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.

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.

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.

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 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.

No tutor answering questions in the discussion platform.

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.

I think this course needs more instruction with the R studio lab

At times it feels lazy how it is put together. The examples are confusing (rather than clarifying) and there is close to no teaching of R, but the assignments are meant to be done on it. In fact in the forums it is endorsed by mentors to learn R somewhere else. Likewise, I saw one comment where the student mentioned how they got confused by a core concept (p-value) and could finally wrap their head around it by watching a Khan Academy video. And sadly, this was also endorsed by a mentor. Overall, I found the effort put into this course insufficient for people who are new to Statistics or R. Therefore, the name of the entire specialization becomes misleading as it suggests that we were going to be taught how to use R in statistics. I had high hopes for this course but sadly I will abandon it and spend my money on an alternative course/specialization.

An excellent and rigorous that covers theoretical and simulation approaches to inference. The teaching is first-rate! The textbook and lecture examples are superb. The final project gives you practice in finding a research question that interests you, translating that question into hypotheses, and then challenges you to find the right method to test the hypothesis. The only improvement I could suggest is more examples and exercises on simulation approaches. I spent a great deal of time on my own learning about that topic.

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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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.

some topics require more explanation!

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

So much disjointed information.... I felt absolutely crushed trying to learn and understand all this. Am waiting for another 8 hours before I can reattempt the quiz.

Personally, I feel that this course assumes the student is automatically an expert in statistics (simply due to completing the first intro to statistics course). The logical progression of how to approach different problems - and the terminology of the statements involved has been thrown out the window...

If you're new to statistics, I suggest you should at least double the time allocation they provided...

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...

The teacher lacks the ability of mathematical description, including clearing defining concepts, describing everything in mathematical languages, and showing math formulas of t-tests. She hopes to hide everything behind the canvas and just show how statistics are applied. But without enough mathematics nothing she said makes sense. I have to search on the internet to get to know what she didn't teach.

------from a math phd.

Professor Mine is terrific. I'm sure she has a great depth of knowledge and grateful that she's able to deliver her knowledge out to listeners. She uses meaningful examples all along the course, no dry pure mathematical cases at all. That helps a ton to digest concepts. And she constantly repeat some core concepts and how to interpret a statistic right. I didn't realize how important this was until I was challenged with questions, then I came back and hear again her interpretation, and the whole thing became clearer. She's one of the best professors I've ever listened to, and I've been through grad school, met so many professors.

The current mentor Rolf was great at supporting. He answers a lot of questions in the forum. He's very responsible and supportive. So if you're considering on taking this course, take it now as mentor will change!

I haven't finished the course yet, but the enrollment rate seems to be quite decent, so I wouldn't expect it to take too long to get final project reviewed and get certificate. I assume this is an important issue for any course takers.

The only downside is that there could be more R code teaching, especially on complicated simulations. That way it may be more friendly to R beginners. I know it's important to do research ourselves for codes, but beginners could lack of proper terminology or vision by nature to do the research on Google. Especially when I'm physically in the Main Land of China, where it takes some efforts to even get on Google, so doing code research took a lot of my time and was a little frustrating towards the end.

But again, the overall course and support are great! If this is not a 5 start course, I can hardly give out my highest mark to any other courses. It helped me to understand inferencial statistics, practice R, and think more like a statistician.

Teaching: I really like the clear and concise teaching style of lecturer and the wide range of simple real-life example used to explain the course content.

I’m a social science student. Although I’ve studied quantitative research methods before, this course gives me some new insights into inferential statistics. I think I will never forget the statistical meaning of p-value after this course!

Course Structure: The course structure is well organized with clear focus in each week.

The first and second weeks are easy to follow, but the third and fourth weeks are more challenging.

Textbook: The textbook used in this course is a good supplementary material, although it is not necessary to read the textbook. Course videos have already explained everything that we need to know at intro level. However, it is worth reading the textbook for the third and fourth weeks.

Assessment: The assessment of quiz in each week is relatively easy. The exploratory data analysis required in peer-reviewed assignment is slightly challenging, because it might be hard for beginners to touch every required point.

Terrific course, i got here after starting the Data Science specialization on John Hopkins uni on Coursera, but there bit on statistics is awful, a waste of time.

I decided to give Courser another shot and definitely not regretting it, this course really go over the basics clearly and make sure to make enough exercise to revisit that clearly explain the fundamentals.

I was happy as getting to the final assignment i found myself doing quite an advance analysis and inference that i notice i really understood the topics on the course.

It has a little expensive tuition fee than other courses such as Data Science (Johns Hopkins) and Data life (HarvardX_edx). But I decided this course rather than choosing the others because I felt that it was well organized and quite good supplements. What I like most about this course is instructor. She looks like enthusiastic to give a her idea and wisdom. It attracts me to take this course even though it is expensive relatively. Anyway, I appreciate her for dedicated teaching in advance.

It is really the best Statistics course that i have ever done. After doing all the course in statistics i'm very much confident in statistics. The course and Specialization is very clear, concise, nice explanation with example videos to have better understanding of the theory. It is highly recommended course for anyone interested to learn statistics in their career. Please do the maximum the course in the specialization to have good grasp of statistics if you are beginner like me :)

Very concise and interesting. I was able to brush up many concepts. It is important to indicate to the students, that the materials provided are not enough to create a final project in a meaningful way.

This is because, the code to use R is limited to the basics throughout the course. Extra or outside research is necessary to increase the R skills.

Many students have technical difficulties, sometimes unusual. Is it possible to address them by expanding a FQA section?

Very interesting material. Statistical inference was one of the great mysteries for me, and it is indeed a technical topic. But the professor does a great job in presenting the material in an intuitive way, giving an awesome introduction. Very interesting real examples too.

Looking forward to have a proof-based equivalent course, though maybe I should focus on a forma probability course first.