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BEST

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非常好！

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This is a really great course, but it is NOT easy!

There is a steep learning curve for people who are beginners in R with the project at the end being quite challenging. The stats part of this course starts with a good learning curve and picks up speed quickly as well. The videos available for this course are generally good quality but could be ironed out a bit more in terms of some rough editing. You can see the age of the video material showing up in some of the videos.

Overall there is good help available on the discussion forums and using the statistics textbook and practicing with the example questions is highly recommended.

The time required as stated for each and every aspect of this course is vastly underestimated, a trained person will be able to finish this in the given time but a beginner can easily double or triple the time needed for the tasks.

Weekly time usage for class and all example questions plus digging for problem solutions in the forums is between 7 and 10 hours, the project at the end for an absolute beginner alone is easily another 10 hours hacking in R plus some extra time getting used to the project material.

Do NOT attempt this course if you can not dedicate enough time for this course!

The course achieves its objectives of teaching R and statistics. Most material is explained clearly and is suitable for beginners. I liked the style of the lectures which weren't boring as well as useful R labs.

I felt that there was a steep learning curve for the programming bit. Having done a course in a different programming language last year, I found this one is quite light on the basics of R. It is good as it forces you to learn through trial and error and google loads but it would be less painful if someone just explained things. I suggest doing https://www.coursera.org/projects/getting-started-with-r, for example, to get a bit more basic understanding (just 2 hours) prior to embarking on this course.

The project at the end took me many hours to complete (a lot longer than advertised) but also taught me lots. Again, if instructions were less vague would have been easier but a challenge can be useful.

Overall, thank you very much for putting this together and sharing the knowledge.

The lectures in this course were fantastic. The professor clearly explained complex topics, and I would gladly take more courses from her. However, I can't help but think that I wouldn't have been able to complete the final project successfully had I not taken previous classes in R. I don't think there was good alignment between the course itself and the final project. Also, the final project involved complex survey design data, but it was not clear if we were suppose to account for this (I assumed not because it seemed out of the scope of the course). I asked this question to the discussion board but never received a response. It was less satisfying to complete an exploratory data analysis knowing that all my numbers were inaccurate because of not taking weights into account. I would suggest a different dataset for the final project.

I have been looking for this type of course for years that combine a solid statistics background and learning a new piece of technology. Most statistics class are cram classes where the student needs to choke down a formula, regurgitate it for an exam, hope it is correct, quickly for get it and move onto the next. This class is the opposite. Concepts are easy to understand, logically thought out and in bit sized-pieces.

Professor Çetinkaya-Rundel is the best statistics instructor I have ever had. She clearly explains concepts, backs them up with applied examples and the textbook is extremely well written. I enjoyed learning R but sometimes the statistics lesson and the R were divorced from each other. The final exam was an excellent concept but it was a little daunting and way above my pay-grade.

Overall, I would recommend this class. I found that the preparation towards the final project could use some improvement, especially plotting using the 'ggplot2' package. Why not work on this during Week 4 through an assignment that works on this? This would have made it easier to focus on the research questions of the project and less on the graph making mechanics. Also, I found that the project was a little bit too open-ended and could have used some more input from the instructors' side. The material during the four weeks of the course was really good and thorough but perhaps a little too difficult to follow for people who have absolutely no background in probability theory. Maybe one should audit the course first and then decide whether it would be a good idea to formally enroll.

I came to this course already armed with some stats skills with the hopes of brushing up on my stats whilst getting to learn to use R. The course is a nice, easy to follow introduction to data and probability. I had no R experience at the start of the course and the weekly assignments helped me build my R skills. However, the R skills taught were not sufficient for me to tackle the final data analysis project. I had to spend a lot of additional time teaching myself R. Before or shortly after starting this course I would suggest others take some time to self-learn (through youtube etc) about data frames, r markdown, dplyr, producing nice looking tables of summary statistics to be shown in r markdown (for the final project), and ggplot2 for nice looking data visuals.