Nice to refresh long forgotten probability concepts. I really like the hands on approach and the fact they emphasize on the intuitive concepts rather than the formulas. However the use of clear terminology is very loose (ex: outcome vs event, binomial used to model trial with replacement without justification, ...). Also don't expect to learn R with this course: it is only a statistics course with some labs using R for illustration of the concepts. You do not need to know R beforehands as you only need to do some copy/paste from the lab document.

Since the reason I took this course was learning R programming, I think this course focuses too much on data theories. From my perspective, it would be better if this course could put more efforts on R programming skills. In addition, when students raise questions on the forum, these questions were seldomly answered by tutors. It is obvious that there were some bugs of coursera platform for a long time, but these bugs were not fixed. However, I learnt how to start R programming by joining this course, which was really helpful to me.

It gives a good explanation on the statistical/probability concepts, but I wish there were more RStudio projects. For example, after each video topic, it would be great to then have a mini-project to do in RStudio related to the video material. There are only 3(? maybe 4) labs in this course, and the labs do not cover all of the material presented. I feel that the course definitely needs a lab working with Normal and Binomial distribution. But again, an RStudio project for each video would be great.

On the whole I thought the theoretical content of the course was good, and that the supporting materials were quite helpful. I would strongly caution prospective students about the amount of time that is actually required to complete the course requirements. Specifically, I found that the amount of time that was, in actual practice, required to learn even the basics of R and to then apply this to actually doing the final assignment vastly exceeded the time suggested by the course instructions.

The textbook is excellent, though it would be helpful to provide some suggestions for a more rigorous treatment of the material. Lectures are well presented and organized. Assessments (which, unfortunately, are what drive teaching/learning outcomes) are of a lower quality. The course project has potential, but poorly executed as a peer review assignment. I have no confidence that anyone with this credential will have met the course objectives. Don't hire based on this course.

Slow down. Introduce more R before asking to create projects in R. Only because I know other programming language was I able to finish week 5. Also very strong group of mentors... God bless you mentors!

Disconnect between course objectives and programming assignments / labs. Reading book you learn one thing, watching lectures another and then unrelated labs, which then culminate in something totally different during week 5?

The lectures are relatively good, though not of consistent quality. Some material is explained very welland some in a bit of a disorganized fashion. The assignments require a level of R knowledge which is neither taught directly nor stated as a prerequisite. For those familiar with cleaning and exploring data with R (ggplot2 and dplyr especially are important packages) this is an excellent course.

It is a fine point to start for a beginner and you do learn the statistics part of the course in a constructive way, but I believe when it comes to learning R it is lacking. You get to learn coding, but not enough as in to be able to apply it in different sort of research! The teaching provided for R is limited and situational, and this is not because it is the introductory course.

Overall a decent course to introduce basic statistics and calculations. A couple of things that I did not like is that the readings don't match the latest edition of the book - they're assigned out of the 3rd edition not 4th - and it's not clear that that is the case. One of the quizzes had a question from the previous week's learning objectives and it threw me for a loop.

Overall I learned a lot in this course, although that comes with a caveat. Some of the more difficult content was breezed over, and I found myself searching outside the coursework to get a better explanation (particularly with probability and most of the R tools.) That being said, if this course is useful as a series of benchmarks to guide you with your own research.

The statistical portion is one of the bests and the professors makes the understanding very easy with numerous examples; however, if you are planning to learn anything about R in this course, then it not for you. The portion dealing with R is highly vaguely explained, which makes it very difficult and frustrating while doing the quizzes; so enroll accordingly.

The statistics and probability portions were very good, but there was almost no instruction with regards to R programming. The project at the end felt very much like an intermediate project instead of an intro project. I spent far more time Googling/YouTubing the gaps in instruction than I actually did completing the project.

The course content was very helpful, and provided a great refresher of probability. However more guidance is needed for the lab exercises. Due to firewalls, I could not access some of the required R content. It would have been nice if the lab exercises reflected the information that was taught in the weekly modules.

I took this course hoping for a fundamental education in utilizing R for statistical analysis. Unfortunately, this course focuses heavily on statistical methods and very little on explaining the R processes used. Good for introductory stats students, not great for those interested in furthering their knowledge of R.

I thought the lectures were a very helpful re-introduction to statistics but felt very disconnected from the assignments in rstudio. I felt lost when I got to the final project. I would recommend incorporating rstudio into the lecture so the students can follow along and practice writing r scripts.

This is an excelent course in statistics but not R; you are barely taught how to set R for data analysis; conversely, the modules focus on developing the capacity to understand basic statistics interpret results and be able to perform probabilistic thinking, but nothing related to data analytics.

I really liked it! Some basic R mechanics were undertaught and I'm very lucky that my data scientist brother volunteered an hour of his time to catch me up. However, I can say that this course genuinely held my interest all the way through with authentic examples and challenging exercises.

It does introduce students to probability very well. But the R learning was very limited to simply following instructions in a file, with little in the way of explaining R or what R commands do. Very limited bridge between the probability and stats part of the course and the R part.

I think the first 4 weeks of the cours were quite easy, and the last week, week 5 was quite hard, and did not really match the first four weeks. It would be good if a little more information was given in the first four weeks about EDA's and other parts of the project.

The course is OK, but in my opinion the price 69€ is higher for the material you learn.

Lower price probably more people take the course paying

Also is a pitty that people we do not want to pay, we could not have a final assignement.

I believe the statistics content is very good, but the R content is very poor, and in the labs where R is used, the explanation is just a text file; there is no person explaining while you are doing the lab

The course is very basic, so it is good for an introduction. Quizzes are simple but the final project takes a lot of time. Why should we have to answer three research questions? Two would be sufficient.

Kind of ridiclous I cannot complete the class without getting my final project graded by another peer when this is a "work at your own pace" class and most discussion posts are at least 3 months old.

Could be improved by providing better tutorials to use R, provide guidelines to use Rstudio desktop as Rstudio Cloud is quite slow and crashes (it crashed 100s of times on loading brfssdata).

well thought out and delivered course, but I would have preferred that it dig in more into the topics. Not necessarily more topics, but deeper treatment of the topics that were covered