Menu
Jupyter Notebook is a free and open-source software that allows for the creation and sharing of documents with code and rich text elements. It works with over 40 programming languages including Python, R, and Ruby making it versatile and flexible. It is used by many professionals in the data science and research communities and is considered the standard notebook by many in these fields. It operates on GitHub and is continually contributed to by the active Jupyter community.
Project Jupyter was founded by Fernando Perez and Brian Granger in 2014 as a derivation of IPython. The name Jupyter started as an acronym for the languages Julia, Python, and R, although it now works with many more languages. The project focuses on creating open-source software for many programming languages. As part of the project, Jupyter Notebook was created as a way to develop and share documents with code, text, or visualizions through a web application. The notebooks can be shared for collaboration through emails or by using tools like Dropbox or GitHub.
Jupyter notebook is great for data science, machine learning, and statistics modeling since it can also share code and visualizations. Notebooks can contain the results and figures in human readable form as well as computer code for executing data analysis. On the front-end, users input text and/or programming code in cells on the webpage. The kernel located on the back-end then receives the information. The code is run by the kernel and results are delivered. Each programming language has its own kernel that executes the code in the notebook document. The IPython kernel, for example, executes code written in Python. Notebooks are located in the notebook dashboard where notebooks and kernels can be managed. The dashboard is also where files and folders are found and managed by adding, renaming, and deleting. It is essentially the file manager of the software.
Jupyter notebook works via the Jupyter Notebook App and can be downloaded onto a desktop to work locally without the internet, or through web browsers remotely utilizing the internet. Jupyter Notebook Viewer an extension that provides another way to view the notebook easily and effectively.
Jupyter Notebook is useful because it provides an interactive way to write and execute code and perform analyses. In particular, data scientists, data analysts, data engineers, machine learning scientists, and researchers like to use the notebook because it provides a way to discuss and elaborate on data analysis. The software can help with data visualization, machine learning, data prototyping, data cleaning, modeling statistics, numerical simulation, and more. Data scientists like that they can write and execute code, see what happens, and then modify the code and run it again.
The tool is also helpful when used for presentations because one document can include code, visualizations, multimedia, equations, and more. It can also be used by researchers and educators as an educational tool for students through which they can interact and have a discourse over problems and access materials such as links, multimedia, and visuals.
Jupyter notebook extensions are available and continuing to evolve to add features to the tool. Collapsible headings, ready made snippets of code, displays for execution time, set notifications, spellchecker, and scratchpads are examples of extensions that can be used to increase the functionality of the notebook.
Since it is so popular and used by many in data science related fields, learning Jupyter Notebook can only increase your employability in these high demand areas.
Do you already know a programming language supported by Jupyter Notebook? It is possible to learn the tool through tutorials and online resources and by utilizing support from the online Jupyter Community. Since the software is free, it is possible to practice and become more proficient with its capabilities.
There is also a plethora of online courses that are focused on teaching the tool at different levels. These courses can provide a more structured option for learning the notebook. If you have yet to master a programming language, or are looking for a more detailed curriculum, there are bootcamps that specifically teach Jupyter Notebook. Many data science bootcamps use Jupyter Notebook and will also teach a programming language so that code can be written in cells and scripts. If you do not already know a programming language, a comprehensive bootcamp like one in data science, may be best for you. Below you will find a list of bootcamps that offer Jupyter Notebook in their course curriculums.
