Thanks

useful

Thanks

goode

great

Nice

nice

good

Good

Good

Good

good

Wow!

good

ok

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There is no doubt that Brian is extremely sharp and knowledgeable about statistical inference subjects. However, I tended to agree the following forum comments from another fellow student.

“…This is unfortunately the worst lecture that I have come across in the Data Science stream so far. The presenter zips through it at a lightening pace. The pace, content, presentation, examples - NOTHING - is suitable for the intended audience (i.e. people taking up the data science stream). The lecture appears to have been recorded for some other audience - maybe people taking up a university course in advanced sats - and just plugged-in into this coursera stream. I wish the course publishers (Johns Hopkins) had put in a little bit of thought and effort into this module and tailored it for this specialization stream rather than lazily lifting and dropping a pre-recorded lecture from elsewhere. It should have been slower-paced - maybe split into 2 or more slower-paced lectures that are gentler on the Data Science stream new bees...”

This course gave me a pretty good basis for a lot of subtopics in the field.

However, it was very frustrating at times. Brian, the instructor, clearly operates at a high level and in his explanations he makes frequent conceptual leaps that are difficult to follow. I spent a lot of time on Wikipedia and Googling the details of the various distributions.

Also, the exercises in the associated mini-textbook have a similar problem: there is not enough practice of the basic concepts to give a beginner confidence (no pun intended). Many of the exercises suddenly introduce assumptions that a beginner would never have made. So, expect to get everything wrong the first time, and then look at the answer and figure out the two or three new aspects of statistics that you needed to know.

I have done PhD level statistics courses before, but found that they either went too deep into theoretical mathematics as to completely loose the audience (at least me), or to not even try explaining what is going on under the hood of R or SPSS. What I really like about this cource is it pushed me to do calculations by hand, which really helped me understand the concepts. Dr Caffo is clearly a skilled statistician and the course is at its best when he goes off script (at least off slides) to explain and illustrate concepts. Minus one star because unfortunately the presentation of the material is uneven and some times distracting, e.g. talking very fast.

This was the toughest of all the Data Science courses so far. Without the statistical inference book, the practical exercises and the swirls it would have been very challenging to pass the course. These were very valuable tools. The videos that are available when I couldn't get a practical exercise right, also helped me a lot.

I majored in mathematical statistics 40 years ago, but have never used it in my whole career. But in my opinion this course explained the concepts much better than how it was done 40 years ago. Congratulations to everybody involved.

I have learned so much and was really proud of myself when I passed this one.

This course will give you a good basic understanding of statistical inference. However, if you are a complete beginner I highly recommend first doing a basic statistics course (such as the one presented by the University of Amsterdam on coursera). Also, do use the recommended textbook and take your time to understand concepts. This course will also work better in context of other courses in the Data Science specialisation (for example R Programming and Exploratory Data Analysis). This course took me 4 months to complete with regular effort (not 4 weeks as suggested!), so factor in extra time for completion.

I think overall Professor Caffo does a fairly good job of explaining the material, though he covers a lot of topics quickly within the course. So I found myself having to pause and rewind often when taking notes. The course project was definitely the most challenging aspect of the course for me, since my background is in SAS and am an R novice. I spend a full weekend putting the report together. Since so many topics were covered in such a quick fashion, I don't think I retained it as well as I should. Luckily my undergrad statistics background helped a bit, even though it was over 20 years ago.

I get that the subject matter is hard and so this isn't going to be easy to absorb regardless of how it is taught. My biggest challenge was too many overloaded sentences where to understand the new area that was being focused on there was an assumption that i already had firm grasp on the set of other subject areas referenced in the same sentence. A lot of confusion as well arising from terms that sound the same except for one word or context of their use which maybe could be helped along by a summary slide of terms and meaning at the end of each lecture or section.

Nice course with an appropriate level covered for the data science specialisation (assuming people taking these courses very have different prior knowledge of statistics). It would however be good to add a second statistics course to the stream with some more advanced topics. Yet, it is still one of the harder courses of the specialisation.

The only big criticism I have is that the course feels a lot less polished than other parts of the specialisation. It feels like cut and pasted parts of other courses added into one course than its own entity.

I am interested in taking the Regression Models course and took this one as a refreshment for the statistics knowledge I already had. I found the course well done and the resources easy to use and throughout.

As a negative point I would mention that as the topics get more involved, the time dedicated to each one seems to decrease, to the point where both MULTIPLE TESTING and GROUP COMPARISONS are pretty much a briefing, which kind of defeates the purpose of including them on the course in the first place...

I give the course a 8.5 out of 10.