Explore the diverse functionality of Jupyter Notebook, an open-source collaboration technology that significantly simplifies the process of learning and coding in popular programming languages. Discover how this unique tool promotes practice, collaboration, data organization, visualization, and skills teaching among data scientists and programmers alike, expanding career opportunities in high-demand fields.
Key Takeaways
- Jupyter Notebook offers a flexible and versatile learning environment for writing and practicing code, enabling users to run sections of code without executing the entire program.
- This technology fosters collaboration on data science and engineering projects across a large community of users, supporting multiple languages and integrating with various tools and applications.
- Jupyter Notebook facilitates the tedious process of data organization and cleaning, making it compatible with tools like Jupyter Innovator to streamline the process.
- It serves as a robust platform for data visualization and sharing, allowing users to compile all aspects of a project in one place and share it with others via compressed files or folders.
- Jupyter Notebook is widely used for teaching data science skills, making it an excellent tool for students to collaborate on projects, practice data analysis and organization, and learn to identify and correct coding errors.
- Hands-on experience with Jupyter Notebook can be gained through courses and certificate programs like the Python Bootcamps and online data science classes offered by Noble Desktop.
Whether you use programming simulators or popular exercises, there are many data science tools that make the process of learning how to code in a new programming language significantly easier. Jupyter Notebook is another one of those data science tools. Developed in 2014, Jupyter Notebook is an open-source collaboration technology that can be used to edit and run code using many of the most popular programming languages. Within the realm of programming, notebooks act as a one-stop-shop that allows you to work on all stages of the data analysis process on one interactive page. Commonly used amongst educators or teams, Jupyter Notebook is especially useful for working in any type of group-setting or cloud-based environment.
How do you use Jupyter Notebook?
While there are many ways to use Jupyter Notebook depending on your goals and intentions, the first step in using the notebook is getting to know the technology. One of the best parts of using Jupyter Notebook is its flexibility and versatility, as you can work with the technology by downloading JupyterLab, or simply opening your browser to use the notebook on the go through the original application. A tool that was made for collaboration and interactivity, Jupyter Notebook is also compatible with the most commonly used programming languages such as R, C++, Ruby, and Python.
Jupyter Notebook also gives the user access to a community of fellow users and open-source programming libraries. Once you begin using it, it is easy to find additional information and instructions on how to use the technology and integrate it into other components that may interest you. Divided into front end and back end interfaces, Jupyter Notebook not only gives users access to the outcome of their code but also assists in the process of tweaking and editing the code before it is executed. This list outlines the top five uses for the Jupyter Notebook both within and outside of the classroom.
1. Programming Practice
One of the primary ways that both students and professionals can use Jupyter Notebook is as a form of practice when writing, or learning how to write, code. When writing code, there is a certain trial and error method to understanding how to fix a program that just isn’t running correctly. In the past, programmers would have to write an entire program and then execute it before knowing whether or not the program actually worked. If the program did work, the computer would simply run the code as written. If it didn’t, the outcome would either not be what was expected from the code or the computer would return an error message with no indicator of what portion of the program was written incorrectly.
With Jupyter Notebook, you no longer have to guess about any mistakes or missteps in your code, because Jupyter Notebook gives coders the option to practice their program in an environment that makes it significantly easier to edit and modify specific portions of code. Specifically, Jupyter Notebook gives users the option to run a section of code without executing the entire program, in order to determine if it works before moving on to writing the next line of code. By letting users run code step by step, or line by line, Jupyter Notebook provides an excellent practice space to work through a program before sending it out or sharing it with others.
2. Collaborating Across Projects and Tools
Another aspect of the Jupyter Notebook that stands out is the ability to collaborate on projects across a large community of users and tools. The Jupyter Notebook community was built on collaboration, as Jupyter Notebook was created to allow people to work together on data science, engineering, or other programming projects. Some features of Jupyter Notebook which make methods of collaboration easier are the fact that it supports multiple languages, is compatible with various tools and applications, and offers open-source and cloud-based programming.
Most recently, other technology companies have also created their own notebooks that allow even more users to collaborate using Jupyter Notebooks. Google created their own platform for Jupyter Notebooks called Google Collaboratory that allows users to collaborate on projects using Google Drive. Jupyter Notebook has also inspired the creation of cloud-based coding notebooks such as Microsoft’s Azure Notebook and Amazon’s Sagemaker Notebook, which are both compatible with, and allow collaboration on, projects within the Jupyter Notebook web-based application.
3. Data Organization and Cleaning
One of the most tedious tasks for a data scientist is the work of cleaning and organizing data. A time-consuming process, data cleaning requires that your data be organized in such a way that the portions of the dataset that you do not want can be easily identified and removed before you can begin using the data to make suggestions or predictions. Since Jupyter Notebook is compatible with multiple tools, it can also be used to streamline this process of data organization and cleaning.
Tools such as Jupyter Innotator can be installed as part of Jupyter Notebook to streamline the process of cleaning and organizing your data. Jupyter Innotator gives users the option to create boxes and boundaries around images, as well as to use drop-down boxes to classify and categorize different parts of the dataset. The drag and drop tools within JupyterLab also make it easier to clean and organize data because users can reorder the data in a notebook and create links that make the process of searching and filtering easier and more efficient.
4. Data Visualization and Sharing
Once you have cleaned, organized, and analyzed your data, Jupyter Notebook can be used for data visualization and sharing. Jupyter Notebook allows users to compile all aspects of a data project in one place making it easier to show the entire process of a project to your intended audience. Through the web-based application, users can create data visualizations and other components of a project to share with others via the platform. There are multiple ways that you can share a data project both within and outside of Jupyter Notebook.
One of the primary methods of data visualization and sharing via Jupyter Notebook is through creating a compressed file or folder with all of your information and data. This file can then be shared as a link with your fellow collaborators or students. This is one of the most common ways that notebook data is shared through repositories such as GitHub. You can also work with a program like Anvil, which turns Jupyter Notebook into a shareable application. As mentioned above, you can also use integrated products and tools like Google Collaboratory to share documentation within a shared drive or platform.
5. Teaching Data Science Skills
Based on all of the previous uses for Jupyter Notebook, it is apparent Jupyter Notebook is primarily used to teach data science skills. It is quite common to see Jupyter Notebooks used within the classroom because it allows students to collaborate on data science and computer programming projects. In addition, it can be used to practice important data science and programming skills, such as data analysis and organization. Through the sharing and visualization component, Jupyter Notebook is an excellent tool for having students show their work on a project from start to finish, as well as learning how to identify and understand where they went right or wrong when writing code.
Want to try your hand at using Jupyter Notebook?
There are multiple courses and certificate programs where students and professionals can get hands-on experience working with Jupyter Notebook. The Noble Desktop Python Bootcamps are a way to practice using Jupyter Notebook for programming and development. Jupyter Notebook is especially useful within the context of data science, so you can also take part in a live online data science class through Noble Desktop. Or find a data science class near you to practice your programming skills with Jupyter Notebook.
