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.

This was a very comprehensive course. I am currently undertaking a PhD in Medical Studies, and this course gave me a better understanding of the basic theory underlying Probability. This was a useful shoehorn into learning and (importantly) understanding Bayesian statistics.

I would have given this course 5 stars, although the end project was disproportionately difficult compared to the rest of the content. The description lead me to believe that no background in R was necessary, however this did not appear to be the case. I highly suggest having a firm grasp on R and R programming language before looking to complete this course.

It was hard to find the resources at first. Example textbook required is OpenIntro edition 3, but the free edition is an edition 4. Took awhile before I could start due to the odd instructions on how to use R. However, overall the entire course was very comprehensive and great for beginners. It does not take 5 months to complete. You can complete about 2 weeks worth of assignment in a single week even while working full time. This is an 8 week course load if you're choosing to study every day (can be completed in a month and less).

Highly recommend to take it if you're new to probabilities too. Great resources here.

Pretty good course and I definitely came away with a good understanding of the fundamentals of basic statistics. The video lectures move pretty fast and kind of assume that you're getting everything which is not always the case. The student forums help a bit, but responses either from the instructors or other students is unpredictable and inconsistent, so you really can't depend on it when you need clarification or can't understand a concept. I had to go outside of the course several times to really grasp all of the concepts. Fortunately there are plenty of resources on the net that can help though.

The course is well designed and organized. I felt like I left having a clear understanding of basic statistical concepts and their applications to data, and what kind of questions one can ask of large datasets.

The only aspect I'd say that could be improved is to provide more comprehensive R skills that help students better prepare for the final assignment - At least for me, who has never used a programming language before, it was a huge challenge, and I had to look up a lot of additional resources to begin to grasp how to use R to approach the data.

Offers a good introduction to probability as promised. Great material, you can really tell that the teachers have made an effort making the content presentable.

The only thing I did miss however, was a lecture introducing coding in R especially since that is what makes up most of the time in doing the peer-reviewed assignment. Nothing fancy, just a single lecture introducing the logic behind the dplyr and ggplot2 packages would have been ever so helpful and could have been covered in less than 30 minutes.

Thanks for a good course!

The stats info and exercises were helpful for learning stats/probability. The labs were somewhat helpful, but did NOT prepare you for the project at the end of the course. Prerequisites say not necessary to know R coding, but I REALLY struggled getting through the assignment in a timely manner and eventually wasn't happy with what I had to submit to make the deadline. I highly recommend taking a few courses in R in DataCamp or watching videos about R Studio (dplyr and tidyr) before attempting the labs and project.

This course is a solid introduction to conducting statistical analysis using the software R. The video and reading materials are clear and informative, and ideal for those with little to no familiarity with the theory behind probability and statistics. Outside of the weekly assignments, there is relatively less exposure and direct instruction for operating R, but I was able to make use of guides and forums in the coursera page to teach myself about R commands and codes as I navigated the provided datasets.

Excellent lectures. Final project requires at least an advanced-beginner knowledge of R, especially of dplyr and ggplot2 packages. Not sure that's clear from the description. I came in having taken a short Intro to R course -- I'd have been overwhelmed by the data analysis project had I not. I relied on numerous YouTube R tutorials to get it done (thank goodness for the generosity of the R community!) and ended up really pushing myself. The instructor is a star. Many thanks if she's reading this!

Overall, I recommend this course. It has the right balance of instructed work and explorative work (when it comes to the lab work). I would have wanted the probability calculations to have been more closely tied to the R programming in the lessons. That being said, I have no prior experience with R and I come from a humanist academic background, and I found the course material instructive enough to help me learn how to code with R and do basic probability mathematics.

Great videos and testing structure. Great feedback to all of my questions in the forums too. 7 hours a week seems about right for me, with the recommended reading and watching the videos. However the course got continually harder and harder and by the time I was at the final exam I was really worried. In the end it got a bit difficult and stressful but I do feel it was valuable, and like I said, getting the feedback, even on weekends in the forums, helped a lot.

This courses is said to be useful for beginner and I do believe. Honestly, it helped me a lot for my starting position as a data analyst. Thanks so much! However, I just suggest that there would be more explanation and details in each lessons for beginners understand with ease. Moreover, in each lab, I did wish I had the solutions after working with R for the very first time. Therefore, I hope, if it was possible, please add more infos. Thanks again!

