Apr 19 2022

How to Make Data Science Education More Approachable

Open-source technologies can make it easier and more enjoyable to learn and teach computer science in higher education.

Imagine being a computer science student taking a database class: You sit down to do your homework, only to realize you’re nowhere near ready to begin. Before you can even start your assignment, you need to configure your laptop to run a virtual machine, install and update an operating system, update network and firewall rules, and install and configure database software.

Now, picture yourself as a professor: You have to wait for your students to get their machines ready. Once they do, you’ve got an entire class using different computers running various OSs. When a student loses an assignment or has an issue, you’ll need to know how to troubleshoot whatever system he or she is using.

Both students and instructors are doing the educational equivalent of what developers refer to as “yak shaving”: performing an endless series of small and frustrating tasks before they’re able to begin the original task they set out to do. Using an open-source tool like the Jupyter Notebook interface on a Linux container platform can solve this problem, creating a scalable, interactive and more effective learning environment.

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What’s a Jupyter Notebook?

Known primarily for its use in data science, Jupyter Notebook is an open-source web application similar to Google Docs, but more versatile. With Jupyter Notebook, students can enter specific commands into a cloud-based program and get results in real time. Meanwhile, professors can embed assignments, post videos of lessons and even incorporate entire textbooks into students’ notebooks, where they can be accessed at the student’s discretion.

This open-source web application can be described as a more targeted and customizable version of a one-size-fits-all massive open online course (MOOC). It’s delivered personally to each student through a web browser on the student’s device — regardless of platform or OS. Students can experiment in their Jupyter Notebooks and learn from their successes and failures, while professors can use the technology to teach in a more targeted and interactive manner without spending time getting students situated.

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A Consistent and Reproducible Experience

With Jupyter Notebook, each student signs on to a virtual terminal that presents the same experience regardless of the OS being used. This enables instructors to focus on teaching instead of standing in for IT in dealing with countless system configurations.

Behind the scenes, students’ work is created and saved in an open-source Linux container, which is an environment containing all the elements needed to run an application. Linux containers are ephemeral and elastic, dynamically spinning up when there’s a need (for instance, when a student begins a new homework assignment or the whole campus is studying for finals) and winding down when that need dissipates (when a student sets homework aside for an offline activity).

Elasticity is beneficial for both schools and students. Schools can keep cloud costs under control by using and paying for cloud resources only when they’re needed, and students won’t have trouble accessing their work during times that can be stressful enough without having to worry about computer issues.

The Jupyter Notebook interface can be used with a version control system, such as a Git, to ensure students’ work is saved, allowing students to resume their tasks from wherever they left off. It also allows them to experiment and break things, knowing that their coursework is protected by revision control and, just like erasing a blackboard, they can roll back to a pristine state if their experiments go awry.

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Schoolwide Benefits of Open-Source Technologies

Adopting the Jupyter Notebook interface and its associated technologies helps CIOs better serve the professors and students who rely on their schools’ technology services. Students and professors can work with minimal downtime using technology built with security in mind, improving the learning experience while lessening the need for tech support calls and minimizing the use of shadow IT.

Others in the academic environment can also benefit from Jupyter Notebook’s user-friendly interface, which democratizes data science. Professors and students in just about every discipline, from English literature to art history and beyond, can take advantage of the technology’s power and usability to perform textual analysis and attack problems in different ways without having to be data science experts.

Perhaps most important, these tools can better prepare students for the work they may do after graduation. Students won’t be looking at code on a slide or a piece of paper, trying to find errors. Instead, they’ll actually be interacting with the code — debugging and seeing the cause-and-effect results — right from their laptops. They may even develop a passion for data science, which could lead to a tightening of the data analytics skills gap.

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