
Berkeley offers its data science course online for free - seycombi
http://news.berkeley.edu/2018/03/29/berkeley-offers-its-fastest-growing-course-data-science-online-for-free/
======
jamestimmins
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).

~~~
resolaibohp
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.

~~~
mr_toad
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.

~~~
wakkaflokka
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.

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

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

------
bartart
People at Berkeley view this class as kind of a joke. The average grade is
insanely high and the topics are covered in much less depth than just the
normal intro cs or stats classes.

[https://www.berkeleytime.com/grades/?course1=7765-all-
all](https://www.berkeleytime.com/grades/?course1=7765-all-all)

~~~
wwweston
Good to know. I looked at this Berkeley course (along with some private
offerings like General Assembly) and got the feeling that they really weren't
worth the investment for a guy with a Math degree, a CS minor, and programming
experience going back to childhood.

But I think I'd like _some_ kind of formal, credentialed program that would
build on my existing linear algebra + software skills (and address the
weaknesses in my statistical understanding that I _know_ are there based on
how I felt about my grasp on the related material for even the classes I
passed)... and maybe isn't quite as big an investment as a full-fledged
master's degree.

Anybody have any suggestions?

~~~
austenallred
This is exactly what we built at Lambda School - our data science/machine
learning program has math (linear algebra, calculus) and CS (python) as pre-
requisites, and is designed to train the rest of the way. It can also be free
until you get hired in field.

It is a big commitment - 6 months full-time or one year part-time.

[https://lambdaschool.com/courses/ds/machine-
learning/](https://lambdaschool.com/courses/ds/machine-learning/)

------
gnulinux
I'm a UC Berkeley alum. When I was there this was a course taken by humanity
majors to learn some programming so that their Resume looks cooler. majority
of STEM majors take CS 61A (SICP) or E7 (Programming in MATLAB). Just noting
this as a context, this is not the class intended for CS majors; this :
[https://cs61a.org/](https://cs61a.org/) one is.

~~~
cbHXBY1D
Or if you want to get experience with data science: Stats 134 (easy), CS188
(medium), CS189 (hard).

~~~
jwilbs
I personally thought Stats 134 was the hardest of those courses by far (though
I took it under a visiting professor who was needlessly difficult). 188 was a
breeze, and I believe the full course is offered for free on edx

~~~
gnulinux
CS 189 (with Sahai) (Machine Learning) was by far the hardest course I took in
my life.

------
master_yoda_1
I think this is a bad trend. These university make basic courses free to gain
popularity and then ask for big money for their real courses.

This is bad in two ways:

1) The people taking these courses do not learn much for the effort and time
they spend. Also it gives them illusion that they know enough as they take
course from big university.

2) Industry is already so confused in hiring, they hire by name. So even you
take these courses and study in depth on your own you can't get hired. Someone
more qualified can not get hired just because they can't pay 100k to get a
degree in machine learning from one of these big university.

This is really a bad trend and we should spend time on real courses. Everyone
knows that TV series are waste of time, these courses are like TV series. Stop
watching them.

~~~
nerdponx
A course called "[Intro to] data science" should be taken about as seriously
in hiring as "Intro to computer science", or "Intro to mechanical
engineering". There's no reason these courses should bear any weight in
hiring, and it's disingenuous to attempt to lead people to believe otherwise.

~~~
master_yoda_1
I was talking about people who study in depth on their own after the course.

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anonymous5133
Always boggles my mind with these "free" online courses that still stick to
old method of "registering" for the class and then following a regimented
schedule.

