This program was just announced. So the first cohort hasn't started yet. There are a bunch of OMSCS students in this thread though who have taken some of the classes.
Back when they were oDesk, I lived in a remote area with no technology jobs. I ended up working for oDesk for a 6 mo stint (Actually for oDesk not a third party). So I'm familiar with the ecosystem. I was just wondering if it was web dev or something more specialized.
It's definitely a race to the bottom but it also represents a good opportunity for junior developers even in developed countries. I've gotten $35/hour jobs before which isn't great but it isn't peanuts either.
This happens all of the time. For example, it is very common to live in NJ and work NYC. In that case, you pay NYC tax, NY and NJ tax and receive credit for your NJ tax based on the NY state tax you pay.
Not to be pedantic but the article is about NY state, NJ, CT etc. Many people live in NY state but do not live in NYC. Additionally, as the article explains, some people try to live outside of the state for over half the year in order to avoid paying state taxes. Presumably, there are considerable financial and personal incentives for maintaining some presence in NY state. So, the answer to your question is yes because the people in question are still typically maintaining a residence in a high tax state.
Soaps and detergents are not hydrophobic. They are part hydrophilic and part lipophilic which is how they pull lipids into the water when you wash something.
> we accept some risks while walking bicycles across multi-lane highways at night without watching for oncoming traffic.
The accident occurred on a surface street in a fairly busy area right down the street from ASU's main campus where there tend to be a lot of pedestrians and bikers
It looks like the script is there to block phantomjs based bots (which can evaluate JS). It seems to check for the global properties mentioned in this article:
I'm taking ML in the fall and I've already taken ML4T and RL. So the order I went in was ML4T -> RL -> ML. My understanding is that's roughly in order of increasing difficulty (although I do suspect RL has gotten a little harder over the last year).
Both ML4T and ML have some RL component. So there's overlap. If you're new to python, then I'd definitely recommend ML4T first because it spends a bunch of time on Pandas/Numpy. ML4T is also easier to get into than ML if it's your first semester.
Take a look at the course review site also. It's really helpful in estimating difficulty:
I think most Computer Science education is focused on fundamentals. So there isn't anything that's CS that will focus on the latest thing. That said, I think any of the specializations will make you a better software engineer. Also, the price... you just can't beat 7K. I don't think there's anything out there that competes on price and quality.
No, they admit people without a BS in CS but I think they want to make sure you'll be able to succeed in the program. So it helps to have professional software engineering experience or some other coursework.
I'm halfway through the OMSCS in the machine learning specialization. It has been a great experience so far and definitely worth it for me.
A couple of things to consider: As you mentioned, it is more focused on Computer Science than Software Engineering/Development. There are a couple of Software Engineering/Architecture/Testing courses but I haven't taken them so I can't comment on how relevant I think they are to my day job.
It's an incredible bargain... 7-8K for an MS (not an online MS) from a top 10 school in CS. That on it's own makes it worth it for me.
It's not easy and it's not like a typical Coursera/Udacity course. Depending on which courses you take it can be quite challenging (which is a good thing). You typically don't have much interaction with the Professors but there are a lot of TAs and other students to help you along the way.
Here's a reddit in case you haven't come across it that answers many questions:
I've completed three of the courses in the ML specialization on coursera (The UW one) and I'd definitely recommend it. It uses python (which I prefer over R or matlab) and it's really well organized with good lectures.