🔢 NumPy Practice Notebook
A hands-on practice notebook focused on mastering NumPy, the fundamental library for numerical computing in Python. This repository documents my learning journey and practical exercises to build strong foundations for data science and machine learning.
📌 Project Purpose
The goal of this notebook is to:
Develop a strong understanding of NumPy arrays
Practice efficient numerical computations
Build a vectorization mindset for performance
Strengthen core skills required for data science and ML
🛠 Topics Covered ✅ Array Fundamentals
Creating arrays
Data types
Shape and dimensions
Reshaping arrays
✅ Indexing & Slicing
Basic and advanced indexing
Boolean masking
Subsetting data
✅ Broadcasting
Rules of broadcasting
Element-wise operations
Practical examples
✅ Stacking & Splitting
vstack, hstack
concatenate
split
✅ Linear Algebra Basics
Dot product
Matrix multiplication
Determinant and inverse
Eigenvalues (intro level)
✅ Performance Optimization
Why NumPy is faster than loops
Vectorization techniques
Efficient computations
🚀 Key Learning Outcomes
Improved problem-solving using arrays
Ability to write faster, optimized code
Better understanding of numerical operations
Stronger foundation for ML libraries like pandas, scikit-learn, and TensorFlow
📂 Repository Structure numpy-practice.ipynb README.md
🎯 Who This Is For
Beginners learning NumPy
Data science students
Anyone strengthening Python numerical skills
👤 Author
Shorya Bisht Data Scientist | Analytics Enthusiast Passionate about learning and applying data-driven skills. https://www.linkedin.com/in/shorya-bisht-a20144349/