Mastering Python NumPy with KSR Datavizon

New Medium Article: Exploring Academic Footprints with Python! Recently, I’ve been exploring how to analyze academic profiles by using Python scraping on several platforms Google Scholar, Sinta, and Scopus. Through this small project, I tried to uncover how academic data can be collected and processed to reveal publication trends and researcher collaborations. 📄 Read for full article here: 👉 https://lnkd.in/gwdyc-nX If you’re interested in web scraping, academic analytics, or data exploration with Python, I hope this piece gives you a new perspective. Thanks for reading! #Python #WebScraping #DataAnalysis #GoogleScholar #SINTA #Scopus #Medium

🚀 Experiment No. 2 & 5: Central Tendency Analysis (Mean, Median, Mode, Variance, Standard Deviation) In this experiment, I analyzed a dataset using Python to calculate key statistical measures such as Mean, Median, Mode, Variance, and Standard Deviation. 🔹 Libraries Used: statistics, numpy, scipy 🔹 Concepts Covered: Calculation of central tendency measures Handling statistical data arrays Understanding dispersion through variance and standard deviation 📊 This experiment helped me strengthen my understanding of data summarization and variability — fundamental concepts in Data Science and Statistics. 🔗 Check out the full implementation and code on my [GitHub:https://lnkd.in/giHv2ua6] #DataScience #Python #Statistics #Numpy #Scipy #MachineLearning #CollegeProjects #GitHub

This week, I built a Python program using NumPy to handle a classic matrix problem: ✅ Create a 2D NumPy array of size 5x4 ✅ Generate its transpose ✅ Calculate column-wise mean and row-wise standard deviation ✅ Compute the dot product of the original matrix with its transpose While it might look simple, this small project taught me how NumPy handles mathematical operations efficiently — and how much power a few lines of code can have when optimized correctly. Here’s what I enjoyed most: Understanding how matrix transposition works at the data structure level Seeing mean and standard deviation come to life across axes Watching the dot product reveal matrix relationships in a clean, vectorized way If you’re also learning data manipulation or Python fundamentals, I’d love to connect and discuss ideas! 💬 #Python #NumPy #DataScience #Programming #AI #LearningJourney #TechStudent

📶 Experiment 9: K-Nearest Neighbors (KNN) Algorithm using Python 📊 In this lab, I explored the K-Nearest Neighbors (KNN) algorithm — a simple yet powerful instance-based learning technique used for both classification and regression tasks. 🔍 Key learning outcomes: • Understanding the concept of distance-based classification • Implementing KNN using scikit-learn • Choosing the optimal value of K for better accuracy • Evaluating model performance using various metrics • Visualizing decision boundaries and classification outcomes This experiment deepened my understanding of how KNN leverages similarity between data points to make accurate predictions, emphasizing the importance of feature scaling and data normalization. 📁 Explore the repository here :  👉 https://lnkd.in/epWys7e7 #DataScience #MachineLearning  #Python  #KNN  #ScikitLearn  #Classification  #DataAnalysis  #PredictiveModeling  #Statistics  #LearningJourney  #JupyterNotebook Ashish Sawant Sir

Exploring NumPy – The Powerhouse of Numerical Computing in Python 🚀 I recently completed a NumPy assignment that deepened my understanding of how data is efficiently handled and processed in Python. NumPy is the foundation for almost every data science and machine learning workflow — and this hands-on task was a great way to strengthen those fundamentals. Numpy_link:https://lnkd.in/eKYByhgc 🔹 Key Concepts Covered: ✅ Creating and manipulating arrays ✅ Performing mathematical and statistical operations ✅ Reshaping, slicing, and indexing arrays ✅ Working with random numbers and matrix operations ✅ Applying vectorized computations for faster processing ✨ Takeaway: NumPy is more than just an array library — it’s the engine that powers data analysis, machine learning, and scientific computing. This assignment helped me grasp how performance and precision can go hand-in-hand when dealing with large datasets. #NumPy #Python #DataScience #MachineLearning #DataAnalytics #Programming #Upskilling

Tech With Tim: Python Skills You NEED Before Machine Learning TL;DR Get your Python game on point before tackling ML! This video breaks down everything from core Python and data handling to essential software-engineering tools, optional math refreshers, and then ramps up into machine-learning foundations, deep learning, real-world projects—and even a bonus LLM section. By the end you’ll know exactly what to practice and how to showcase it. Along the way you’ll find two beginner-friendly DataCamp tracks (with an exclusive 25% off link) and an invite to DevLaunch’s hands-on mentorship, where you build real projects and actually land that dream data job. 🚀 #Python #MachineLearning #SoftwareEngineer Watch on YouTube https://lnkd.in/gFAgY78v

💻 Week 4 Progress Update – Data Science Using Python From AMU This week, I explored some of the most important data types in Python that form the foundation for data handling and manipulation :- 🔹 Strings – operations, slicing, and key methods 🔹 Lists – creation, modification, and list comprehensions 🔹 Tuples – immutability and data packing/unpacking 🔹 Sets – unique collections and set operations 🔹 Dictionaries – key-value storage and essential methods Each topic strengthened my understanding of how Python manages different kinds of data efficiently — a crucial skill for any data professional. #Python #DataScience #LearningJourney #DataScienceUsingPython #DataTypes #Upskilling #AI #Programming

Day 10 – PYTHON VARIABLES 🧠🐍 (MY TechRise cohort 2.0 journal). Today in my TechRise Cohort 2 journey, I learned about Python Variables — the building blocks of every program! Variables are like containers that hold data, and I explored different data types such as integers, floats, strings, booleans, and even complex numbers. I also practiced data type conversion in Python using simple code examples. Here’s a quick snippet from my learning: a = 10 k = float(a) p = complex(a) print(k) print(p) Every new lesson makes Python more exciting and practical for real-world AI and Machine Learning applications. 🚀 #TechRiseCohort2 #Python #AI #MachineLearning #CodingJourney #DigitalSkills

🚀 Exploring the Power of NumPy! Lately, I’ve been exploring how NumPy empowers Python to handle data with both precision and speed. What began as simple array manipulations soon unfolded into a deeper understanding of how data is represented, stored, and transformed efficiently. 💻 Exploring array creation, mathematical operations, and reshaping techniques revealed how NumPy streamlines complex computations and brings elegance to problem-solving in Python. 📂 Check out my complete work here: https://lnkd.in/grZgGSAV Some key takeaways from my exploration: 🔹 Efficient handling of large datasets using arrays 🔹 Vectorization for faster computation 🔹 Array slicing, indexing, and reshaping techniques 🔹 Real-world applications in analytics and AI Working with NumPy made me realize that it’s not just about numbers — it’s about logical thinking, optimization, and transforming raw data into insights 💡 KSR Datavizon #Python #NumPy #Numpyarrays #DataScience #MachineLearning #CodingJourney #Programming #DataAnalytics #LearningJourney