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Let's all learn stats!
Udacity are running an Introduction to Statistics course starting on the 25th June 2012.
Many of us could stand to learn some more stats, I certainly could. This seems like a great opportunity!
It is mandatory for all LWers to enroll in this course.
Update: the last line was a joke. Obviously people are not finding it funny. Sorry.
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Comments (37)
To make more people use Udacity, it could perhaps help if you described how it works. All I know is that users must be registered. What happens then?
How important is the time? It is necessary to see the lessons when they are published, or can they be seen later too? (If yes, can I see some older lessons now?) What is the advantage of seeing this course from 25.6.2012 as opposed to just wait and see it when it is complete? Etc.
At least for me, not having a clue about this is an inconvenience, which significantly lessens the probability that I will try it.
I completed CS373, "Programming A Robotic Car", at Udacity in April, and can answer these questions.
There is generally one lecture and one homework exercise posted each week. The deadline for the homework is the following week. You can resubmit homework any time before the deadline, and see how you did any time after the deadline. There are also "quizzes" within the lectures: unassessed exercises for which you get the answer as soon as you want to look at it. There is a final exam at the end (isomorphic to "homework", just called by a different name). You get a digital certificate at the end if you do well enough on the homework and exam. Behold!
Once material is posted, you can see it until the end of the course (maybe afterwards indefinitely, I can still see them when logged in to Udacity). In fact, I can see all the materials for courses I haven't done as well, including answers to exercises and exams, which is surprising. But maybe I'm looking at the exercises and answers from previous runs of the courses and they'll be different the next time round.
For courses being run for the first time (such as the suggested Statistics course), all I can see is the preview video -- it's quite likely that all the lecture and homework materials haven't been completed yet.
So I suggest you register at Udacity and look around to see what you can see.
It's up to you to decide how you want to work with this. If you're not interested in the certificate, you could just register at Udacity, wait until the course is completed, then work through all the material at whatever pace you want. Something you will lose by doing that is the interaction with other people doing it in real time. You can read the Udacity forums after the fact but you wouldn't be participating in them.
Personally, I preferred to work through the course in real time, meeting all the assessment deadlines, but YMMV.
Thank you! This helped me a lot. Now I feel motivated to do the course in real time.
EDIT: Do you think it is possible to miss two homeworks and still get the certificate? (This course collides with the July Minicamp.)
I did the pre-Coursera ML class and pre-Udacity AI class at the same time (both professors went on to for the respective companies after the experience), got two certificates with a reasonable grade, being a father and having a dayjob. I can attest to having fairly average intelligence by LW standards. (So yes, I think it's quite doable)
Their FAQ is inconsistent on this. Q 14 says that your final grade is determined only by your exam grade, but Q 2 says that homework counts too. It also says there is no deadline for homework (except, presumably, the end of the course). There were homework deadlines when I did it, but Udacity is evolving.
As I understand it, what counts for the final grade is allowed to vary by class.
I'll be providing support in ##patrickclass on freenode.
Having taken some Udacity courses I do not expect it to be hard for average LW-er, and probably useful (I never took a decent stats course in college). There is no penalty for dropping out if it's too easy.
IIRC the course will have complementary python programming exercises, which is a good complement to learning pure math.
Fellow commentors, we should attempt charitable interpretation.
It does seem likely that learning about statistics will make a lot of common mistakes (i.e. accounting for sampling bias, continuous significance testing being wrong, intuitive assessments of four-sigma-plus events being way off almost all the time) actually part of our brains. I base this on Udacity's AI class making things like Bayes rule and A-star way more a part of my brain.
First video is up. Looks a bit too easy so far, but that's on purpose. Later units look more interesting, based on the summaries.
German humour :-/
I have enrolled in the course.
Me too.
Since I can't stay away from LW, I may as well reveal that I am doing this class as well. The units aren't difficult and just today a new one is out that deals with Bayes Rule among other things.
I high recommend the class for the mathphobic since the first unit will at least help you a great deal with understanding graphs.
If anyone needs any help feel free to ask. I hope many others are taking it!
I'm in too.
I have as well.
Oh yeah? Are you precommitting in any way to actually finishing this course you're urging on us? Maybe you should do it and report back how it went.
I found it funny :)
I'll be taking Stats I & II this spring, but it would be awesome to learn a bit beforehand. Definitely signing up. Thanks for the info!
Small correction: the "Udacity" hyperlink leads to
<http://lesswrong.com/lw/h8/tsuyoku_naritai_i_want_to_become_stronger/>.
Thanks, I've corrected.
I'll give it a go.
I finished an entry-level Statistics course about half a year ago and I've forgotten nearly all of it, hopefully this time will be better.
Hopefully the videos are good.
I recommend Old Papers. (In old papers people know what they are talking about, and how it relates to everything else from their time, and how it relates to what their innovation is, and they explain it all. Getting ideas from the people who made the ideas has always been good for me.)
I mention one favorite old paper here.
It's curious that in all three volumes of that work (searched on Amazon), there's not a single paper by E.T. Jaynes, and just a single mention of him anywhere.
Presumably implying that people don't do that now? Is that a general trend, and in other fields than statistics? To take a nonrandom example, Judea Pearl seems to measure up well by those standards.
Wow! Excellent point, and excellent question! Updating. Thanks.
I would participate if I hadn't so many other things to do, especially considering I just finished an introductory course in statistics and probability theory.
Why not, I need to brush up my knowledge anyway, I'm in!
Edit: I guess not. I may not be able to participate considering my pledge.
I think you are assuming that "all LWers" do not have an introductary knowledge to statistics.
I wouldn't mind some more advanced statistics courses, but I am perfectly capable of performing all of the tests I expect to encounter in this course. My efforts to become stronger are better spent elsewhere.
See here.