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📊 NumPy Guide for Data Science

Welcome to the NumPy for Data Science guide — your gateway to mastering numerical computing in Python. Whether you're diving into data analysis, machine learning, or scientific computing, NumPy is the foundation you’ll keep coming back to.


🚀 What is NumPy?

NumPy (Numerical Python) is the core library for numerical operations in Python. It allows you to work with powerful multi-dimensional arrays, perform fast mathematical computations, and unlock the performance needed for data science workflows.

🧠 Think of NumPy as the engine under the hood of nearly every major data science tool — from pandas and scikit-learn to TensorFlow and PyTorch.


🎯 Why Learn NumPy?

  • ✅ Speeds up large mathematical computations by orders of magnitude
  • ✅ Simplifies matrix operations, linear algebra, and broadcasting
  • ✅ Foundation for other data science and ML libraries
  • ✅ Helps in writing vectorized, clean, and memory-efficient code

📚 What You’ll Learn

This guide is structured through hands-on notebooks, each building your understanding step by step:

  • Day 1: Introduction to NumPy Link

    • Creating arrays
    • Basic math
    • Array attributes
  • Day 2: Intermediate Concepts Link

    • Indexing ,Slicing
    • Reshaping and flattening arrays
    • Aggregations and statistical methods
  • Day 3: Practice Session Link

    • softmax FUuntion
    • Normaliztion FUnction
    • Different Problems to solve
  • Day 4: Advance Numpy Link

    • List vs Numpy Array :Speed,Memory
    • Fancy Indexing
    • Boolean Indexing
    • BroadCasting
    • Broadcasting Rules
  • Day 5: Core Math Function with Numpy Link

    • sigmoid Function
    • MSE(MEan Square Error)
    • Binary Cross Entopy loss
    • Categorical Cross Entopy loss
  • Day 6:Missing Values Link

    • Missing values handling
    • np.isnan(arr),~(np.isnan(arr))
    • np.where(),np.nan_to_num()
    • Matplotlib with numpy
    • plot different graphs
  • Day 6: Matplotlib overview with Numpy Link

    • Matplotlib with numpy
    • plot different graphs
  • Day 7:Practice Day Link

    • Missing values
    • Fancy indexing
    • cauchy matrix
  • Day 8: TricksLink

  • Practice Notebook:

    • Applying concepts to real data problems
    • Performance comparisons with vanilla Python
    • Mini-challenges and insights Practice 1

🧑‍💻 Perfect For:

  • Aspiring Data Scientists
  • Python Developers entering the ML space
  • Students of Mathematics, Physics, and Engineering
  • Anyone looking to speed up heavy numerical tasks

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🤝 Contributions

Found a bug or want to add new examples?
Open an issue or a pull request — contributions are welcome!


📄 License

This project is released under the MIT License.


✨ Start here. Learn NumPy. Build something incredible with data.

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