May 1, 2024
Updated May 11, 2025
20 minute read
Jupyter Notebooks are web-based interactive computational environments that allow users to create and share documents containing live code, equations, visualizations, and narrative text. This versatile tool has become increasingly popular, particularly in fields like data science, machine learning, and education, because it enables users to combine code execution, rich text elements, mathematical formulas, plots, and various media types within a single, shareable document. The primary purpose of Jupyter Notebooks is to support interactive data science and scientific computing across multiple programming languages.
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Find a path to becoming a Jupyter Notebooks. Learn more at:
OpenCourser.com/topic/c05v92/jupyter
Reading list
We've selected four books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Jupyter Notebooks.
A practical handbook focusing on data science applications of Jupyter Notebooks, including data cleaning, exploratory data analysis, machine learning, and data visualization. Assumes some prior programming knowledge.
A specialized guide to using Jupyter Notebooks for machine learning tasks, covering topics such as data preprocessing, model training, and evaluation.
A practical guide to using Jupyter Notebooks for data analysis and visualization, covering topics such as data cleaning, data manipulation, and interactive visualizations.
A guide to using Jupyter Notebooks for Python developers, covering topics such as data analysis, machine learning, and web development.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/c05v92/jupyter