NumPy
Anyone who can write Python but stalls the moment the data gets big enough that loops stop being an answer.
5 modules from the Python for Data Analysis curriculum.
What you'll learn
By the end of this course
- Explain why NumPy is fast, in terms of memory layout and typed storage
- Create arrays with array, zeros, ones, arange, linspace, full, and eye
- Inspect arrays via shape, ndim, size, dtype, itemsize, and nbytes
- Choose dtypes deliberately, and recognise integer overflow and precision loss
- Index and slice 1-D and multi-dimensional arrays with [row, col] syntax
- Distinguish views from copies — the single most important NumPy gotcha
- Reshape arrays with reshape, ravel, flatten, and .T, and explain -1
- Measure the memory and speed difference against Python lists
Curriculum
5 sections · 5 lessons
Section 1
5 modules
Section 1
- Introduction to NumPyThe array — the single data structure underneath pandas, scikit-learn, and every scientific Python library.~120 min
- Array OperationsCommand of every operation you will perform on numeric data — and the axis parameter that governs all of them.~130 min
- BroadcastingThe ability to predict — rather than guess at — how arrays of different shapes combine.~110 min
- Random NumbersReproducible randomness, and the ability to turn uncertainty into a number you can defend.~120 min
- Linear AlgebraThe mathematics underneath every regression, PCA, and neural network — implemented from scratch, then done properly.~130 min
About this course
5 modules from the Python for Data Analysis curriculum.
This course covers Introduction to NumPy, Array Operations, Broadcasting, Random Numbers and Linear Algebra.
About 10h 10m of material across 5 modules, each with the dataset it teaches from and exercises with worked solutions.
Every module is a notebook you download and run yourself in JupyterLab — the point is that you execute the code, not that you watch someone else execute it. Recordings are added module by module; a module without one yet is taught in full by its notebook.
Who it's for
- Researchers and postgraduate students who need the analysis done properly, not just done
- Analysts moving from spreadsheets to something repeatable and auditable
- Anyone who has followed a Python tutorial and still cannot open their own data and get an answer
What you'll need
- A computer you can install Python and JupyterLab on — notebook 02 walks you through it, on Windows and on macOS
- No prior programming experience for the foundations; the later tracks assume the earlier ones
- The foundations covered earlier in this curriculum, or equivalent experience
- Your own dataset is welcome but not required — every module ships with the data it teaches from
Start NumPy today
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