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

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

Merge Alternately: Two-Pointer Zipper Pattern with Remainder Handling Alternating merge requires tracking positions in both strings independently. Two pointers advance together while both strings have characters, then append any remainder from the longer string. Using list append + join avoids O(n²) string concatenation overhead. String Building Optimization: Python strings are immutable — repeated + creates new strings each time (O(n²)). List append + final join is O(n). This pattern is critical for any string construction in loops. Time: O(n + m) | Space: O(n + m) #TwoPointers #StringBuilding #MergePatterns #PythonOptimization #SoftwareEngineering

🚀 Day 42/100 – LeetCode Challenge Solved 206. Reverse Linked List today! 🔍 Problem Summary: Given the head of a singly linked list, reverse the list and return the new head. 💡 Approach: Implemented an iterative solution using three pointers (prev, curr, next) to reverse the links efficiently in-place. Also explored the recursive approach to strengthen understanding of linked list manipulation. ⚡ Complexity: • Time Complexity: O(n) • Space Complexity: O(1) (Iterative) 📌 Key Takeaways: Importance of pointer manipulation Handling edge cases like empty list Difference between iterative vs recursive approaches Consistency is key — one step closer to mastering Data Structures & Algorithms! 💪 #LeetCode #100DaysOfCode #DataStructures #Algorithms #CodingJourney #Python #ProblemSolving #LinkedList

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

Dates often look simple in a dataset. But to a machine, they are just strings until we tell it otherwise. In this project, I focused on date parsing and time-based feature extraction using Python and Pandas. Working with earthquake and landslide datasets, I explored how to: • Identify incorrect data types in date columns • Convert string-based dates into proper datetime format • Extract useful components such as the day of the month • Validate parsing results through distribution visualization The objective wasn’t just converting dates. It was understanding how correct data types enable time-based analysis, prevent errors, and make datasets usable for modeling and exploration. Sometimes, meaningful insights begin with something as simple as telling the system that a column is actually a date. #DataScienceJourney #DataCleaning #Python #Pandas #DataAnalysis #MachineLearning #LearningJourney

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

Pleased to see my article on how to use LLMs for data cleansing in The Actuary. The article explores not only a use case of LLMs for actuaries, but some techniques that you can use in Python to enhance performance beyond prompting. https://lnkd.in/eicGkbn8 Special thanks to Kay Khine Myo with whom I collaborated on this piece.

I’m delighted to have collaborated with Paul Beard on this piece. It was a great opportunity to deepen my understanding of AI techniques and develop versatile Python codes along the way. Thanks to his guidance and collaboration, I’m thrilled to see our article featured in The Actuary. In the article, we explore how large language models (LLMs) can be applied to data cleansing in the actuarial space, along with practical techniques using Python that go beyond prompting to improve performance and robustness. I’m excited to share this with the actuarial community and would love to hear your thoughts or experiences using LLMs in insurance or actuarial contexts. For the full article: https://lnkd.in/eicGkbn8

🚀 Day 54 of #100DaysOfCode 🧩 Problem: Minimum Number of Flips to Make the Binary String Alternating Today I solved an interesting problem involving string manipulation, rotations, and sliding window techniques. 💡 Key Idea: An alternating binary string can only follow two patterns: • "010101..." • "101010..." Since the problem allows rotating the string (moving the first character to the end), the trick is to consider all rotations efficiently. 🔑 Optimization Trick: Instead of rotating the string repeatedly, we can concatenate the string with itself ("s + s") and use a sliding window of size "n" to simulate all rotations. Then we compare each window with both alternating patterns and count the minimum flips required. ⚡ Concepts Used • Sliding Window • Greedy Thinking • String Pattern Matching • Optimization Trick ("s + s" rotation) 💻 Language: Python This problem was a great exercise in thinking about string rotations and pattern mismatches efficiently in O(n) time. 📈 Consistency is the key to improving problem-solving skills! #LeetCode #CodingChallenge #Python #DataStructures #Algorithms #ProblemSolving #100DaysOfCode