Jupyter Notebook
Interactive, reproducible analysis in the browser.
Jupyter Notebook is taught here in 11 lessons, running from Notebook fundamentals through to Beyond local notebooks: Colab, Voila and Quarto. Each lesson takes one topic, shows the working code, and links onward to the next, so the course can be read straight through in order.
| Track | Python & Data Science |
| Lessons | 11 |
| Level | Beginner to intermediate |
| Reading time | about 2 hours |
| Prerequisites | Helpful, but not required: Matplotlib |
Lessons
- Notebook fundamentalsCells, kernels and execution order — how a notebook actually works and the habit that keeps results trustworthy.
- A notebook workflow that scalesProject layout, imports, reproducibility and handing a notebook to someone else without surprises.
- Installing JupyterLab and managing kernelsNotebook, JupyterLab and Notebook 7 compared, installing into a virtual environment, and why a kernel is not the same thing as an interpreter.
- Markdown, LaTeX and rich outputTurn a notebook into a readable document: headings, tables and maths in Markdown cells, plus images, HTML and audio objects from IPython.display.
- Magics: line, cell and shell commandsTime, capture, run and shell out from a notebook, and write a small magic of your own when the built-in ones run out.
- Widgets and interactive outputBuild sliders, dropdowns and live plots with ipywidgets, keep the update logic out of the widget handler, and know what stops working when you export.
- Visualisation inside notebooksInline backends, figure size and resolution, pandas and seaborn output, and interactive libraries that need a renderer.
- Notebooks vs scripts: jupytext, nbconvert and papermillKeep the analysis in a notebook and the review in a text file, export to any format, and run the same notebook once per parameter set.
- Version control, diffs and reproducible outputWhy notebook JSON produces unreadable commits, how to strip outputs automatically, and how to make a notebook that renders identically on another machine.
- Debugging and the hidden-state trapPost-mortem debugging, breakpoints, logging, and the techniques that expose a notebook whose results depend on cells you already deleted.
- Beyond local notebooks: Colab, Voila and QuartoHosted notebooks for zero setup, Voila for turning a notebook into a dashboard, Quarto for publishing, and the cases where a notebook is the wrong tool.
More in Python & Data Science
Python 3 NumPy pandas Matplotlib Flask FastAPI Django SciPy Pillow Python 2.x R Julia
FAQ
How long does the Jupyter Notebook course take?
It has 11 lessons, about 2 hours of reading. Expect roughly twice that if you type out and run every example.
Do I need prior experience for Jupyter Notebook?
Not strictly. It helps to have read Matplotlib first, because some lessons build on it, but every lesson explains its own assumptions.
What should I read after Jupyter Notebook?
Continue with Flask (12 lessons), the next course in Python & Data Science.