Opiniones y comentarios de aprendices correspondientes a Inferential Statistics por parte de Universidad Duke

Solid 3 stars. Lots of material is covered quickly and I learned a lot. The lectures are informative and supplement the book (I definitely recommend reading the (free) book). On the negative side, I noticed that the online discussion forum for the course isn't monitored by the instructors and the mentors seem to respond to only some questions. I noticed that almost all of the questions posted by students in the past year that went unanswered. I mean no one even bothered to respond to them at all. That's shameful, esp. if those students who submitted questions are paying for a certificate.

The course is very useful and helps me understand the formal testing process of data analysis. I just hope it would cover more of non-parametric testing techniques and dive into a bit more into effect size testing. Anyway, It also provides a lot of insights into important statistical measures of information, which could potentially be extended to the field of predictive modeling and machine learning.

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This course is an excellent overview of inferential statistic tests / hypothesis tests and confidence intervals. The organization and material is quite good, with exercises and applications using R.

A very good introduction to the fundamentals of inference and NHST. It's very important that you do all excercises and readings or you will not learn as much. Also, the course won't provide a lot of information on how to use R, but if you spend a good amount of time on your project and make sure that it's good you will learn enough. I had to review a lot of R projects that were not very good, which suggests that some students aren't learning what they should.

If you want to learn statistics or have limited knowledge on the topic, and also want to learn a bit about how to use R, take the course. If you already know statistics and you only want to learn R, then this might not be the course for you, as the emphasis is on statistics per se.

Well-taught, but they need to provide more resources to help people learn R. R is not a user-friendly app and I needed to google how to do a lot of the things they're asking us to do. Needless to say, I can google how to work in R on my own without paying Coursera a fee.

While I understand and appreciate that the scope of the class is more focused on the application / ideas of the statistical methods without delving too much into the mathematics, I would appreciate if some of it was used to explain why some equations work the way that they do. For example, in talking about F test statistics, it was difficult to understand the reasoning behind F = variance between groups / variance within groups until I had to look up other explanations elsewhere. While I believe that the instructor teaches well in most parts, I often find it difficult to follow along because she goes through a lot of assumptions and I'm unclear as how / why she is allowed to go on her assumptions.

The course did teach statistics but there were some problems with R commands, assignment expectations and grading outcomes. For one, this course really needs to do a better job of emphasizing that the student is expected to use R commands provided by Course 1 (I think it's called "Exploring Data") as well as this one. It would be helpful if students had easy access to all of the labs in that course as well as this one. Secondly, the list of expectations given out for the final project omitted several requirements (apparently we are expected to use R commands learned) though this is a small problem.

The biggest problem is the peer-grading. Reviewing other peers was actually very helpful in that it does teach you about your mistakes. For example, I realized after grading others that I had made two major errors and was ready to redo my project if needed or, if I passed, never make those types of errors again. This feeling that you are learning and that this is a quality course is taken away when your grade doesn't match your work. In my case, I got a perfect score which is nonsense. Like I said I made two big mistakes one of which was not including a confidence interval when I should have. What if I made others that I didn't see?

Feedback is important. It is a big part of learning. So is the ability to actually use the skills being taught (the R commands were taught much less clearly than say in Datacamp which this course suggests we use even though it is NOT free and I signed up for the Duke course specifically because it taught statistics using R). It's a good course overall and you will learn statistics; but when you're charging people a fairly high monthly fee, you should deliver on your promises to give feedback and to effectively teach one of the major course goals.

It was good. But I feel like I've spent half of the time untangling sly phrasing of questions.

I should not be charged if I have completed the project and simply waiting for other users to review it. This is dependent on how many users are taking the course at any given time. A bad system that results in users paying more for uncontrollable uncertain factors...

An overview of inference, light on the math, light on the theory, and with an unfortunate failure to reinforce what may be the most important part of practice: what should be done when conditions for a particular method are not met. When you teach students how to evaluate the conditions required for certain methods, but then walk through those methods even when the conditions aren't met, you reinforce poor practice. If you want to use an example where the conditions aren't met, STOP once you find out the conditions aren't met. STOP and REINFORCE the fact that you cannot use a method without meeting conditions. It is not a valuable exercise to walk through the plug and chug calculations anyway. STOP, discuss why you can't proceed, and then move on to another example if you want to give your students an opportunity to practice taking the method through to its conclusion.

This cours helps me a lot to understand the mechanism under the numbers and statistic, I really recommend u to follow it if you wanna to be a data scientist!

Yet another superb course in this specialization.

Be ready to spend lots of time and learn lots of things.

It was a great course, one of the best things was the R programming for statistical test and inference.

Excelente.

nice course. it will definitely gives you skill.

I found the treasure.

The concepts are explained in a very simple and effective manner with the help of a case study. Background knowledge of R will be very handy if one wants to cover the topics at a faster rate.

This is a great course. One can learn so many things from this course.

This course is super and explained so well by the professor. I would recommend this course to anyone who has interest in data science

Expertly designed course, Useful.

Amazing!

very informative

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Excellent! Clear and very interesting.