
Review of the First Three Johns Hopkins Coursera Data Science Courses - ignacioelola
http://www.jeffheaton.com/2014/05/review-of-the-first-three-johns-hopkins-coursera-data-science-courses/
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brudgers
_I am probably not the typical student for these courses. I already work as a
data scientist. Additionally, I am a Ph.D. student in Computer Science, and
the author of several Artificial Intelligence (AI books)_

Coming from HN, this is one of the great things about Coursera courses for me.
But it seems to freak out some students when the bar for top of the class is
set by people with extensive professional backgrounds. [1] What can make for a
corrosive environment is that there is often someone who stokes their ego by
talking about how easy the course is in the forums...though this is rarely
anyone at Jeff Heaton's level [or an HN'er].

[1] I took an introductory programming course with an HNer who had been on a
c++ technical committee. But I am used to looking around the table and not
seeing the dimmest bulb in the chandelier.

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sbhat7
Google web cache:
[http://webcache.googleusercontent.com/search?q=cache:8LyCs4h...](http://webcache.googleusercontent.com/search?q=cache:8LyCs4hlAsYJ:www.jeffheaton.com/2014/05/review-
of-the-first-three-johns-hopkins-coursera-data-science-
courses/+&cd=1&hl=en&ct=clnk)

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rmb177
I completed the first two courses in the first four-week block and am
currently taking the third course. Mr. Heaton's review provides a really good
summary of what to expect.

Overall, I'm also happy with the course. I was expecting a little more degree
of difficulty and a little higher workload than what I've run into so far. If
you're an experienced developer with a Github account, the first course can be
completed in a couple of hours. The R Programming course was more along the
lines of what I was expecting. So far, the third class is closer to the first
than the second (in terms of difficulty...does require a bit more time).

Going into the course, I wasn't expecting to come out a "data scientist" ready
to land a full-time job in the field. My experience so far confirms that
expectation. But it's a fun course, a good way to get started in R, and a good
way to spring-board your exploration into the field. It's nice to have
deadlines as a motivation to keep on track and stay on a track for learning.
I'm hoping by the end of the curriculum I feel confident enough to try and
land some small free-lance projects.

I'm paying for the "official" certification. I'm not sure if it's really worth
it, but at $50/class it's not putting a big dent in my finances.

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sanderjd
My interest was piqued by this article, but upon initial investigation, I'm
having trouble stomaching putting $50 toward learning how to use github... Any
reason I shouldn't just skip ahead?

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rmb177
The $50 is an optional fee for the Signature Track. If you pay for this track,
Coursera adds some extra checks to validate your identity and issues you a
"Verified" certificate.

You can still take all of the courses for free and get a certificate, but
Coursera won't validate that you actually did the coursework.

I'm guessing you could pick and choose to pay for only certain classes, but
you have to pay for all of them to earn the overall "Specialization"
certificate.

See the following url for details:
[https://www.coursera.org/specialization/jhudatascience/1?utm...](https://www.coursera.org/specialization/jhudatascience/1?utm_medium=listingPage)

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yen223
To employers out there, how much does the fact that a candidate completed a
Coursera/Udacity course factor into your decision to hire him? Does it matter
if he paid for the certificate?

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eitally
0, pretty much the same as a traditional degree. It wouldn't even cross my
mind to ask about the certificate.

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brg
The certificate does not matter to me, but completing the course is a bonus if
the candidate mentions it. It demonstrates to me the ability to affect self-
improvement.

~~~
RBerenguel
I'm about to finish the ML course (after 3 tries.) And essentially you have to
battle the huge amount of boredom and keep doing it week after week without
stop. Just yesterday I took a quiz without even watching the lecture or
anything: scored 4.5/5\. Proceeded to finish the programming assignment, again
without bothering with the lecture, 100/100\. Not to say I won't watch the
lecture later, I want to make sure I know exactly the way it's done (will do
today probably, I wanted to finish the assignments fast this week because last
I almost passed the deadline.) But they are passable without that much effort.
Just persistence.

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ermintrude
I've become good at understanding lecture videos played back at faster than
real-time. Sometimes I can get up to 2x playback speed with VLC and still
understand what's being said. It'd be so boring watching things in real-time.
I'm so glad Coursera let you download videos so you can do that.

~~~
craigching
Yes, absolutely have to agree with this! The lectures can be time consuming in
some of the classes and this really makes it bearable. You really just need to
get the gist of what the professor is presenting and then use the lecture
notes/presentations as-needed. I would probably skip the video lectures
altogether, but occasionally there is something in the lectures themselves
that you need to pick up on. At least this has been my experience with the
"Data Analysis" classes, my first foray into MOOCs.

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antisocial
I think we are doing the specialization at the same time.

As an open source maintainer I can say that “question asking etiquette” is NOT
common knowledge.

I don't have any problem with having that as a credit earning question. But I
hated it when it carried 20-25% of the credit in one of the quizzes. This
course could have asked more questions in the quiz to test its students more.

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some_pythonista
Could anybody say how these compare to Andrew Ng's ml-class? I'm almost
through ml-class and I'm wondering, whether these courses add much.

~~~
antisocial
Andrew Ng's class has hours and hours of content each week and it is pretty
thorough (I am just auditing, don't have much knowledge).

I feel that Data Scienctist Toolkit was a slacker course, it could have
covered more topics or tested the students more. Five questions in each quiz
and four such quizzes and you are done.

These courses are morale boosters, you can earn a certificate in four weeks
and that keeps some motivated.

I like the concept of five late days for the whole course, lets you stay on
track despite one's busy schedule.

