How to make teaching data science more accessible

A consistent and repeatable experience

With Jupyter Notebook, each student connects to a virtual terminal that presents the same experience regardless of the operating system used. This allows instructors to focus on teaching instead of replacing IT with countless system configurations.

Behind the scenes, student work is created and saved in an open-source Linux container, which is an environment containing all the elements necessary to run an application. Linux containers are ephemeral and elastic, dynamically activating when there is a need (for example, when a student starts a new assignment or the whole campus is studying for finals) and ending when that need dissipates ( when a student puts their homework aside for an offline activity).

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

The Jupyter Notebook interface can be used with a version control system, such as Git, to ensure student work is saved, allowing students to pick up where they left off. It also allows them to experiment and break things, knowing that their courses are protected by revision control and, much like erasing a blackboard, they can go back to a pristine state if their experiments go wrong.

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Benefits of school-wide open source technologies

Adopting the Jupyter Notebook interface and its associated technologies helps CIOs better serve teachers and students who rely on their schools’ technology services. Students and faculty can work with minimal downtime using technology designed with safety in mind, improving the learning experience while reducing the need for tech support calls and minimizing usage phantom computing.

Others in academia can also benefit from Jupyter Notebook’s user-friendly interface, which democratizes data science. Teachers and students in almost any discipline, from English literature to art history and beyond, can leverage the power and convenience of technology to perform textual analysis and problem solving. in different ways without having to be data science experts.

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

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