Seriously, just upload the lecture videos, put the homework online and
textbook. Add a message board and you're golden.

~~~
lucasverra
Having ~7 moocs and 1 udacity nanodegree under the belt, here is my anecdata :

Before Coursera, i was never able to finish anything on MIT opencourseware.
Free flow of information need too much commitment from my end to be digerable.

It was the structure given by

> "registering" for the class and then following a regimented schedule.

that i managed to start and finish. Disclaimer: I discovered Coursera after
grad school

------
benhamner
For those interested in a practical, hands-on course, we just released one at
Kaggle
[https://www.kaggle.com/learn/overview](https://www.kaggle.com/learn/overview)

------
jph
Berkeley and the UC schools are making major strides in online education,
including edX participation and on-campus projects. If you're interested in
Berkeley and data science, there's an online masters program too. (Disclosure:
Berkeley is in my client roster).
[https://requestinfo.datascience.berkeley.edu](https://requestinfo.datascience.berkeley.edu)

~~~
appleiigs
US$65K for tuition

~~~
amrx101
LOL. As someone from third world where local currency is enormously devalued
with respect to US$, I wonder why would anyone do this?

~~~
pc86
If you think that's bad look up Executive MBA programs. Corporations basically
give a university a nearly six figure donation for their top executives to get
a tax-free tuition benefit for a rubber-stamped MBA largely indistinguishable
from a real MBA.

I know the above probably sounds like sour grapes, I don't have an MBA or any
graduate degree, I just think the whole Exec. MBA thing is a total scam
against the companies paying for them and a huge cash cow for universities.

~~~
fjsolwmv
Exec MBAs are not for top execs. "exec" is just the marketing tag. It's
exactly the same as exec Masters that GATech and UW do to extract tuition
reimbursement funds from big companies like Microsoft

------
seycombi
direct link: [https://www.edx.org/professional-certificate/berkeleyx-
found...](https://www.edx.org/professional-certificate/berkeleyx-foundations-
of-data-science#courses)

(There are two ways you can follow the course: Certificate Program is paid,
but the AUDIT program is free)

~~~
based2
Pursue the Program ( $357.30 USD - old: $397 )

------
dpflan
Have anyone followed the curriculum suggested here?

> [http://datasciencemasters.org/](http://datasciencemasters.org/)

------
graycat
Okay, here's a view of what appears to be part of the course:

We have a course (right a school application of stuff taught in school!) with
two teachers, that is, two sections of the course, each section with its own
teacher and its own students. At the end of the two courses, that is, the two
sections, we want to compare the teachers. So we give the same test to all of
the students from both courses.

Suppose one section had 20 students and the other one, 25 -- the point here is
that we don't ask that the two numbers be equal; fine if they are equal, but
we're not asking that they be.

So, there were 45 students. So, get a good random number generator and pick 20
students from the 45 and average their scores; also average the scores of the
other 25; then take the difference of the two averages.

That was once. It was _resampling_. Now, do that 1000 times -- remember, we
have a computer to do this for us. So, now we have 1000 differences. If you
want, then, "live a little" and do that 2000 times. Or, for A students, do all
the combinations of 45 students taken 20 at a time. Ah, heck, lets stick
closer to being practical and stay with the 1000.

Now, presto, bingo, drum roll please, may I have the envelope with the actual
difference in the actual averages of the actual scores in the two classes.

If that actual difference is out in a tail of the empirical distribution of
the 1000 differences from the resamplings, then we have a choice to make:

(1) The two teachers did equally well but just by chance in the luck of the
draw of the students one of the teachers seemed to do much better than the
other one.

(2) The actual difference is so far out in the tail that we don't believe that
the two teachers were equally good, reject the _hypothesis_ that there was no
difference, called the _null hypothesis_ , and conclude that the teacher with
the higher actual average was actually a better teacher.

Sure, it happened that the real reason was that one section of the course
started at 7 AM and was over before the sun came up and the other section was
at 11 AM when nearly everyone was awake. We like to f'get about such details!
Or, sure, we might get criticized for a poorly _controlled_ experiment.

