Sikander Shah’s Post

I used to think NumPy was just another Python library… until I understood this 👇 NumPy is all about working with arrays efficiently. Instead of using normal Python lists, NumPy lets you handle data faster and smarter. Think of it like this: A Python list = normal road 🚶♂️ NumPy array = highway 🚀 For example: If you want to add 10 to every number In Python list: You loop through each element In NumPy: 👉 It happens in one line That’s the power. NumPy is heavily used in: - Data Science - Machine Learning - Data Engineering If you're working with data, learning NumPy is not optional. It makes your code faster, cleaner, and more efficient. What confused you the most when you started NumPy? #NumPy #Python #DataScience #MachineLearning #DataEngineering #CodingJourney #TechLearning

I always heard: “NumPy is faster than Python lists.” But today, I tested it myself 👇 Day 8 of my Data Science Journey 🚀: I added 1,000,000 elements using: 🔹 Python lists 🔹 NumPy arrays 📊 Result? NumPy was significantly faster. 💡 Why this happens: NumPy uses vectorized operations and runs on optimized C code, avoiding slow Python loops. 👉 This is why NumPy is the backbone of Data Science & Machine Learning. Small step today, but building real understanding. #DataScience #Python #NumPy #LearningInPublic #Day8

Day 10/30 – Exploring NumPy Today I explored NumPy, the backbone of numerical computing in Python. Why NumPy? NumPy makes working with arrays fast, efficient, and way more powerful than traditional Python lists. What I learned: - Creating and manipulating arrays (ndarray) - Performing fast mathematical operations (element-wise calculations) - Understanding broadcasting to apply operations without loops - Working with multi-dimensional arrays - Using built-in functions for mean, median, standard deviation Key Takeaways: - NumPy is highly optimized → faster than lists - Reduces the need for manual loops - Forms the base for libraries like Pandas, Matplotlib, and ML frameworks From simple calculations to complex data processing, NumPy simplifies everything. A must-know library for anyone stepping into Data Science or Machine Learning #Python #NumPy #DataScience #MachineLearning #CodingJourney

Discover the top 10 Python machine learning libraries for data science, including scikit-learn, TensorFlow, and PyTorch, and learn how to choose the right library for your project. https://lnkd.in/g3FcG39a #PythonMachineLearningLibraries Read the full article https://lnkd.in/g3FcG39a

One thing that completely changed how I think about data 👇 👉 Writing code vs Designing for scale In Python: You solve problems on a single machine In Spark: You solve problems across a cluster of machines Same problem. Totally different thinking. Example: - Python → Loop through list and calculate sum - Spark → Use distributed transformations like "map" and "reduce" The real shift is: ❌ “How do I write this function?” ✅ “How will this run across multiple nodes efficiently?” This is where many developers struggle when moving to Big Data. It’s not about syntax. It’s about distributed thinking. Learning this the hard way, but enjoying the process 🚀 #DataEngineering #BigData #Spark #LearningInPublic

🚀 Python Practice – NumPy Continuing my Python learning journey by stepping into data analysis tools 📊🐍 In this session, I explored NumPy: ✔️ Creating arrays (1D & 2D) ✔️ Array operations and indexing ✔️ Mathematical operations on arrays ✔️ Reshaping and slicing arrays Practiced using NumPy for efficient numerical computations and handling large datasets compared to regular Python lists. Understanding NumPy is helping me work with data faster and perform calculations more efficiently 💡 A big thanks to Krish Naik for his amazing teaching and guidance 🙌 Documented my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Excited to move closer to real-world data analysis 🚀 Next: Pandas and working with datasets 📈 #Python #NumPy #DataAnalytics #LearningJourney #Coding #KrishNaik

Days 66–67 of the #three90challenge 📊 The last two days were about strengthening fundamentals + stepping into data-focused Python. 📅 07-04-2026: Review Day Revisited core Python concepts: • Variables, data types, lists & dictionaries • Loops and functions • File handling Focused on writing cleaner code and connecting concepts together. 📅 08-04-2026: Started NumPy basics 🧮 Entered the world of numerical computing with Python. What I learned: • Working with arrays instead of lists • Faster and more efficient data operations • Performing basic mathematical computations Big realization: Python basics build logic. NumPy starts building data processing power. Step by step, moving closer to real data analysis 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #NumPy #DataAnalytics #LearningInPublic #Consistency #Upskilling

Discover the top 10 Python machine learning libraries for data science, including scikit-learn, TensorFlow, and PyTorch, and learn how to choose the right library for your project. https://lnkd.in/gbxPHrKH #PythonMachineLearningLibraries Read the full article https://lnkd.in/gbxPHrKH

Discover the top 10 Python machine learning libraries for data science, including scikit-learn, TensorFlow, and PyTorch, and learn how to choose the right library for your project. https://lnkd.in/gbxPHrKH #PythonMachineLearningLibraries Read the full article https://lnkd.in/gbxPHrKH

Discover the top 10 Python machine learning libraries for data science, including scikit-learn, TensorFlow, and PyTorch, and learn how to choose the right library for your project. https://lnkd.in/gbxPHrKH #PythonMachineLearningLibraries Read the full article https://lnkd.in/gbxPHrKH