While this program seems interesting, valuable, and a step in the right direction, the minute I read the $60,000 price tag I nearly choked on my gum and quickly pressed the back button. Coursera classes for me at least in the near future, hopefully within the next few years these degree programs will scale a little better and costs will plummet.
It sounds like a great degree program, and I'm encouraged to see it go on-line, but the $60K price tag seems over the top to me. Didn't Georgia Tech just announce an online MS for $7,000?
I'm just not sure this makes sense. While tuition has gone way up at Berkeley, most science and engineering degrees are considered academic, rather than professional degrees, so the fees are considerably lower.
So yeah, law or business school tuition+fees (professional program) are between $50-$60 a year, but academic programs (which includes engineering) is less than half that. A lot of this comes down to whether data science will really be a "professional" degree with high earnings.
Truth is, it might, I'm not ruling it out. I got an MS in Industrial Engineering from Berkeley after a math degree, hoping I could get something practical. I had some good experiences, but I also spent what was to me a depressing amount doing proofs about convex sets and stochastic processes. I probably shouldn't complain, that's what an academic degree program is. Maybe something like a professional program would have been much better for me.
Another question is whether holding this degree is valuable independent of what you learn. I know that may sound silly, but it makes a difference. Suppose you were allowed to study law courses on coursera. How much would it be worth it to get to say your degree was officially from Harvard and now qualify for the bar, even if all you did was quietly watch the videos and do the homework? More than $60k, I'd say.
Could the same be said for data science? You've watched the identical videos and done the homework... how much of a premium would it be worth to say you got an MS degree online from Berkeley? It would be worth something sure, but not as much as the law scenario. There's no "data science bar" that can prevent you from practicing, and there are so many different acceptable degree paths to becoming a data scientist. And while some may reasonably dispute this, I have found high tech to be more concerned with what you know than where you learned it.
All in all... sounds like a great degree, but 60K definitely gives me pause.
You are being incredibly nice and understanding, and you shouldn't be. $60k for an online class is just plain robbery. I'm sick and tired of this bullshit that higher education has been ramming down our collective throats courtesy of a system in which easily available, seemingly infinite credit on the part of the students allows colleges to continuously seek out new revenue by burdening students with ever increasing debt.
Its funny to me that state schools, including Berkeley, used to have reasonable tuition up until the early 1980's, until the baby boomer voters in each respective state demanded lower taxes and the politicians obliged by cutting state tuition assistance year after year. When the state paid 90% of a student's tuition, the colleges didn't have the option of constantly jacking their price up. Suddenly, when it was student debt paying the bills, the colleges stopped doing anything to cut the bloat.
Thank you Berkeley for again reminding me what a scam the academic institutions have become.
I agree. I desperately want to go back to school (didn't attend college in my youth, and feel I am hitting the limits of self-education), but I'm not willing to put half the value of a house on a 4 year program.
a) My house is a 10 minute bus ride from UC Berkeley, b) I am plenty familiar with house prices and down payments as it took us two years to find this one, and c) I was talking specifically about a 4-year degree, which is likely to cost more than $60k.
Yeah, they did; however, the Udacity program doesn't begin to accept new outside students until next year, and I think the program is closer to $8000. Anyways, it's such a steal, and I think the MS in comp sci is more flexible than a niche degree like data science. You could teach comp sci at a 2 year college, for example; whereas, a data science masters will just get you a job in the industry.
> How much would it be worth it to get to say your degree was officially from Harvard and now qualify for the bar, even if all you did was quietly watch the videos and do the homework?
I think you make a crucial point. Even if you were eligible in both situations to sit for the bar (say by virtue of California's looser education requirements), the Harvard name would be worth a heck of a lot more than $60k, at least assuming you were curved against other Harvard students in your examinations. The value of a law degree, at least in many circles of the legal profession, is 50% name brand, 40% sorting within the class, and maybe 10% education.
A few fields take this trait of education to a counterproductive extent (banking, consulting, law), but I think the unfortunate truth is that for most fields, the value of a degree is more than 50% in branding and sorting, rather than education. Even in engineering, outside some progressive places, I wouldn't put it much below 50%.
Still less than 60k. Unless it has the same bar the Harvard of today has, its not the same brand.
Part of the thing with Harvard, is getting in is so hard that if i'm interviewing a candidate half the job is already done. But if anyone is allowed into the program (and why not?) then that filter is gone... so the name while still having value as a great program loses the value as a filter.
