Track
numpy
NumPy
Numerical arrays and vectorized computation.
- Mode
- server_script
- Practice
- Server runner
- Lessons
- 36 units
Before you start
NumPy arrays, broadcasting, vectorization, and numerical computing primitives.
Foundation of the Python scientific stack—Pandas and ML libs build on ndarray operations.
Notebooks, feature engineering pipelines, and custom numerical code.
Server Python runner exercises with small, inspectable array programs.
Early in the data-science path—immediately before or alongside Pandas.
Lesson order
Sequential — follow top to bottom36 lessons are live in this track. Start from step 01 for the smoothest path.
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01 intro Introduction to NumPy
beginner
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02 what-is-numpy What is NumPy?
beginner
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03 numpy-vs-lists-preview NumPy vs Python lists preview
beginner
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04 numpy-ecosystem-preview NumPy ecosystem preview
beginner
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05 numpy-workflow NumPy workflow
beginner
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06 ndarray-creation Creating ndarrays
beginner
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07 numpy-dtypes NumPy dtypes
beginner
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08 shape-reshape Shape and reshape
beginner
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09 indexing-slicing Indexing and slicing
beginner
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10 array-math Array math
beginner
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11 numpy-random Random numbers with NumPy
beginner
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12 ufuncs Universal functions (ufuncs)
beginner
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13 broadcasting Broadcasting
intermediate
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14 axis-operations Axis operations
intermediate
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15 aggregations Aggregations
intermediate
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16 boolean-indexing Boolean indexing
intermediate
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17 set-operations Set operations on arrays
intermediate
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18 dot-matmul Dot product and matmul
intermediate
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19 linalg-basics Linear algebra basics
intermediate
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20 transpose-views Transpose and views
intermediate
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21 stacking-splitting Stacking and splitting
intermediate
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22 fancy-indexing Fancy indexing
intermediate
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23 meshgrid Meshgrid and coordinate grids
intermediate
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24 structured-arrays Structured arrays
advanced
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25 saving-loading-npy Saving and loading .npy files
intermediate
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26 memory-views Memory views and strides
advanced
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27 nan-handling NaN handling
intermediate
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28 linear-algebra-preview Linear algebra preview
advanced
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29 performance-vectorization Performance and vectorization
advanced
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30 numpy-with-pandas-preview NumPy with Pandas preview
intermediate
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31 matplotlib-numpy-preview Matplotlib and NumPy preview
intermediate
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32 scipy-teaser SciPy teaser
intermediate
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33 numpy-in-ml-preview NumPy in machine learning preview
intermediate
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34 interview-essentials-numpy Interview essentials for NumPy
intermediate
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35 production-checklist-numpy Production checklist for NumPy
advanced
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36 pandas-bridge-lesson Pandas bridge lesson
intermediate
Open →