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I find it curious that there are so many courses for data-science related subjects, which superficially seem to cover the same material, and relatively few courses covering more traditional CS topics such as computer systems, networks, OS. I suppose it has to do with the market, but also feels like colleges are skating to where the puck is, rather than where it will be (or perhaps, where it could be).

This is a general issue i have seen as well.

We live in 2018 and there is no open source course for everything. Instead there are probably 10-30k universities who have similar courses and professors who give the same lecture every year.

They get paid often enough by countries to create and do those courses. In germany most of our unversities are paid by all of us germans anyway.

And what do you find online? Always the starter verion like 101 computer science or videos with bad audio or video, no proper exercises, no solution helper etc. Nothing. You have to go to different sites to sometimes pay or sometimes not.

there are no local locations to meet up with people.

There should be a global initiative for global free and open access learning. Sponsored and supported by companies and countries. Build upon a core of a knowledge graph based on topics or 'snippets of knowledge'. Like for example: math -> add -> sub

Something like 'The Map of Mathematics' (https://www.youtube.com/watch?v=OmJ-4B-mS-Y)

And when you wanna get the global accepted math 101 level, you have to take specific topics / snippets.

And those snippets can than be filled with different people who are making a lecture for that topic and you can choose whom you like more or who is better in explaining it to you.

What do i do instead? I ask around for the lecture scripts because they are always behind a simple password protected area or have multiple links to different pages of different universietses who offer different courses for free as videos for there students in sometimes/often bad quality and / or bad video players etc.

It sucks and this is stupid.

>There should be a global initiative for global free and open access learning. Sponsored and supported by companies and countries. Build upon a core of a knowledge graph based on topics or 'snippets of knowledge'.

I like this idea and the framing of it a lot

Have you seen this? Does this meet your criteria/have enough sufficient resources/curriculum?

> http://datasciencemasters.org/

Nope, not at all.

I'm not looking for the next github page with collections of tons of different sites with different courses.

I still hope for one platform where all those smart people out there are working together to optimize learning.

I forget if its the Verge or some other popular podcast but they are always suggesting Apple just put together a fully open and accredited online university.

The problem is that there are a lot of people getting paid a lot of money all over the world to work in post-secondary education whom control the keys to accreditation and whom have proven very resilient at resisting any optimization efforts.

Apple isn't open. Their stuff has tended to not work on Linux.

I don't see why one would need Apple to do it. A consortium would be much better and more likely.

Apple is big enough to piss away one billion dollar and create a crazy good online University.

Also need done good pr I guess :)

Here is a list of CS courses including many graduate level courses:


Check out khanacademy.

I have also found this interesting. What I don't understand is that the amount of data science jobs are no where near the levels that people make it seem. I am not sure where all these people will end up working if they want to be a data scientist. There is not a need to hire huge teams of data scientists like you might for dev roles, it doesn't scale the same way.

Every job that involves data in any way is being relabeled a data science job. Most of them are just generating dashboards and posters in Excel or Tableau for people who are data illiterate. I know many people with maths/stats/comp-sci backgrounds who end up in these sorts of jobs.

“Just add a bunch of green up arrows and red down arrows, your manager will love it” was advice from a co-worker of mine. Sadly, she was right.

It's actually become somewhat hilarious to me. Like you said, the data science label is being applied quite liberally (no judgment, I'm not the world's authority on how it should be applied), so here you have companies paying $100k or more to have people do Excel work or Tableau visualizations.

"Data scientist" is the new "business analyst".

I think data science moved into the hole where analysis used to be.

This. You are exactly right. I work for a startup not in anyway related to Data Science or ML etc. We use Python here. My flatmates work for a big named DataScience company and most of the time its numpy and a bit of data visualisation.Pandas and Numpy to the rescue. I am like dude, I can do that in blink.

In an engineering class, when dealing with a problem about factories that produce widgets and consume resources/widgets from other machines, I made a complex Excel spreadsheet that was animated to pass little numbers around that represented the items produced or consumed.

It didn't actually give correct results, I'm not sure it could have worked (I needed to do two updates on the same cycle and never did figure out how), and I documented that it was buggy.

But it looked cool and I got good marks. So my experience pretty much agrees with yours.

Where can I get a job like that? I'd be happy to take it at this point.

Data engineering as well.

Haha, so much yes.

Every graduate that doesn't find a high paying job in the field is an excellent candidate for the next level of education.

After you have given a university hundreds of thousands of dollars and a decade-plus of your life, you will then be ready to teach the next crop of students.

> Every graduate that doesn't find a high paying job in the field is an excellent candidate for the next level of education.

1. You really think Berkeley's top-ranked PhD program is recruiting people who couldn't find jobs? No. Not only can 100% of successful top-tier PhD applicants find jobs, 100% of them are strong candidates for the top echelon on entry-level jobs.

If you disagree, go look up the people in Berkeley's CS PhD program. Point out a single person you think didn't turn down mid-100s job offers to attend Berkeley.

Getting into one of these top-5-to-10 PhD programs is no small thing...

