![]() There are two main types of Jupyter Notebook hosted and local notebooks. According to the Kaggle Survey 2022 results, Jupyter Notebooks are the most popular data science IDE, used by over 80% of respondents. Jupyter Notebooks are primarily used by data professionals, particularly data analysts and data scientists. Instead, you simply mix your analysis with the report text in the notebook. That means that you can avoid the error-prone process of copying and pasting analysis results into a separate report. The key benefit of notebooks is the ability to include commentary with your code. Notebooks combine computer code (such as Python, SQL, or R), the output from running the code, and rich text elements (formatting, tables, figures, equations, links, etc.) in a single document. This tutorial assumes that you have used a data science programming language before, such as Python, SQL, R, or Julia. We will explore notebooks using Datacamp Workspace, a hosted notebook service that provides all the functionality of Jupyter notebooks, along with functionality for connecting to databases, real-time collaboration, and publishing your work. This tutorial provides an overview of Jupyter notebooks, their components, and how to use them. Jupyter notebooks are documents for technical and data science content.
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