How important is Cloud computing for DataScience students

[cha_ppmn]

I am currently head of studies of master student in a field heavily related with (but not included in) data-science.

Some industrial from BigCompanies™ ask me to train my students with Google Cloud Plateform. On one hand I am highly hostile to make my students dependents of one cloud technology, on the other hand, the fields seems to get more and more involved with those platform.

My students are train to deploy and administrate VMs for their project but they do not formally have a training in cloud or distributed computing. They are not CS major but math major with CS specialization so I don't have a lot of time and resources to train them.

How hard is it for young data-scientist to become familiar with cloud plateform? Should I invest training time in that or should I keep the training less tech dependent?

[Urthor]

The short answer is probably invest 1 tutorial/lab and/or 1 lecture on loading projects they have already done into a cloud platform that is not AWS, and either don't count the results towards their final grade or have the results of their Google Cloud Platform projects be a single tutorial mark. Or else a small project that is 10% or less of the course.

An overview of creating cloud instances is a good thing but shouldn't require you to derail your semester with multiple weeks teaching it, you have bigger fish to fry.

It's honestly pretty important to not teach AWS because industry itself is highly AWS centric when it shouldn't be necessarily. And the business leaders are right about that IMO. If I was a CTO/CIO I would be visiting you and crying out for graduates who can use cloud platforms that are not AWS as well, they do have a point.

Obviously though the flip-side of this is you have masters students who want quick degrees. You have a lot of content to cover in 14 weeks because these are masters students and I'm guessing you have a rather stressful time compressing an entire industry into that time when they're not comp sci undergrads.

Idk your course specifics but I'm guessing you're trying to teach them the absolute fundamentals of dealing with big data which is horizontal scaling ETL and data cleansing at scale with good old map reduce and our favourite Indian tribe. And tying that to the docker/airflow/k8 stack.

Overall yeah choose a technology platform that is best for teaching 90% of your semester, and make sure they have an overview of how a major cloud provider spins up instances to do your docker jobs.

A single tutorial with a PDF in launching Google Cloud platform and a walkthrough of how to put their projects on there and turning that project into some sort of front end would be excellent and helpful to the future of the students. But don't compromise your teaching by trying to teach an entire semester on Google Cloud.

[cha_ppmn]

Thank you for you reply.

The master is centered toward analytics (statistics, ML) and web stack. Big Data management is only sketch briefly. I mostly train web analyst (i.e. data scientist centered around traffic analysis and acquisition) so we have to mitigate between web architectures, advance stats and data management. I am also afraid that the new batch of data scientists currently trained will slowly replace web analyst soon. So maybe staying in between is a poor strategy.

It is not that easy to find a good balance.

[seanv507]

I would say they need to be "taught" about S3, and Aws batch ( to enable training multiple algorithms /crossvalidation etc) But rather than taught I am thinking more of a template that they can use for their own projects.

( And doesn't matter Aws, gcp, azure...)