2. MOOCs aren't PhD programs and in nearly all cases aren't designed to feed into PhD programs.

3. Finally, at least in CS, at least for the moment, educators are in extremely high demand. And again, at least for CS, that demand isn't being manufactured by the academy.

You really think Berkeley's top-ranked PhD program is recruiting people who couldn't find jobs?

It is well known that PhD programmes churn out far more PhDs than can reasonably be employed in their field.

I mean, including friends and former colleagues I probably know maybe 300 people with PhDs. Of those I can count on my fingers those actually doing research in academia, probably one hand those on tenure track. But do you really think any of them slogged through the programme in Physics or Biology just to get a software job writing CRUD apps, or prettying up BI reports?

You're arguing against a point that the OP didn't make.

See point 3. At least in CS, at least for the moment, there's insane demand for CS educators. Maybe not research track at top 20 R1, but definitely requires a PhD.

Close to 100%, but not 100%.

This is sort of technically true. Modified statement, based on extensive person experience: ~80+% had such an offer in hand, and the remaining 20% either:

a) didn't bother applying but would've been shoe-ins, or else

b) knew very early (freshman year) they were research-bound and optimized for a non-industry objective function (but could've skated into an industry job of their choice given a shift in undergraduate career focus). E.g., couldn't pass a coding interview and no industry internships but have one or more top-tier publication in a hot subfield.

But (b) is kind of stupid to think about. It's like saying a successful lawyer would not make captain in the military. This may or may not be the case, but either way, who cares?

Lawyers can commission directly as captains in the USAF (and probably other branches) as long as they are not too old and can pass the fitness test. I think they also have to have passed a bar exam in one state, but it doesn't matter which one. They can get up to $65k of student loans repaid and don't even have to go through the same basic training as everyone else.

So (b) is slightly less trivial than saying a successful lawyer would not make captain in the military because most lawyers are one conversation, a few signatures, and one oath away from being a captain.

So don't delay lawyers, join today!

Did you actually do any recruiting? Or are you like me and just sort of started inadvertently talking this way after a certain number of years?

No recruiting, I started off by wanting to point out that military lawyers don't "make" captain, they are given that rank to start. Then I realized it sounded like a little like a recruitment talk so I decided to go all in.

Yes, I know, that was my point.

> ...So (b) is slightly less trivial

I'd argue not. In both cases, a fully capable and prepared person has to jump through a couple of relatively trivial hoops. And when those hoops aren't possible to jump through it's a weird case. We can split that hair, but it's silly.

You chose a funny example. A lawyer can make captain in the military just by signing on the dotted line, same with a doctor.

It’s a pyramid sceme you can get in on with financing.

I disagree with this. Every enterprise company has an analytical department, even more so in the public sector. My municipality has 8 guys working on analytics for instance.

They are mostly economics or (I’m not sure what it’s called in English, but it’s a degree in societal administration), but they really ought to be data scientists because everything they do is based on huge sql data sets.

We pay private contractors a lot of money to turn our data into cubes and manageable models because none of our analytics know how.

In 10 years I suspect anyone with that job title will need data science on their resume. Not just to manage the data, but also to start doing machine learning on it.

By comparison we have one network guy to run the network for 10.000 employees and 5000 students, with a backup guy who knows everything the first guy does but works with something else, you know, in case the first guy quits.

My municipality has 8 guys working on analytics for instance.

Do you propose that they should take a MOOC like this one, or that they should be replaced by MOOC graduates?

I think they’ll be replaced with candidates that mix economics and data science as they naturally retire or go elsewhere. If I was young in the field I’d definitely take a degree in data science, especially if I worked in the public sector where we’re much more willing to pay for your education as well as give you time off to take it.

I actually think in order to do it right you need to have a sizable team (n > 7) of data scientists in order to keep them honest, productive, and developing their skills. They will need to pair up with a team of software devs/ML/data wranglers to help them push the edge on what they are doing. Depending on the company that could be the complete engineering team, or it could be a disjoint set.

Rebrand marketing into a data science'y name to attract quantitatively minded applicants (already starting to happen in some orgs)

As someone in data science this isn't a trend I particularly like. I suppose to some extent these jobs can be filtered out by requiring a salary that they're not willing to pay for that work.

I don’t think the colleges care where their students end up when they’re done with them.

They care to the extent that the alumni are willing and able to make fat donations.

Startups do not need a huge team of data scientists... but industry in general does need data analysts and this would certainly enhance the skills of that crowd.

MIT open courseware has a bunch of classes related to traditional CS topics. Also just searching universities you can find some. Data Science is the new hip class and it’s just very aggressively marketed. The other one being marketed heavily is intro to coding .

Their computer security class is amazing. All lectures, notes, labs, quizzes etc: https://ocw.mit.edu/courses/electrical-engineering-and-compu...

I think all MOOCs have traditional CS courses but marketing hype is on Data Science and ML. I remember doing Tim Roughgarden(StanFord) Algorithm course on Coursera. Loved it.

Good to know about OCW. Are there often videos as well as assignments? My issue in the past was that they often didn't have many of the teaching resources.

