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.
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.
- ✅ 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
This guide is structured through hands-on notebooks, each building your understanding step by step:
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Day 1: Introduction to NumPy Link
- Creating arrays
- Basic math
- Array attributes
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Day 2: Intermediate Concepts Link
- Indexing ,Slicing
- Reshaping and flattening arrays
- Aggregations and statistical methods
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Day 3: Practice Session Link
- softmax FUuntion
- Normaliztion FUnction
- Different Problems to solve
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Day 4: Advance Numpy Link
- List vs Numpy Array :Speed,Memory
- Fancy Indexing
- Boolean Indexing
- BroadCasting
- Broadcasting Rules
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Day 5: Core Math Function with Numpy Link
- sigmoid Function
- MSE(MEan Square Error)
- Binary Cross Entopy loss
- Categorical Cross Entopy loss
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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
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Day 6: Matplotlib overview with Numpy Link
- Matplotlib with numpy
- plot different graphs
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Day 7:Practice Day Link
- Missing values
- Fancy indexing
- cauchy matrix
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Day 8: TricksLink
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Practice Notebook:
- Applying concepts to real data problems
- Performance comparisons with vanilla Python
- Mini-challenges and insights Practice 1
- 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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Found a bug or want to add new examples?
Open an issue or a pull request — contributions are welcome!
This project is released under the MIT License.
✨ Start here. Learn NumPy. Build something incredible with data.