Looks like I can't edit... but I shouldn't have said that academic fees are "less than half" the professional fees. That's true, but doesn't indicate just how much lower academic fees are. Total in-state tuition and fees for an academic graduate program is just under $16,000.
"Professional" degrees seem to be the new thing in Uni-marketing. The assumption appears to be it would be corporate sponsored, thus "cost does not matter".
They may need better data science behind their marketing.
I think these online technical degrees are just the next iteration beyond those one-year MS/MEng degrees that started popping up around 2000. This was back when "financial engineering" was the buzzword... that worked out well, didn't it?
I'm sure some firms will really like these degrees, but if you're a company (like Google, for example) insisting on a specialized degree, why not aim for PhD level? The income differential is marginal. In Chicago, PhD quants for funds generally start out around $140-150k. I'd argue those ppl are going to be more productive researchers/data scientists than what can be produced by an online program, due largely to value of a research oriented degree versus a skills oriented degree like this one. Even if there is a project component, it isn't the same as writing a thesis. I did an M.Eng way back, trust me: it just isn't the same as banging your head against a research topic for a couple years.
Also, for what it is worth, I found the target audience for one-year financial engineering degrees (Goldman Sach, etc) didn't respect the degrees as much as PhDs & MS w/thesis. They generally regarded it as a 5th year of university.
Criticism aside, I'm sure this is a great skill builder and ML is fun thing to learn. Not for $60k, though.
I like how they're still marketing that McK data science report. "Hadoop everything"
I agree with regarding an M.Eng as a 5th year of University, though it's a year of actual major-related courses rather than Engineering core classes. That's in fact how I decided to do one: my professor pointed out that it was not one year on top of four, but one on top of one. I got to take another year of electives that were applicable to my career, rather than the core classes (chemistry, etc) that I was required to do during especially the first two years.
I do disagree with this snark, though:
> This was back when "financial engineering" was the buzzword... that worked out well, didn't it?
My M.Eng (Cornell ORIE) was not in FE, though my department offered it. Their (admittedly biased) response to this line of thinking: if we had more financial engineers, we would have had people who actually understand the instruments that were being traded.
No doubt the people who created exotic Mortgage-Backed Securities were FE types... if only Moody's and S&P employed some as well, perhaps they wouldn't have been rated AAA. Then again, I'm assuming incompetence rather than malice; the ratings agencies did have incentives to lie.
EDIT: I did mine at CU back in 2003 in applied, not FE. I had many friends in FE, most all went into credit. I worked in trading for six years before quitting for a PhD. I do not place any value on an FE degree; looking back at the curriculum they offered, it is obvious that they were thinking the wrong way (the credit models they were teaching were complete shit and they had no concept of micro-structure; ironic given Maureen O'Hara teaches at the Johnson School).
EDIT2: The non-FE profs were and still are very awesome and remain good friends. Did you have Henderson?
Henderson is one of my favorites; I just went back for my 5-year and he was just so wonderful to talk to.
The FE curriculum never really interested me; I took OR methods in FE as an undergrad and the professor (a surfer-dude postdoc from UCLA, Will Anderson) convinced me that the efficient market hypothesis was mostly right.
After that, FE seemed a little... well, in the words of my classmate Ryan, "like looking at the surface of the waves to see if there are whales humping."
Mostly right is a mostly correct statement. Heard of Renaissance Technologies?
IMO, continuous time finance is a flawed model. Academics always rattle off theorems based on assumptions that do not hold across all time scales. I've worked with traders lacking any formal education that have a better understanding of the market than someone like Protter will ever have. Trading isn't about investing; it is about capital flows.
Narenl's comment is invisible due to a hellban. I don't agree with the comment, but it bears scrutiny.
narenl 5 hours ago | link [dead]
IMO This is not targeted towards individuals applying on their own.
I went to a walkabout of a similar online education company's office and asked them about the high cost for an online degree. The answer I received was
1. there is demand for this and
2. Most of the demand comes from people in the military serving in remote locations or people working inside other large organizations which foot the cost. and
3. They provide online infrastructure to courses of schools like UC-B, UNC etc and the colleges set the price so as not to dilute their "brand" because the online degree does not mention the fact that the degree was obtained online (this could have changed).
All in all, my initial shock was a bit tempered after hearing the realities involving all 3 parties : the school, the student and the online enabler.
