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.
I like this idea and the framing of it a lot
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.
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.
Also need done good pr I guess :)
“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 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.
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.
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.
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?
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?
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!
> ...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.
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.
Do you propose that they should take a MOOC like this one, or that they should be replaced by MOOC graduates?
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.
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.
I remember when Dolly was cloned and we would have a whole new industry...
What is the field that is going to revolutionize curing diseases through genetics engineering etc?
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.
It is also a possibility that Data Science is an easier topic to learn than Computer Science, and thus more popular.
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.
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.
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.
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.