[abdujava]

One of my random question is that what does Google gain by spending resources on developing course like this? Do they want more people to do machine learning as there is a short age of developer with this skill in the market or is there something else involved in the mix?

Secondly, for some reason data science just doesn't excite me as much as typical software development goes. Like, why am I not excited enough to go down the path of specializing in data science in field of machine learning? Even if there is more money in it, I'm still not extremely motivated to learn it.

What i do particularly enjoy is good ol' back end web development. I don't have a degree in computer science but working on a information system degree with focus on "programming", I dream/working my ass to become cult of "software engineer" type II, a sophisticated software developer/programmer. I love building layers, optimizing code, learning new tools, algorithms data structure (without knowing math), creating unit tests, following programming paradigm. It excites me so much. And my core skills to dive into is block chain.. I love studying that topic too and all the algorithms it comes with it.

But when I see data science, no excitement. All I imagine is image manipulation and fancy charts. I know I sound a bit ignorant but, that's how it is.

[boxspam]

> One of my random question is that what does Google gain by spending resources on developing course like this?

Mindshare or more generally PR. Also to "collect" the talent on their platforms (Tensorflow, Google Cloud, ...). Also these guides were repurposed from existing (internal) guides and are a few years old by now, so the cost is low.

You further describe the role of a data engineer or ML engineer. If you'd approach data science with a focus on engineering and tool use, you could be one of the few dangerous data scientists that is able to go end-to-end (should be safe for at least 5 years when such pipelines are evolved without much human intervention).

> But when I see data science, no excitement. All I imagine is image manipulation and fancy charts.

This is because, while there is legit substance to the hype, the hype is real and it is focused on deep learning ImageNet (and later GAN's, Atari games, Go). Being able to show deepdreamed images and cat neurons is like catnip to journalists. Computer vision is but a very small part of ML and lots of data-driven companies have no need for such skills. Charts are made by analysts.

Everything (including block chain) will move closer to ML paradigm of learning software. Data infra engineers will see their infra increasingly used for ML. It remains all software (very advanced, but accessible to anyone) and hardware (still a asymmetry here between industry lab and practitioner). Don't get left out: Do machine learning like the great engineer you are, not like the great machine learning expert you aren’t.

[hhs19832]

Great and honest points.

>Secondly, for some reason data science just doesn't excite me as much as typical software development goes

Fair enough. Part of the reason is "data science" has been so jammed pack of nonsense and people who don't do the actual work of building things, as you describe below.

> What i do particularly enjoy is good ol' back end web development. I don't have a degree in computer science but working on a information system degree with focus on "programming", I dream/working my ass to become cult of "software engineer" type II, a sophisticated software developer/programmer. I love building layers, optimizing code, learning new tools, algorithms data structure (without knowing math), creating unit tests, following programming paradigm. It excites me so much. And my core skills to dive into is block chain..

Ok this makes sense. But I'd be worried about 5 years from now. When all the little gears and things that go on in backend becomes a commodity (or abstracted away in the "cloud"), what are you going to do?

> I love studying that topic too and all the algorithms it comes with it.

That spark of interest in the algorithms, (which is just about logic, which is what math is basically about in the end), is basically the essence of what makes "Data science" so attractive.

[scarecrowbob]

"But I'd be worried about 5 years from now. When all the little gears and things that go on in backend becomes a commodity (or abstracted away in the "cloud"), what are you going to do?"

Well, over the last 8 years or so I started out in a similar kind of place, and have gotten quite good at building CRUD and business logic and glue, and fixing crap on the front end, and configuring servers.

Maybe I can stand in for the OP a few years down the line?

Over the last quarter, I've been splitting my time between things like linux admin automation and a set of pre-calculus core classes.

To answer your question on my personal scale, my whole ability to do this kind of work with my mediocre CS education (my BA is in Philosophy, and my PhD work is in Lit) is premised on leveraging the points in the systems where "all the little gears and things that go on in backend have [become] a commodity"... hence I just integrate ERP systems with WordPress or try and clean up some business's AWS drupal hosting setup some crap like that. That's been a fun and rewarding conjunction of my love for systems and the commodification of parts of IT/ programming work.

My hope is that by the time all the little bits of these data science topics become "abstracted away" over the next couple of years, I will understand the general underlying things well enough to use them. But who knows if that is a good bet or not... certainly not me.

However, it feels perfectly fine to learn things like math... I'm way, way better at it than I was as an undergrad 20 years ago and so it's quite a lot more fun for me. It's not like knowing some math has no application outside of this narrow field.

I dunno if my personal answer (keep learning, and enjoy fixing crap) matches the OP or helps extend your points/ question, but I've been getting a lot of fun (and some money) out of following my answer.

[technologia]

I think I have an answer to that first question; Altruistically I'd like to think its to help facilitate more ml engineers and scientists. Realistically, amongst the other reasons noted by everyone else, its a way to attract enterprise users to their technology & invariably their cloud.

Consider a larger organization (1000+ people perhaps), if groups within that org can train their people with these materials or even send them to Google to be trained in this subject matter they can come back with a nice shiny credential. Whether that ultimately becomes useful to that individual or the group is up to them but really it helps google foster that relationship with the main organization to eventually snag higher contract values.

That probably made no sense, but I thought I'd give my two cents (however crummy they might look).

[dws]

> One of my random question is that what does Google gain by spending resources on developing course like this?

s/Google/someone at Google/

20% time leaves discretionary time for people who're motivated to get something like this started. Official approval may come along the way.

[gaius]

Everyone I know at Google says 20% time comes on top of 100% time these days

[gaius]

Do they want more people to do machine learning as there is a short age of developer with this skill in the market or is there something else involved in the mix?

They want to sell TPUs, this is part of generating the demand.

[amorphous]

> What i do particularly enjoy is good ol' back end web development.

By all means, keep at it! Better to be an exceptional backend dev than average ML engineer. No one can predict the future anyway. It's certainly possible that the ML job surge is gonna stop abruptly when most of the advances have been captured by APIs.

[codeisawesome]

Regarding your latter part, do read the “define: CTO OpenAI” (don’t have link I’m on mobile) - author has fascinating insights on just how important engineering of the specifics you describe is, for ML work to progress and show results.

[lovelearning]

An ML/data engineer tasked with productizing a data pipeline still does all of those - building layers, optimizing code, learning new tools, algorithms data structure, creating unit tests, following programming paradigms.