This is also called a _statistical hypothesis_ test or a _two sample_ test. It
is a _distribution free_ test because we are making no assumptions about
probability distributions of the student scores, etc. Since we are not
assuming a probability distribution, we are not assuming a probability
distribution with _parameters_ and, thus, have a _non-parametric_ test. Uh, an
example of a probability distribution with parameters is the Gaussian where
the parameters are mean and standard deviation.

Such tests go way back in statistics for the social sciences, e.g.,
educational statistics.

In more recent years, leaders in resampling include B. Efron and P. Diaconis,
recently both at Stanford.

Why teach such stuff? Well, some parts of computer science are tweaking old
multivariate statistics, especially regression analysis, and calling the
results _machine learning_ and/or _artificial intelligence_ , putting out a
lot of hype and getting a lot of attention, publicity, students, and maybe
consulting gigs. Also the newsies get another source of shocking headlines to
get eyeballs for the ad revenue -- write about AI and the old "take over the
world ploy"!

So, maybe now some profs of applied statistics, what for a while was called
_mathematical sciences_ , etc., or other profs of applied math want to get in
on the party. Maybe.

What can be done with resampling tests? I don't know that there is any
significant market for such: Long ago I generalized such things to a curious
multidimensional case and published the results in _Information Sciences_. The
work was a big improvement on what we were doing in AI at IBM's Watson lab for
_zero day_ monitoring of high end server farms and networks. Still, I doubt
that my paper has ever been applied.

One of the best areas for applied statistics is the testing of medical drugs.
Maybe at times resampling plans have been useful there.

I have a conjecture that resampling plans are closely tied to the now classic
result in mathematical statistics that order statistics are always sufficient
statistics. Sufficient statistics is cute stuff, from the Radon-Nikodym
theorem in measure theory and, in particular, from a 1940s paper of Halmos and
Savage, then both at the University of Chicago. Some of the interest is that
sample mean and sample variance are _sufficient_ for Gaussian distributed
data, and that means that, given such data, you can always do just as well in
statistics with only the sample mean and sample variance and otherwise just
throw away the data. IIRC E. Dynkin, student of Kolmogorov and Gel'fand, long
at Cornell, has a paper that this result for the Gaussian is in a sense
unstable: If the distribution is only approximately Gaussian, then the
sufficiency claim does not hold.

Other applications of resampling, such applied math, etc. might be in US
national security. E.g., maybe monitoring activities in North Korea and
looking for significant changes ....

Maybe there would be applications in A/B testing in ad targeting, but I
wouldn't hold my breath looking for a job offer to do such from a big ad firm.

For all I know, some Wall Street hedge fund or some Chicago commodities fund
uses such statistics to look for significant _changes_ in the markets or
anomalies that might be exploited. I doubt it, but maybe! Once I showed my
work in anomaly detection to some people at Morgan Stanley, back before the
2008 crash of _The Big Short_ , and there was some interest for monitoring
their many Sun workstations but no interest for trading!

Net, IMHO for such applied math: If can find a serious application, that is, a
serious problem where such applied math gives a powerful, valuable solution,
the first good or much better solution, with a good barrier to entry, and
cheap, fast, and easy to bring on-line and monetize, then be a company founder
and go for it. But I wouldn't look for venture funding for such a project
before had revenue significant and growing rapidly and no longer needed equity
funding!

Otherwise look for job offers (1) in US national security, (2) medical
research, (3) wherever else. But don't hold breath while waiting.