Intro to coding is a fascinating one. From a marketing/business standpoint it makes sense, but 3-4 it was extremely frustrating to see dozens of intro coding courses, but practically nothing for intermediate programmers. Thankfully we're past that point for the most part.

It depends on the class but they have videos and assignments there .

Because it sounds cool...I remember when I was applying for college (2001), nanotechnology and biomedical engineering was all the rage. Glad I stuck with electrical engineering.

I thought biomedical engineering still has a lot of potential to offer?

My friend who majored in biomed kept getting passed over during her job search. Turns out all the biomed companies just wanted to hire mechanical engineers. (She did eventually find a job in her field.)

I guess it's analogous to the data science degrees popping up today. Will be interesting to see if it ends up as a fad degree or a legitimate career path.

My impression (as someone who was once very interested in biomedical engineering) was that it was always mostly mechanical engineering plus some bio/chem and teaming up with doctors. I didn't end up going that route but I got an ME from a school where a number of mechanical engineering professors worked with the affiliated hospital on projects.

The people I knew that majored in biomedical engineering went on to med school... Probably one of those areas that is always in the news and science magazine but still too early to revolutionize life.

I remember when Dolly was cloned and we would have a whole new industry...

Evidently I am ignorant based on comments from people who are obviously better informed than me.

What is the field that is going to revolutionize curing diseases through genetics engineering etc?

Or bioinformatics ~2009?

I'm just curious -- where do you think the puck will be? I've had a number of younger acquaintances ask for career advice. Pursuing some kind of data science seems like an obviously smart direction now, but I've wondered if this, as well as traditional CS career paths, may be in danger of becoming over-saturated areas, now that everyone views them as sure paths to a job that pays well.

By its nature data science work is very undefined. There is not yet a widely established design path for data science as there is for software development. The risk for someone starting their career in data science is that they will end up in an organization that doesn't know how to data science. So I'd recommend steering young folk to larger teams that have PhD level Statisticians.

On the tech side front end or data science both seem like good options.

Companies are need a store front, so front end work will continue for a while, until it is super easy for any joe to make a professional website.

Data Science looks promising too, because it is automating and solving problems that previously could not be done.

However, outside of tech the world still needs skilled blue collar workers. For example, I don't see carpenters being automated away any time soon. I hear some of those jobs pay better than tech work too.

i think there is a natural barrier for these paths being too hard and too mathy for most people. people have known for decades that engineering is a good path.

Yep, a lot of the people who want to get into it run away when they find out you need to know how to code and understand the math. For some reason there is this idea that it is easy money.

I simply mean in terms of the availability of online courses. Eventually there will be harder CS courses online. While there's been enormous movement in this direction in even the last 2 years, there's still a lot of room for growth and improvement.

math is the secret weapon here. Cybersecurity, data science, programming

I think it’s because data science has a more immediate, broader applicability than computer science. Not everybody needs to know how to program a full application; but they should be able to load in a dataset and statistically analyze it. Looking at the type of people taking Data 8 compared to CS61A (the introductory programming course), I would say the former is a fairly diverse crowd (political science majors, economics majors, biology majors, etc.)

It is also a possibility that Data Science is an easier topic to learn than Computer Science, and thus more popular.

It might be more popular as many data science courses use Python and therefore students don't have to get their code to compile.

Seriously though, I think people are drawn to data science out of a desire to create stories with some underlying support (data/evidence) in order to influence policy or business decisions.

61A also uses Python.

It says fastest growing course. At my university it's also the fastest growing course. It grew from 0 to 50 students in the last semester.

You're talking only about the public + free MOOC stuff, right? I think it's reasonable for that to be biased toward less specialized stuff.

Internally, Berkeley definitely isn't based toward the intro-level stuff. Quite the opposite. But the most polished, rehearsed, mass-manufactured classes are certainly the gigantic intro-level ones everyone takes.

> I find it curious that there are so many courses for data-science related subjects

Is it that there are more courses in data science relative to other topics, or is just more marketing around these classes? It costs Berekely essentially nothing to pump out some press around the release of course materials in data science.

GaTech has a lot of good MOOC Computer System courses.

Yeah they seem to be one of the few that's really leading the way here.

All these courses in data science ain't gonna solve science biggest mystery, that is consciousness. Too bad we don't focus on that.

/Cognitive scientist

We are starting to see a bit of a pushback although it's hard to discern amongst all the hype. There's some recognition that deep learning is just one technique that happened to pop (to use Rodney Brooks' term) for a variety of reasons but that we haven't made huge progress in cognitive science and other fields.

Deep learning is the current shiny toy but I suspect we'll find it isn't actually sufficient for a lot of things we want to do and we'll run into a wall a lot of people aren't expecting.

That's why it's called data science not cognitive science. Duh!

Our computer security course at Berkeley regularly attracts 600+ students. Berkeley is doing fine in terms of traditional course offerings.

I meant in terms of online courses. Would love to see that class put online.

All the materials (except lecture videos) are available online; just google CS161 Berkeley.

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