16 ms·

Berkeley offers its data science course online for free

801 points by seycombi 9y ago

seycombi 9y ago
direct link: https://www.edx.org/professional-certificate/berkeleyx-foundations-of-data-science#courses https://www.edx.org/professional-certificate/berkeleyx-found... (There are two ways you can follow the course: Certificate Program is paid, but the AUDIT program is free)
- based2 9y ago
  Pursue the Program ( $357.30 USD - old: $397 )
  - deleted 9y ago
    [deleted]
- qntty 9y ago
  Note that if you don't want to pay, you should click "View Courses" and click on 1 of the 3 courses in the Foundations of Data Science series.
tenkabuto 9y ago
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.
jamestimmins 9y ago
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).
- ithilglin909 9y ago
  I'm just curious -- where do you think the puck will be? I've had a number of younger acquaintances ask for career advice. Pursuing some kind of data science seems like an obviously smart direction now, but I've wondered if this, as well as traditional CS career paths, may be in danger of becoming over-saturated areas, now that everyone views them as sure paths to a job that pays well.
  - oculusthrift 9y ago
    i think there is a natural barrier for these paths being too hard and too mathy for most people. people have known for decades that engineering is a good path.
    - resolaibohp 9y ago
      Yep, a lot of the people who want to get into it run away when they find out you need to know how to code and understand the math. For some reason there is this idea that it is easy money.
  - jamestimmins 9y ago
    I simply mean in terms of the availability of online courses. Eventually there will be harder CS courses online. While there's been enormous movement in this direction in even the last 2 years, there's still a lot of room for growth and improvement.
  - itronitron 9y ago
    By its nature data science work is very undefined. There is not yet a widely established design path for data science as there is for software development. The risk for someone starting their career in data science is that they will end up in an organization that doesn't know how to data science. So I'd recommend steering young folk to larger teams that have PhD level Statisticians.
  - proverbialbunny 9y ago
    On the tech side front end or data science both seem like good options. 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.
erokar 9y ago
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.
jph 9y ago
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 9y ago
  US$65K for tuition
  - amrx101 9y ago
    LOL. As someone from third world where local currency is enormously devalued with respect to US$, I wonder why would anyone do this?
    - heurist 9y ago
      These prices seem mostly targeted employers who offer tuition reimbursement.
    - pc86 8y ago
      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 8y ago
        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
- 9y ago
csjr 9y ago
Does anyone know how it compares to bootcamps like DataCamp[0] for e.g? [0] https://www.datacamp.com https://www.datacamp.com
- analognoise 9y ago
  Boot camps are stupid. Go to community college. It's ridiculously cheap, and the credits are worth something.
  - barry-cotter 9y ago
    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 9y ago
      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 9y ago
        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?
Treegarden 9y ago
why is there no syllabus - as in a list of contents? I want to know what really is behind this buzzword stuff.
- QML 9y ago
  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.
graycat 9y ago
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!
bartart 9y ago
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
- bhl 9y ago
  The instructors have explicitly said, if you have previous CS or Statistics knowledge, the class isn’t for you. This is for people who don’t know how to program and haven’t taken a statistics course yet.
  - throwawayjava 9y ago
    None-the-less, IMO parent's observation is important for purchasers of MOOCs. It's true for a lot of the very popular MOOCs.
  - StashOfCode 8y ago
    I code myself. I've watched it. Pretty good learning platform (never had a look at a MOOC before): I am impressed (but I will take a look at other MOOCs mentioned in this discussion and I will certainly be less impressed very soon). Finished the first week. Right now, I find it a good introduction for someone who has no knowledge of code and statistics. IMHO, the main advantage would be that such a person may learn what coding and statistical reasoning looks like. The main disadvantage would be that this person think he has learned enough and keeps not trying to learn more about those topics. What an enlightening free week for the people: first look at "Do you trust this computer?", then attend this MOOC "Data8.1x"?
- Someone1234 9y ago
  I randomly started checking other CompSci courses on that site and they all had a similarly high average grade, in some cases even higher (A instead of a B+). Which are the hard courses at Berkeley in CompSci using the site you linked?
  - 9y ago
carlosgg 9y ago
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-sparktm https://www.edx.org/xseries/data-science-engineering-apacher...
- swedishfish 9y ago
  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.
meri_dian 9y ago
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?
- itronitron 9y ago
  The CS program should focus more on data structures and algorithms (and possibly UX and good ol' software dev as well) and the DS program should focus more on statistical/analytical methods and their particular nuances and limitations. If the DS program is done well, with a lot of stats classes, then it is not a weaker program.
  - chibg10 8y ago
    Honestly, with the current state of stats teaching, they might be better off just avoiding the stats classes in most cases. I don't need yet another drone that tries to convince me I should make a business decision because of "statistical significance" but can't explain why I should care about statistical significance (p.s. I shouldn't).
- gaius 9y ago
  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 9y ago
    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.
gnulinux 9y ago
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.
- deleted 9y ago
  [deleted]
- cbHXBY1D 8y ago
  Or if you want to get experience with data science: Stats 134 (easy), CS188 (medium), CS189 (hard).
  - jwilbs 8y ago
    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 8y ago
      CS 189 (with Sahai) (Machine Learning) was by far the hardest course I took in my life.
  - gnulinux 8y ago
    Meh, I disagree. CS 188 is not a very useful course. You can learn 188 material very quickly (i.e. reading Wikipedia for a couple hours) if you do well in CS170 and CS 189. I think for data science: Stats 134, CS 189, EE 127, EE 126 are most useful (in this order). Of course, in order to do well in CS 189 you need to have a good background in probability which can either be CS 70/Math 55 (if very well understood), or Stats 134, or EE 126.
benhamner 9y ago
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
anonymous5133 9y ago
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 9y ago
  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
dpflan 8y ago
Have anyone followed the curriculum suggested here? > http://datasciencemasters.org/ http://datasciencemasters.org/
master_yoda_1 8y ago
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 8y ago
  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 8y ago
    I was talking about people who study in depth on their own after the course.
- bagacrap 8y ago
  What a bleak world you must live in if all TV counts as a waste of time. Does that extend to other forms of drama? Literature? Entertainment? Even if you believe it's pointless it's pretty clear it's no something everyone else "knows".
simpleAdam 8y ago
this would seem to be a playlist https://www.youtube.com/watch?v=xcgrnZay9Yc&list=PLFeJ2hV8Fyt7mjvwrDQ2QNYEYdtKSNA0y https://www.youtube.com/watch?v=xcgrnZay9Yc&list=PLFeJ2hV8Fy...
daveheq 8y ago
Who has time for this?
- Double_a_92 8y ago
  Unemployed desperate people thinking that this will get them a job.
frabbit 8y ago
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 8y ago
  Neither of which are the least bit consequential for anyone more interested in learning about data science.
  - frabbit 8y ago
    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.

