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.
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.
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"?
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.
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.
Depending on what stats you want to do, there are some pretty decent MOOCs. No one is going to claim that Daphne Koller's PGM course is weak in anyway for example[1].
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?
The hardest CS undergraduate course offered is probably 189: Introduction to Machine Learning. Nonetheless that class has a B+ average; so I wouldn’t say difficulty of class correlates with a low grade average. There are others like 170 and 162 which you can check out.
If my memory holds, there’s a policy that class averages should be around 3.0-3.3 (B/B+).