Overall a great course for brushing up on your statistics fundamentals - take it for the primer on probability and distributions. A similar approach to the RStudio work would be helpful. I'm finding Coursera's strength rests on its video lectures; in this course you get lectures on the statistics, but relies on written guides when it comes to learning R. Some videos that show how the statistical fundamentals can be applied in R would be great.

The content of this course is not terribly difficult, I thought, but it's a very good introduction into most aspects of Data Science. You learn to familiarize yourself with R quite well and get a lot of independence to create a final project based on a huge data set. One thing I would've liked is a sample completed project, start to finish, to see what was expected- the things that got produced (which you peer review) varied hugely in quality.

As a non-stats brain, overall really good introduction to stats.

The final project for this course seems pretty difficult and is a huge difficulty jump from previous weeks. It requires a good grasp of manipulating data with R and plotting. Regardless I found it very satisfying to complete. The final project is a bit frustrating, as the people peer-reviewing my project didn't put any effort into it. I was hoping to find some critical feedback.

Great introduction/review of basic stats concepts. I think the course designers assume a knowledge of/familiarity with R beyond what they claim in the Course Description. The weekly labs were somewhat helpful, but could benefit from providing the students with a bit more instruction on the functions of R. The learning curve during the Week 5 project was a little steep for a complete novice like me, but overall I found this course worthwhile.

I had to drop this course because I had too many other things on my plate, but hope to enroll again another time. I thought the course was well structured, with very good examples, or explanations, of experiment design. Thanks to the instructors for providing free access to external materials. I thoroughly enjoyed what I was learning. Currently, the only reason I didn't give 5 stars is because of the trouble I kept having with R Studio.

The course videos are good, but the R programming is not well explained. I've enrolled in other Coursera courses that use R, Python or Octave, and they all provide clear demo videos for beginners to get up to standard with the code. This course doesn't do that so it's not a good intro to R, which is a shame because working in statistics these days is all about using R and similar tools so there should be a stronger emphasis on that.

Overall, I think the lessons, the lectures and exercises were presented in a very clean and effective way. Unfortunately, the research project was a real leap in terms of cleaning data and working within R. I did not feel that exercises adequately prepared one for the final project. I think this either needs to be scaled back or there needs to be far more discussion and practice with handing R data before the project.

Presentation of the content, course slides and labs are best from what I've seen on Coursera. The only downside was that to my feeling the final project and the course content are somehow disconnected. The course itself deals with introduction to probability, while final project is EDA. I don't think there was enough materials on EDA in the course, so the final project took more effort and was confusing at first.

Me gustó mucho el curso y siento que aprendí bastante sobre cómo hacer visualizaciones con R.

Sin embargo recomiendo que para el proyecto enseñen un poco mejor cómo transformar el archivo Rmd en HTLM.

I really liked the course and I feel like I learned a lot about how to make visualizations with R.

However I recommend that for the project they teach a little better how to transform the Rmd file into HTLM.

I enjoyed the course very much and found its implementation very clear. The intermediate questions and the quizzes helped to solidify the learning content. Dr. Mine Çetinkaya-Rundel is very sympathetic and it' s easy to follow her explanations. The final assignment, however, was relatively difficult and definitely took more time than was indicated. All in all, I would recommend the course.

I was able to equip myself with basic statistical knowledge through this course. I do feel like the R program could be taught a little bit better. But overall, I am very comfortable with the course content and I cant wait to keep learning and use all that I have learned in the upcoming projects. I especially liked the peer-reviewing part as I was getting more insights into other's works.

Really good foundation course for those who aren't familiar to statistics and gives out great resources to learn or refresh some material. For the assignments, I like how they give an option of either doing it from the DataCamp website or RStudio. I wish there was somewhat a better way of understanding the R libraries in the assignment, but I just don't know what. Overall, I love it.

This course enables one to start right from basics and develop strong fundamentals in exploratory data analysis. One thing that could be improved is providing for more "R programming" commands or reference materials which can be used by the learner to gather more variations of the commands used in that section and thereby improve code and formatting of plots/graphs.