801 points by seycombi 9y ago

direct link: https://www.edx.org/professional-certificate/berkeleyx-foundations-of-data-science#courses https://www.edx.org/professional-certificate/berkeleyx-found... (There are two ways you can follow the course: Certificate Program is paid, but the AUDIT program is free)

based2 9y ago
Pursue the Program ( $357.30 USD - old: $397 )
- deleted 9y ago
  [deleted]
qntty 9y ago
Note that if you don't want to pay, you should click "View Courses" and click on 1 of the 3 courses in the Foundations of Data Science series.

tenkabuto 9y ago

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.

jamestimmins 9y ago

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).

ithilglin909 9y ago
I'm just curious -- where do you think the puck will be? I've had a number of younger acquaintances ask for career advice. Pursuing some kind of data science seems like an obviously smart direction now, but I've wondered if this, as well as traditional CS career paths, may be in danger of becoming over-saturated areas, now that everyone views them as sure paths to a job that pays well.
- oculusthrift 9y ago
  i think there is a natural barrier for these paths being too hard and too mathy for most people. people have known for decades that engineering is a good path.
  - resolaibohp 9y ago
    Yep, a lot of the people who want to get into it run away when they find out you need to know how to code and understand the math. For some reason there is this idea that it is easy money.
- jamestimmins 9y ago
  I simply mean in terms of the availability of online courses. Eventually there will be harder CS courses online. While there's been enormous movement in this direction in even the last 2 years, there's still a lot of room for growth and improvement.
- itronitron 9y ago
  By its nature data science work is very undefined. There is not yet a widely established design path for data science as there is for software development. The risk for someone starting their career in data science is that they will end up in an organization that doesn't know how to data science. So I'd recommend steering young folk to larger teams that have PhD level Statisticians.
- proverbialbunny 9y ago
  On the tech side front end or data science both seem like good options. 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.

erokar 9y ago

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.

jph 9y ago

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 9y ago
US$65K for tuition
- amrx101 9y ago
  LOL. As someone from third world where local currency is enormously devalued with respect to US$, I wonder why would anyone do this?
  - heurist 9y ago
    These prices seem mostly targeted employers who offer tuition reimbursement.
  - pc86 8y ago
    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 8y ago
      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
9y ago

csjr 9y ago

Does anyone know how it compares to bootcamps like DataCamp[0] for e.g? [0] https://www.datacamp.com https://www.datacamp.com

analognoise 9y ago
Boot camps are stupid. Go to community college. It's ridiculously cheap, and the credits are worth something.
- barry-cotter 9y ago
  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 9y ago
    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 9y ago
      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?