Now you may just have gotten enough from about 1/3rd of the Berkeley course!

~~~
subroutine
What you are describing is known as bootstrapping (if sampling with
replacement) jackknifing (if sampling without replacement), or (in the case
you want to run a significance test, and not simply create a distribution or
stats like confidence intervals) a permutation test. I think you already know
that; I'm just mentioning in case others want to look these up by name. Also
while they can be called 'distribution free' it only means you are not
assuming a prefab distribution. If you want to perform a significance test
you'll be creating (explicitly or implicitly) a distribution of your
calculated statistic (known as the empirical distribution). If you want to be
very explicit about this, you can plot a PDF or CDF of your sampled stats just
like you could with a gaussian, exponential, poisson, etc., distribution.

We teach these methods to our students in intro stats at UC San Diego. Have
been for as long as I've been here (5 years). Last year a data science program
was also created here at UCSD. I've TA'd a flagship course in that program
too. It's almost exactly the same content; the major difference is imo are the
faculty personalities. The stats profs are smug, while the data science profs
are energetically self-important. They teach the same shit. Self motivated
students with a STEMy personality tend to learn more in the stats courses
because the profs drive on hard core theory; on average though, students do
better in the data science course because the profs are so bombastic the kids
walk out of each class thinking they are basically ready to join the fellas
over at Waymo on some machine learning projects - maybe even show 'em a thing
or two, cutting edge tricks learned back at the ol' uni.

~~~
graycat
Nice!

Yup. Thanks.

> known as the empirical distribution

Yup, and I wrote:

"out in a tail of the empirical distribution"

Yup, "rank" tests, "permutation" tests: With my TeX markup:

E.\ L.\ Lehmann, {\it Nonparametrics: Statistical Methods Based on Ranks,\/}

And, yup, again with my TeX markup,

Bradley Efron, {\it The Jackknife, the Bootstrap, and Other Resampling
Plans,\/}

Last time I knew, Roger Wets was at UCSD. He read one of my papers and
suggested JOTA where I did publish it!

------
Treegarden
why is there no syllabus - as in a list of contents? I want to know what
really is behind this buzzword stuff.

~~~
QML
It's probably around the same content as this semester's iteration:
[http://data8.org/sp18/](http://data8.org/sp18/). I would read the online book
for more info.

------
frabbit
There are at least two big turn-offs to this course at first blush: 1) they
insist on using anaconda (effectively another package manager complicating the
already layered interaction of system pip, virtualenv, virtualenvwrapper etc
). 2) they use Microsoft VisualStudioCode (so, inevitably a good deal of time
in this course will be spent learning how to navigate a bloated IDE)

~~~
pc86
Neither of which are the least bit consequential for anyone more interested in
learning about _data science_.

~~~
frabbit
As it turns out I was wrong about one of those points: in fact the course
prefers that you avoid MSVisualStudioCode and instead use the Jupyter
notebooks.

But, this bring us back to a much more central topic in _data science_ : the
tools and environment DO matter. Hugely.

Reproducibility is central to not just data science but all science. This is
facilitated by the use of Free, Open platforms which adhere to common
standards.

Imagine trying to debug why someone has a different answer than you when there
are x*variant-of-program environments in which they have obtained their
answer?

At the most basic level this course should be distributing a Docker image or a
VM image of some sort in order to ensure that everyone has the same version of
the software.

Even if you do not care about any of the above, please, shed a tear for the
student who would like a simple setup.

Thank you.

------
meri_dian
What exactly is Data Science? It seems like such an overused term and the
value of the subject really gets diluted for me when I see charts in Tableau
being offered as examples of "data science".

What's the difference between, say, a Master's program in Computer Science
where one studies machine learning and a Master's program in Data Science? Am
I wrong for thinking the Data Science program weaker?

~~~
gaius
_What exactly is Data Science?_

Data Science and DevOps are both just labels for things people have been doing
under more mundane terms for 40-odd years.

Even Machine Learning is just a trendy buzzword for what used to be called
Predictive Statistics.

~~~
nl
I did stats before data science was a thing, and then ran a data science team
afterwards, and it's dramatically different.

I've never seen any stats text book or course discuss techniques for dealing
with large amounts of data to any significant level, but in data science that
is a core part of what you do.

I ran production systems before DevOps and after. Again, it's very different -
prior to devops, there was no emphasis at all about using software engineering
techniques to manage and deploy software. The most you'd get was some scripts
_maybe_ kept in source control if you were lucky.

Now I run an AI company, and a key part of the ML we use involves generating
structured text files from images. I _guess_ predictive statistics is
technically a correct label, but the tools and techniques are so dramatically
different that that thinking of them as separate fields is more correct than
incorrect.

