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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