Then you would most likely try emphasizing your managerial side with a more business type degree. They would need to slap at least some "data driven leadership" label on it. The name (and mode of delivery) makes it sound like a fairly poor choice for rubbing shoulders with other c-level types - something that is certainly a main decision point for executive masters.
I was just going to write this exact comment. I am a data scientist, and I do not think the $60,000 price tag is worth it. I might do it for $2,000 or $3,000, but that's already being a little generous.
The $60,000 price tag must come with a guarantee that you'll be making the proposed $110,000 - $130,000 salary range for at least 5 years. Otherwise, wow.
The best way to learn these things is to just dive right in. If one need's human interaction, the community is easily within reach (at a much lower cost).
Maybe one could argue that the "networking" is worth the price tag. Still, I get these "data meetup" emails about 5 times a day, which I could easily go to for networking.
Alternatively:
1. Take Coursera's excellent Intro to Data Science for free
2. Spend time doing Kaggle competitions and learning along the way
3. Profit
Incidentally Coursera's Intro to Data Science looks/ed to be a trial run for the first of three classes in the UW Data Science certificate program [1]. Each class is a bit over $1k. UW did the same with Intro to Computational Finance and Financial Econometrics.
Kaggle looks really cool. Thanks for this comment introducing me to it. They even have introductory competitions for those not well versed in data science with tutorials on things like python and random trees. Pretty sweet!
Perhaps rather than offering snappy responses with negative tones, you could offer something constructive to the discussion?
Say, what you think the skills required for day-to-day work as a data scientist are, and how you'd suggest someone develop them.
Perhaps also what you think the best approach is to credentialing your learning--grooming a pedigree--if neither Kaggle nor a degree program are good approaches.
Sure thing. Sorry for the previous terseness--I really really really hate this whole "Coursera --> Kaggle --> DS job at Facebook" meme when it (rarely) appears on HN, since it isn't even close to reality.
I'm not a data scientist, but I work with them very closely as an engineer and I've considered going down the same path. When I talk about data scientists, it's not a reference to any of the following:
> Engineers working with big data technology, like Hadoop, Storm, Kafka, who are essential but often uninvolved in model construction and evaluation.
> Analysts who develop models, then hand them off to engineers/IT to code them up (or keep them in Excel spreadsheets).
Instead, I'm thinking about someone with a specific background. They likely have a PhD, since that's an excellent way to experience the "ask-explore-code-test-present" workflow needed to answer an interesting question with real-world implications. The strong academic background is not necessary, but it greatly reduces friction during the research workflow (since you've spent 3-4 years in it). I'm getting a MS and working hard to make it as research-oriented as possible, fwiw.
This person also has a strong foundation in applied math. They might have worked on signal processing questions, applied algorithms for learning Bayesian network structure to proteins, or thought about the transition from Hopfield networks to RBNs or whatever awesome deep learning stuff is going on nowadays. A guy I respect described this quality as that of "a traveler," someone who can understand advanced work in a number of disciplines in addition to their specialty.
This person is an engineer. They learn languages easily, understand algorithmic complexity and think about the complexity of their models. They don't have to be Linus.
Finally, the person is forward-thinking. They understand that questions are motivated by business needs, and that answering these questions can have serious implications for the company or its partners. I should channel patio11 here!
Anyway I'm obviously very opinionated about this, but it's just one opinion. I'm happy to discuss this more with anyone who's interested, though--contact is in my profile.
What do you feel would needed to be added to the mix in order to prepare a person for the day-to-day responsibilities of a data scientist? Also which of those responsibilities do you see as most challenging?
There are two pieces Kaggle can't help you with: working through the full research cycle and developing performant models. It also emphasizes the wrong goals (for example error minimization is almost never your primary goal), but I need to work at some point and have spent enough time in this thread, so I'll skip that. :P Email me if you want to discuss, though.
Anyway Kaggle can't help with the full research cycle, since you're not identifying a relevant question yourself (this is surprisingly hard) or presenting your answer to others. The latter is hard for any route, since you really only encounter that type of volume in industry.
>> The program will cost $60,000, which school officials
>> said compares favorably with other professional degree
>> programs. Entry-level data scientists in the San
>> Francisco area can command salaries in the $110,000 to
>> $130,000 range.
$60,000 is laughable.