~~~
gaius
I struggled for years to understand DevOps because I couldn’t see what was
different from how I worked already... the answer was nothing :-)

~~~
fjsolwmv
DevOps gave a name to what your were doing, that other sysadmins and product
devs did not. Is that bad?

------
carlosgg
Berkeley also used to have this Data Science with Spark series on edX but they
taught it just the one time and now even the archived versions of the courses
are closed.

[https://www.edx.org/xseries/data-science-engineering-
apacher...](https://www.edx.org/xseries/data-science-engineering-apacher-
sparktm)

~~~
swedishfish
I'm so sad this was never taught again. It's the most useful MOOC I've taken,
and it motivated me to start using pyspark on a daily basis. I would say the
class is better for learning pyspark than actual data science concepts though.

------
tenkabuto
For those interested, you might want to check out
[http://data8.org](http://data8.org) I'm not sure how it compares to the OP
course, though.

------
csjr
Does anyone know how it compares to bootcamps like DataCamp[0] for e.g?

[0] [https://www.datacamp.com](https://www.datacamp.com)

~~~
analognoise
Boot camps are stupid.

Go to community college. It's ridiculously cheap, and the credits are worth
something.

~~~
barry-cotter
If you can learn skills that get you a job from a boot camp they’re not
stupid. The fact that Lambda School and App Academy don’t get paid unless you
get a job and they still exist suggests rather strongly that they get people
jobs.

~~~
analognoise
Skills without the foundations are going to be useless in a technology shift
or economic downturn. Also getting a job is great - how about keeping one, or
advancing?

It seems very short sighted; community college only takes 2 years. Your career
lifetime is what, 45 years? The ROI is insane, why would you shortchange
yourself?

~~~
barry-cotter
I think you radically underestimate how useful work experience is, both in
terms of what you learn at work, and in terms of actually having money, rather
than not having any. Technology shifts happen all the time. Being able to keep
up is a necessary skill but it’s a great deal easier to learn the latest JS
framework if you know another one. Given the large number of degreeless
programmers and the fact that CS graduates are a minority of working
programmers I think we can take it for granted that neither are _necessary_.

The only boot camp grad I’ve spoken to personally had a degree in Human
Genetics, did App Academy after deciding they didn’t want to do it as a career
and got a Django job out of it, despite having no knowledge of Python. They
had learned enough Ruby on Rails in three months of more than full time work
to impress in an interview.

Community college is _only_ two years? That’s half the time needed for an
actual Bachelor’s degree, but radically less valuable than one for getting a
job. There are two sensible reasons to get an A.A. or A.S., to get a job
afterwards or to get the Bachelor’s that comes after. You need to pay for it
but more importantly you can’t get a real job during it and you need to eat
and live during it.

Even if a good boot camp is strictly inferior to a median A.S. in Computer
Science the first can still be a better choice purely because it takes less
time. Having known people with Bachelor’s in CS who can’t code I doubt an
Associate’s is better.

Who would you hire? The boot camper with two and a half years work experience
or the A.S. graduate with one? What if neither of them has a B.A. to go with
it? What if both do?

------
erokar
Many if not all of the courses on Edx has a free audit option, like this one.
It gives you no certificate and often you cannot access or submit exercises.

------
simpleAdam
this would seem to be a playlist

[https://www.youtube.com/watch?v=xcgrnZay9Yc&list=PLFeJ2hV8Fy...](https://www.youtube.com/watch?v=xcgrnZay9Yc&list=PLFeJ2hV8Fyt7mjvwrDQ2QNYEYdtKSNA0y)

------
daveheq
Who has time for this?

~~~
Double_a_92
Unemployed desperate people thinking that this will get them a job.

