Statistical Inference

Statistical Inference is the sixth course in the Data Science specialization, and the first course in the analytical portion of the course (followed by Regression Models and Practical Machine Learning. The course covers probability, variance, distributions (normal, binomial, poisson), hypothesis testing and p-values, power, multiple comparisons, and finally resampling. Overall this is a rather poor introduction to statistical methods, and the only really relevant hypothesis test covered is the simple t-test.

This is the first course taught by Brian Caffo, who is more mathematically-inclined, and he doesn't do a particularly good job of explaining the material in an intuitive way. There are a few good portions of the course, though, and I though the explanation of statistical power using the manipulate package in R was particularly good, and quite a bit better than the coverage I've received in face-to-face university courses I've taken. Otherwise, though, this course in no way will prepare students to actually conduct most common statistical tests, and it doesn't cover non-parametric statistics in any depth whatsoever. I have heard good things about the former Duke statistics course on Coursera, and that course has just (at the time of this writing) been released as a new specialization (Statistics with R), so that might be a better choice for learners looking for a better coverage of statistics using R packages.

Overall, three stars. There are a few gems hidden among the rest of the course content, but overall the course is not particularly good for learning statistical techniques, and it is unlikely that you'll come away from this with any real understanding of how to apply statistical hypothesis testing unless you have pre-existing experience in this area.

First of all the course is not easy, especially for a person who have little or no experience in statistics and math-like me-. Anyone starting this course should be aware of this. Some concepts really needs extra time to study after lessons, probably it would be better to start from a really basic book like "statistics for dummies". That was what i did actually. I needed to study for a couple of months to have a good understand of this lesson. But at the end i started to get familiar with statistical language and satisfied with the course. There are many real life examples, swirl classes and books provided. Ofcourse it could have been better with a better and detailed design of content but yet i am satisfied with it, and i can recommend it to anyone who wants to deal with statistics.

I was enrolled in the data science specialization with John Hopkins University in Coursera, and this was the 6th class in the program, out of 10.
This class is the one that made me drop out of the program entirely.
I was able to follow easily the Data Scientist's toolbox, R programming, Getting and Cleaning Data, Exploratory Analysis and Reproducible Research, as the classes were laid out in a good manner, and the assignments and projects reflected what was being taught.
Mind you, I have a programming background already but didn't know R prior to this program.

Then come statistical inference, and this is where hell starts. The subject itself is very interesting, but there's absolutely NO way you can go through the class without looking at other course material elsewhere. The teacher (a new teacher starting this class) just shoots up complex mathematical formulas without really explaining where they come from, or what/how to memorize it, then moves on to more complex ones without giving you a breath or more thorough explanation. I don't know how many times I went on Khan Academy to fully understand everything.

R had very little in the class, as it was more of a mathematical course. But then the project comes and boom, time for you to apply all those confusing probability concepts with R, which you've never had the chance to do before until now. (that's when I dropped the class, at week 4)

I've read others'review and couldn't agree more, and people saying that the Regression Models class is as bad, well no thank you. I'll be taking the Duke's program from now on. However, I really liked the first 5 classes of the JH data science specialization.

The course covers quite a lot of material, very quickly. Unfortunately, the material, while nominally for beginners, requires a decently strong statistics background. Even with a good foundation of statistics it was difficult to follow when examples are presented quickly and referenced back to material covered in prior lectures or even weeks as if they ought to be totally fresh in the student's mind. I found it most frustrating that when I was struggling to grasp a concept, the instructor would say something like, "It is obvious that..." or "everybody knows..."

There is a lot of material, and if you have time to go and learn everything that is covered you should be good with this, but don't expect to learn the subject just by watching the lectures and doing the swirl exercises.