The justification based on salaries in California is laughable.
Unless a relocation package to California from anywhere there is internet is included in the fee?
I read an article in WSJ on rent hikes in SF this morning. Yikes.
I grew up in SoCal and miss it terribly, but my cost of living in Chicago is a tiny fraction of what it would be in SF and my income isn't behind California norms.
Maybe if I was 24 and looking for experience... I suppose you can apply the same argument to finance in Manhattan.
Nurses in the SF Bay Area make that much out of a 2 yr college that costs less than $3k. Hell, a LOT of people make that in the SF Bay Area without pursuing $60k educations. Not everyone should or want to be a nurse, but when you put things like that into perspective, it makes one wonder if the costs justifies the pursuit.
The price is ludicrous, especially considering the plethora of high quality free online courses.
I think their pricing may come from the fact that data science is the 'hot' field right now, so I suspect they'll be capitalizing on the corporations that will start pumping money into training their employees in data science. So I wouldn't be surprised if you start seeing some forture 500s covering the cost of this for a new hire.
Here's a problem with something like
data science:
First, the field is not professional
like law, medicine, or even some parts of
engineering. So, there's no licensing,
board certification, recognized
professional continuing education credits,
professional job performance peer-review,
legal liability, etc.
Instead, you can just say
that you have a Master's in data science
and know some programming, database, statistics,
etc.
Second, the degree isn't really a direct
approach to business or entrepreneurship.
So, the degree is aimed at making a person
an employee. This means that somewhere there
must be an employer including one ready to
create a job, recruit someone for that job, and
pay $120,000+ a year for the person.
Now, just
who is going to create this job, e.g.,
put it in their budget and partly bet their
career on it? And just why? I mean for
what the program taught in programming,
database, statistics, something else?
And where will the real money
actually come from, i.e., who with real
P&L responsibility will actually cough up
the $120,000 a year plus benefits, office space,
travel, etc.? Or, let's think about the
$120,000 a year: Ballpark, the full cost
stands to be twice that, $240,000 a year.
After two years on the job, maybe the person has actually
delivered some value or is ready to start.
So, the two years is $480,000. Heck, guys,
even in Silicon Valley, that's a large
seed round or a small Series A for a whole
company and not just one employee slot!
I don't know but can ask: Are there some
people at Berkeley smoking funny stuff?
Given the success of well run hacker schools like Dev Bootcamp and Flatiron Schools, I am surprised no one has yet tried to apply that model to training data scientists. It seems like a sector similarly deprived of properly trained talent and also with a similar initial learning curve to develop the basic skill set.
From what I understand from skimming the site, Dev Bootcamp teaches people RoR and a few other web technologies in 9 weeks. All that tells me is that you know one programming language, it doesn't tell me whether you are a programmer, whether you understand concurrency problems, race conditions, algorithms etc. Sure, this is alright for a lot of programming positions because there are a lot of positions out there that don't need those.
The problem with data science is that it is incredibly hard to teach anyone Linear Algebra, Probability, statistics in 9 weeks. Sure, I can hand wave all that and then teach you a bunch of machine learning algorithms. All you get at the end of it is people who claim they understand it intuitively and don't need the math. Except that mathematical intuition builds up accumulatively.
It is easy to see this in interviews; you can see folks who are really good at drawing pretty pictures to explain say PCA. They have no clue when not to use such a thing. It makes no intuitive sense to them why PCA breaks down when there are outliers. If they can't draw a picture of it, it is difficult for them to comprehend.
thanks for the detailed response. I know very little about the actual application of data science, so it nice to hear the thoughts on the feasibility of this from someone who does. Either way, someone seems to think they can teach it in a bootcamp format, so I am curious to see how they manage.
There's also http://insightdatascience.com/ . This is a little bit different; it's a program aimed at training postdocs in various fields to be data scientists.
$60K seems like an awful lot for an on-line program. I think they are getting a lot of internal flack over the pricing. It seems to me that Berkeley should be jumping into on-line learning, as their state funding dries up. The best way to do this consistent with their mission is a mass market approach. By putting in Tiffany pricing, they're going to fail both their mission (training data science, educating the public, etc) and they won't bring in much money to fill any revenue holes.
Harvard's online masters degrees are closer to $20K all in.
This should be at the top of this thread. Save the $60k for a MS or PhD in machine learning, applied math, or information systems at a graduate program. Don't do this.
