnkd.in/eGiB7ZxU Model your domain. Not just your data. #Python #Pydantic #Data #Models #Patterns

Data isn't just 3D. Often, it’s 10-dimensional, 100-dimensional, or more. How do you find patterns when you can't even visualize the space? Enter Principal Component Analysis (PCA). In our latest video, Dr. Sindhu Ghanta demystifies PCA in 3 simple steps to help you collapse high-dimensional complexity into actionable insights: - The Geometric Intuition behind the best angles - The Math Under the Hood (simplified!) - Practical Pitfalls and when PCA actually fails Watch the full breakdown and grab the Python notebook to try it yourself! 👇 ▶️ Watch: https://lnkd.in/gdGkEw8r 👨💻 Code: https://lnkd.in/gUQmiDkp #MachineLearning #DataScience #PCA #Python #Schovia

𝗧𝗵𝗲 𝗴𝗿𝗮𝗽𝗵 𝗹𝗼𝗼𝗸𝗲𝗱 𝘀𝗶𝗺𝗽𝗹𝗲. 𝗧𝗵𝗲 𝗰𝗼𝗱𝗲 𝘁𝗵𝗮𝘁 𝗯𝘂𝗶𝗹𝘁 𝗶𝘁 𝗱𝗶𝗱𝗻'𝘁. Day 22 of #1000DaysOfLearning 🗓️ Today I plotted my first graph in matplotlib — a 𝘀𝗰𝗮𝘁𝘁𝗲𝗿 𝗽𝗹𝗼𝘁. 📊 What I worked through: → plt.scatter() vs plt.plot() — and what each communicates → Controlling 𝗺𝗮𝗿𝗸𝗲𝗿 𝘀𝗶𝘇𝗲, 𝗰𝗼𝗹𝗼𝗿, 𝗹𝗮𝗯𝗲𝗹𝘀, 𝘁𝗶𝘁𝗹𝗲𝘀, 𝗮𝗻𝗱 𝗹𝗲𝗴𝗲𝗻𝗱𝘀 → Grouping data points using slicing and color lists The code gets long for what looks like a simple output. But 𝘁𝗵𝗮𝘁 𝗹𝗲𝗻𝗴𝘁𝗵 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 — every label, every color, every legend entry is a deliberate line. Matplotlib assumes nothing. 🎯 Also noticed that 𝘇𝗶𝗽 𝗮𝗻𝗱 𝘁𝘂𝗽𝗹𝗲 𝘂𝗻𝗽𝗮𝗰𝗸𝗶𝗻𝗴, which felt less useful in regular Python, come up naturally when working with coordinate data. Made more sense here than any time I saw them before. 💡 #Python #DataScience #Matplotlib #DataVisualization #LearningInPublic

😎 One of the Most Clever DSA Questions — 𝐏𝐚𝐫𝐭 𝟑# ▶️ 𝐕𝐚𝐥𝐢𝐝 𝐏𝐚𝐫𝐞𝐧𝐭𝐡𝐞𝐬𝐞𝐬 An easy problem, but it really tests your logical thinking and understanding of Stack data structure. 💡 𝐈𝐍𝐓𝐔𝐈𝐓𝐈𝐎𝐍 ------------ Take a 𝐡𝐚𝐬𝐡𝐦𝐚𝐩 of closing → opening brackets. Iterate over the string s. If you encounter an opening bracket, push it into the stack. If you encounter a closing bracket, check the top of the stack. Pop the previous opening bracket and compare it with the expected one from the hashmap. If they don’t match, the string is invalid. At the end, if the stack is empty, the parentheses are valid. ⚡ Time Complexity: O(n) ⚡ Space Complexity: O(n) Follow Muralikrishna Devarakonda for more 💛... #DSA #LeetCode #CodingInterview #Algorithms #Python #SoftwareEngineering

Data analysis often goes wrong before the analysis even begins. The ingestion step: where data is pulled from databases, web sources, and APIs: is where silent errors go undetected. Duplicates, nulls, schema mismatches. Episode 3 of the Practical Learning Series covers the patterns, the validation checklist, and the mistakes to avoid. Because reliable analysis starts with trustworthy data. Swipe through → #DataScience #Python #PracticalLearning #Analytics #DataManagement #DataScienceInstitute

𝗢𝗢𝗣𝗦 𝘄𝗮𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗼𝘀𝗲 𝘁𝗼𝗽𝗶𝗰𝘀 𝗜 𝗸𝗲𝗽𝘁 𝗿𝗲𝘃𝗶𝘀𝗶𝘁𝗶𝗻𝗴 — 𝗻𝗼𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗜 𝗳𝗼𝗿𝗴𝗼𝘁 𝗶𝘁, 𝗯𝘂𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗜 𝗻𝗲𝘃𝗲𝗿 𝗳𝘂𝗹𝗹𝘆 𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝗺𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗼𝗳 𝗶𝘁. Read about it from multiple sources. Understood the syntax, could follow the examples. But the mental model was always shaky. College lectures sorted that — not by covering something new, but by connecting things in the right order. Inheritance types, method resolution, how one class builds on another — it started making structural sense rather than just syntactic sense. Revisited it all. Coded through single, multi-level, hierarchical, and hybrid inheritance. And for the first time it felt like I actually owned the concept, not just recognized it. #Python #OOP #CS #DataScience

Spent today exploring pandas while starting work with the MovieLens dataset for a recommendation systems project. A few small observations from the process: • pandas makes it incredibly easy to move from raw CSV files to structured data exploration • building a user–movie matrix is just a pivot operation away • debugging environments in VS Code can be surprisingly tricky when working with virtual environments The most interesting part for me was realizing how quickly you can move from: raw rating logs → structured dataset → matrices suitable for recommendation algorithms. Next step: experimenting with similarity-based recommendations using the dataset. Small progress today, but the foundation for something much bigger. Challenge : what pandas method gave the output in the terminal 🙃 🙃 #MachineLearning #DataScience #Python #RecommenderSystems