HR Sanskar A.’s Post

📊 Numerical Computing in Python Python is one of the most powerful tools for scientific computing and data analysis. With libraries like NumPy, SciPy, Pandas, and Matplotlib, developers can easily perform complex calculations, analyze large datasets, and build data-driven models. From data science and machine learning to finance and engineering simulations, numerical computing plays a critical role in modern technology. I wrote a short article explaining numerical computing in Python and the key libraries every beginner should know. Read the full article here 👇 https://lnkd.in/djNSUnva #Python #DataScience #NumericalComputing #MachineLearning

Check out the Statistics Globe Hub: https://lnkd.in/e5YB7k4d The Statistics Globe Hub is an ongoing learning program that helps you stay up to date with statistics, data science, AI, and programming using R and Python. #randomforest #featureselection #machinelearning #datascience #rstats #statisticsglobehub

As part of my continuous learning journey in Python, Data Analysis, and Artificial Intelligence (AI), I documented and published my Python Libraries notes on GitHub. These notes cover key libraries: NumPy for numerical computing, Pandas for data manipulation and analysis, Matplotlib and Seaborn for data visualization and creating meaningful insights from data. 💻 Python Libraries Notes 🔗 HTML version: https://lnkd.in/dUV83GYF 🔗 PDF version: https://lnkd.in/deJvpWPi Continuing to build my skills in Data Analysis and AI by learning and sharing knowledge. 🚀 #Python #DataAnalysis #ArtificialIntelligence #NumPy #Pandas #DataVisualization #LearningJourney

Check out the Statistics Globe Hub: https://lnkd.in/e5YB7k4d The Statistics Globe Hub is an ongoing learning program that helps you stay up to date with statistics, data science, AI, and programming using R and Python. #ggstatsplot #datavisualization #statistics #datascience #rstats #statisticsglobehub

Check out the Statistics Globe Hub: https://lnkd.in/exBRgHh2 The Statistics Globe Hub is an ongoing learning program that helps you stay up to date with statistics, data science, AI, and programming using R and Python. #randomforest #featureselection #machinelearning #datascience #rstats #statisticsglobehub

This week, I continued my learning journey into a deeper level: Advanced Python and an introduction to NumPy as a fundamental tool for data processing. At this stage, I started to understand how Python goes beyond simple scripting and can efficiently handle more complex operations—especially when working with large-scale data. With NumPy, numerical computations become faster and more structured, from handling multidimensional arrays to performing optimized mathematical operations. This learning experience has broadened my perspective on how data is processed behind the scenes, particularly in data science and machine learning. I’ve summarized these materials into a slide deck for easier understanding. Feel free to check out the PPT here 👇 Digital Skola #DigitalSkola #LearningProgressReview #DataScience

Check out the Statistics Globe Hub: https://lnkd.in/exBRgHh2 The Statistics Globe Hub is an ongoing learning program that helps you stay up to date with statistics, data science, AI, and programming using R and Python. #ggstatsplot #datavisualization #statistics #datascience #rstats #statisticsglobehub

🚀 Day 9 of My Python Learning Journey Today, I explored NumPy — a powerful library for numerical computing in Python 🐍 Here’s what I learned: ✔️ Creating and working with arrays ✔️ Performing fast mathematical operations ✔️ Understanding why NumPy is faster than regular Python lists I realized how efficiently large datasets can be handled using NumPy, making it a core tool for data analysis and machine learning 💡 This step brought me closer to understanding how real-world data is processed at scale. Excited to continue exploring more libraries and build practical projects 🚀 Consistency is turning into confidence! If you have tips or resources for mastering NumPy, feel free to share 🙌 #Python #NumPy #DataScience #Day9 #LearningJourney #Coding #Programming #Growth

Check out the Statistics Globe Hub: https://lnkd.in/exBRgHh2 The Statistics Globe Hub is an ongoing learning program that helps you stay up to date with statistics, data science, AI, and programming using R and Python. #quarto #rstats #datascience #reproducibility #reporting #statisticsglobehub

Weekly Challenge 11: K-Nearest Neighbors You don't always need massive libraries like scikit-learn to do Machine Learning. Sometimes, the best way to truly understand an algorithm is to build its core logic yourself! For Week 11 of my Python coding challenge, I implemented the K-Nearest Neighbors (KNN) algorithm purely with math and Python. KNN is essentially a voting system based on proximity. 1 A new, unknown data point enters the space (the green star). 2 We calculate the Euclidean distance to EVERY other point. 3 We find the "K" closest neighbors (in this case, 5). 4 The neighbors vote! If the majority are Blue, the new point becomes Blue. It’s a beautiful mix of geometry, sorting algorithms, and data structures. I used Matplotlib to visualize how the algorithm "connects" the unknown point to its closest peers to make a decision. Full source code on my GitHub: https://lnkd.in/eV-FieS2 #MachineLearning #Python #DataScience #ArtificialIntelligence #KNN #Algorithms #CodingChallenge #UANL