Complete NUMPY in oneshot in depth |

🚀 Master NumPy in Python: 20 Essential Exercises for Data Science & Interviews!**

Welcome to our Btech Coding Club, where we turn complex coding concepts into simple, actionable lessons. In this video, I’ll walk you through *20 must-know NumPy exercises* that cover everything from array manipulation to linear algebra—perfect for **beginners and interview prep**!

🔗 *Download the PDF Solutions:* [https://drive.google.com/drive/u/0/fo...]

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*📌 What You’ll Learn:*
✅ *Array Creation & Manipulation* – Random arrays, reshaping, and diagonal tricks.
✅ *Indexing & Slicing* – Extract sub-arrays like a pro.
✅ *Statistical Operations* – Mean, median, variance, and normalization.
✅ *Broadcasting & Linear Algebra* – Matrix multiplication, determinants, and eigenvalues.
✅ *Advanced Tricks* – Fancy indexing, masked arrays, and structured data.

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*🔥 Why Watch This Video?*
*Hands-on Practice**: No fluff—just **real-world examples* you’ll use in data science.
*Interview Ready**: These exercises are **common in technical interviews* (FAANG included!).
*Free Cheat Sheet**: Grab the **PDF solutions* to revise anytime!

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*📂 Timestamps:*
00:00 – Intro & Why NumPy?
02:15 – Array Creation (Exercise 1-2)
05:30 – Slicing & Indexing (Exercise 3-4)
09:45 – Statistical Operations (Exercise 5-6)
13:20 – Broadcasting (Exercise 7-8)
16:50 – Linear Algebra (Exercise 9-10)
20:00 – Advanced Techniques (Masked Arrays, Structured Data)
23:40 – **BONUS**: Pro Tips for NumPy Optimization

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*💡 Who Is This For?*
*Python Beginners* transitioning to data science.
*Job Seekers* prepping for coding interviews.
*Data Analysts/Scientists* looking to *speed up workflows* with NumPy.

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*🚀 Subscribe for More!*
Hit *Subscribe* and the *🔔 bell* to join our coding community! Next up: **Pandas 10x Performance Hacks**.

📢 *Let’s Connect!*
*GitHub:* https://github.com/MehulPrajapati1
*Linkedin:*   / mehulprajapati-  
*Discord:* [Join for Q&A]



*💬 Feedback?*
What NumPy topic should I cover next? Drop a comment! 🎯

#Python #NumPy #DataScience #CodingInterview #MachineLearning #Programming

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