Treegarden 9y ago

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

QML 9y ago
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.

graycat 9y ago

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!

bartart 9y ago

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

bhl 9y ago
The instructors have explicitly said, if you have previous CS or Statistics knowledge, the class isn’t for you. This is for people who don’t know how to program and haven’t taken a statistics course yet.
- throwawayjava 9y ago
  None-the-less, IMO parent's observation is important for purchasers of MOOCs. It's true for a lot of the very popular MOOCs.
- StashOfCode 8y ago
  I code myself. I've watched it. Pretty good learning platform (never had a look at a MOOC before): I am impressed (but I will take a look at other MOOCs mentioned in this discussion and I will certainly be less impressed very soon). Finished the first week. Right now, I find it a good introduction for someone who has no knowledge of code and statistics. IMHO, the main advantage would be that such a person may learn what coding and statistical reasoning looks like. The main disadvantage would be that this person think he has learned enough and keeps not trying to learn more about those topics. What an enlightening free week for the people: first look at "Do you trust this computer?", then attend this MOOC "Data8.1x"?
Someone1234 9y ago
I randomly started checking other CompSci courses on that site and they all had a similarly high average grade, in some cases even higher (A instead of a B+). Which are the hard courses at Berkeley in CompSci using the site you linked?
- 9y ago

carlosgg 9y ago

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-sparktm https://www.edx.org/xseries/data-science-engineering-apacher...

swedishfish 9y ago
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.

meri_dian 9y ago

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?

itronitron 9y ago
The CS program should focus more on data structures and algorithms (and possibly UX and good ol' software dev as well) and the DS program should focus more on statistical/analytical methods and their particular nuances and limitations. If the DS program is done well, with a lot of stats classes, then it is not a weaker program.
- chibg10 8y ago
  Honestly, with the current state of stats teaching, they might be better off just avoiding the stats classes in most cases. I don't need yet another drone that tries to convince me I should make a business decision because of "statistical significance" but can't explain why I should care about statistical significance (p.s. I shouldn't).
gaius 9y ago
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 9y ago
  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.

gnulinux 9y ago

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.

deleted 9y ago
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cbHXBY1D 8y ago
Or if you want to get experience with data science: Stats 134 (easy), CS188 (medium), CS189 (hard).
- jwilbs 8y ago
  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 8y ago
    CS 189 (with Sahai) (Machine Learning) was by far the hardest course I took in my life.
- gnulinux 8y ago
  Meh, I disagree. CS 188 is not a very useful course. You can learn 188 material very quickly (i.e. reading Wikipedia for a couple hours) if you do well in CS170 and CS 189. I think for data science: Stats 134, CS 189, EE 127, EE 126 are most useful (in this order). Of course, in order to do well in CS 189 you need to have a good background in probability which can either be CS 70/Math 55 (if very well understood), or Stats 134, or EE 126.

benhamner 9y ago

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

anonymous5133 9y ago

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 9y ago
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

dpflan 8y ago

Have anyone followed the curriculum suggested here? > http://datasciencemasters.org/ http://datasciencemasters.org/

master_yoda_1 8y ago

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 8y ago
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 8y ago
  I was talking about people who study in depth on their own after the course.
bagacrap 8y ago
What a bleak world you must live in if all TV counts as a waste of time. Does that extend to other forms of drama? Literature? Entertainment? Even if you believe it's pointless it's pretty clear it's no something everyone else "knows".

simpleAdam 8y ago

this would seem to be a playlist https://www.youtube.com/watch?v=xcgrnZay9Yc&list=PLFeJ2hV8Fyt7mjvwrDQ2QNYEYdtKSNA0y https://www.youtube.com/watch?v=xcgrnZay9Yc&list=PLFeJ2hV8Fy...

daveheq 8y ago

Who has time for this?

Double_a_92 8y ago
Unemployed desperate people thinking that this will get them a job.

frabbit 8y ago

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 8y ago
Neither of which are the least bit consequential for anyone more interested in learning about data science.
- frabbit 8y ago
  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.