Prerequisites for the course?
Hi,
I'm pleased to see this course offered on edX again. But unfortunately I'm very much in doubt as to whether my current background knowledge will suffice to benefit from this course. I already wanted to take Andre NG's course on coursera in June but immediately backed down when I saw that previous knowledge of hypothesis testing and the least squared method was required. Though I have to admit that I didn't even try to fill in these maybe rather small knowledge gaps due to my limited time then.
My current background knowledge is:
 a reasonable good command of high school level calculus (just refreshing it)
 familiarity with basic arithmetic matrix operations (not more than high school level)
 very basic Python programming experience (currently taking the introduction course on udacity)
 only VERY basic familiarity with concepts of probability and statistics
The least mentioned is definitely my weakest point. I'm only superficially familiar with the very basic concepts like permutation/combination/mathematical expectation/mean/median/mode/standard deviation/Bayes' Theorem/standard deviation and variance/gaussian distribution and I would have to refresh even these basic concepts.
I have no experience with linear regression or the method of least squares. Though I do understand both concepts (without knowing the exact formulae) I've never really learned or used them.
I have no experience at all of hypothesis testing.
Given this "modest" background knowledge, will a few weeks (let's say 1015 hours/week) of preparation to fill the gaps suffice to be really adequately prepared for this course?
In case yes, my most important question is: on which subjects should i concentrate in my preparation?
(In case no, I would nontheless try to use the archived version of the course at a later point after thouroughly filling in the gaps. So in any case I would be interested in the question on which subjects I should concentrate to prepare for this course.)
