Jupyter Notebook is an open-sourced, free web application that enables users to create and share documents containing visualizations, equations, text, and live code. It is commonly used to clean and transform data. Jupyter Notebook users can document and run code, review the outcome, and create a data visualization based on the insights gathered without having to leave the environment.

Most programmers will need to show their work at a certain point. It is typically shared in two forms: raw source code that offers complete information or as a compiled executable that indicates what the software does. One of the main advantages of using Jupyter Notebook is that users can view and execute code in the same user interface, which allows them to make changes to code and to see the results in real-time.

Project Jupyter began in 2014 as a nonprofit, open-source project. The organization’s goal is to develop open standards, open-source software, and services that support interactive computing in several dozens of programming languages. It was developed to support various interactive computing products, such as Jupyter Notebook, JupyterLab, and JupyterHub.

JupyterLab provides users with an interactive, web-based environment that works with various activities and documents, such as terminals, text editors, and Jupyter Notebook. Because of its flexible design, those working with data can arrange or configure the user interface to support various workflows in an integrated manner. Users are able to place activities or documents side by side and integrate them, which leads to the creation of new workflows that can be used in interactive computing endeavors.

JupyterHubprovides its users with access to a plethora of resources and computational environments without the need to perform installations or maintenance tasks. Work can be completed in one’s own workspace within a shared resource, which system administrators can then oversee. JupyterHub is scalable and customizable and provides researchers, teams, students, and Data Scientists with the necessary tools to efficiently complete projects.

Although computational notebooks have been around for some time, Jupyter Notebook’s popularity has grown in the past few years, likely due to its streamlined interface and customizable features.

Uses for Jupyter Notebook

Jupyter Notebook has applications in a variety of industries, and also functions in different capacities within a given field. It is most commonly used for:

  • Numerical simulations
  • Statistical modeling
  • Exploratory analysis
  • Data visualization
  • Machine learning
  • Creating interactive software manuals and tutorials
  • Data cleaning
  • Presentations

Some users rely on Jupyter Notebook for just one of these tasks, whereas others use it to perform a combination of actions.

Pros & Cons of Using Jupyter Notebook

Users cite many benefits, as well as a few drawbacks, to working with Jupyter Notebook.


  • The server side is easy to host, which adds a level of security.
  • This powerful tool allows users to showcase their work, which includes the code as well as the results.
  • Jupyter Notebook provides a platform by which one user can use another’s work as a starting point for their own efforts.
  • Its cell-based approach is especially effective for prototyping.
  • Jupyter Notebook is both platform-independent as well as language-independent. It can be processed by several languages, as well as converted to any file format, such as PDF or HTML.
  • It is especially helpful for data visualizations. The shared notebook allows users to narrate visualizations, as well as share datasets and code, which makes it a great tool for projects that require interactive changes.


  • Because Jupyter Notebook code is written in cells rather than functions/classes/objects, duplicate code is often created.
  • Collaborative coding efforts are problematic because it’s easy to get out of synch when copying parts from one another’s work.

While it’s important to note the disadvantages to working with Jupyter Notebook, most users cite its many advantages as reasons to continue using this tool.

Who Uses Jupyter Notebook?

Because it supports multi-language programming, Jupyter Notebook is the go-to choice for Data Scientists for tasks like exploratory analysis, fast prototyping, and sharing code. Jupyter Notebook is also used by Python Developers, Data Engineers, Data Architects, and Software Engineers. In addition, this web-based environment is especially useful for exploratory analysis, which is used by Data Analysts. Its interactive data visualization tools are available as JavaScript libraries.

Hands-On Data Analytics & Data Science Classes

Learning more about Jupyter Notebook and its many helpful features is a great first step toward launching a high-paying career in data science or data analytics. If you are interested in discovering how to use Jupyter Notebook, as well as other tools for managing and visualizing big data, Noble Desktop’s data science classes provide a great option. Courses are available in-person in New York City, as well as in the live online format in topics like Python and machine learning. Noble also has data analytics courses available for those with no prior programming experience. These hands-on classes are taught by top Data Analysts and focus on topics like Excel, SQL, Python, and data analytics.

Another great way to learn more about Jupyter Notebook is to enroll in one of Noble Desktop’s in-person or live online Python classes. These hands-on classes are taught by industry experts, and help students master the most powerful programming language. Students receive timely instruction on topics like machine learning, web development, Python for data science, and FinTech.

Those who are committed to learning in an intensive educational environment can enroll in a data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 40 bootcamp options are available for beginners, intermediate, and advanced students looking to learn more about data mining, data science, SQL, or FinTech.

For those searching for a data science class nearby, Noble’s Data Science Classes Near Me tool makes it easy to locate and learn more about the nearly 100 courses currently offered in the in-person and live online formats. Class lengths vary from 18 hours to 72 weeks and cost $915-$27,500. This tool allows users to find and compare classes to decide which one is the best fit for their learning needs.