This is a brilliant course that makes statistics and probability as approachable, engaging and clear as humanely possible.

Prof. Mine Cetinkaya-Rundel explains every subject very clearly, and has included some very effective quizzes and lab exercises.

I first encountered R markdown files in this course and have used them constantly ever since.

My only tiny point of criticism is that the non-graded exercise quizzes are way easier than the real quizzes, and do not really prepare you at all to the more complex questions in the actual quizzes. It's a petty and unimportant kind of criticism in an otherwise wonderful course.

If everyone taught stats like Prof. Cetinkaya-Rundel, this important subject would have been a whole lot better understood and utilized globally.

Mrs. Mine is the greatest teacher that I have ever seen. Her real life examples are highly benefical to grasp the each detail of the subject. She is not a professor that talks only about theoretical background and not mentions any practical case. She gives the theoretical background and more importantly she solves a plenty of real life examples and after that, it is not possible for you to not understand the subject. Thank you so much for giving this course as I supposed that statistics is only pain, but now I know the concept and I can solve problems easily.

There are rmd files in the course that teache how to use R-studio and I think they are enough to understand how to use the program. Thank you for everything.

Excelente. Es un buen curso introductorio. Hace particular énfasis en las distribuciones normal y binomial. Da una pasada introductoria a R que, entre otras cosas, no es enseñado durante las clases sino que a través de los talleres que se realizan al final de cada capítulo. Son explicados con solvencia conceptos como correlación, causalidad y generalización.

Para quienes no saben, desconocen o no han tenido contacto con markdown valdría la pena ver un par de vídeos en youtube. Yo manejaba algo de R, pero nunca había tenido contacto con markdown y me pareció una herramienta muy útil, y aunque no es explicada en las lecciones o en los talleres, el proyecto de final del curso debe ser hecho en markdown.

Professor Cetinkaya-Rundel's explanations are clear and she gives many examples, the quizzes are fair and I think it is an excellent idea to have a lab in R to get students familiar with that tool.

I recommend that students read the book chapters and do the practice problems there, it's very helpful.

My one criticism is that the amount of R taught in the course is not really enough to do a good job on the capstone project, because the data in the given database is formatted very differently. I think maybe the course staff could reformat the database to make it more user-friendly for beginning R users, but in the meantime you may want to study a little R on the side at, say, DataCamp.

Very, very useful course. Exactly what I was looking for. You have to do some research beyond what's covered in the labs to really get acquainted with R, but there is so much available online that it wasn't an issue for me. The open-intro textbook is fantastic, and the course lectures help summarize the textbook info in a rich way that adds to the textbook content. The suggested problems, quizzes, labs and final project were all fantastic for reinforcing the content learned and actually putting it in practice (as most of us learn best by doing). All in all, I'm extremely pleased, and I'm moving onto the next course in the Statistics with R progression with much excitement!

Absolutely amazing! It is clear that the professor, Ms. Mine Çetinkaya-Rundel is passionate about the subject and knows it inside out. The practical example-based approach to learning is appreciated, since a lot of statistics courses don't give learners a realistic setting to think about their knowledge, leaving them with the infamous 'how will this help me in real life?' question. The book, OpenIntro is also very helpful in this regard.

The R course has been introduced nicely too. The difficulty curve might take time to get used to, but the packages introduced and the codes used make sense, so it should not take too much time.

Wholeheartedly recommended!

I really liked this course.

The course comprises of lectures, which are clear and are rich in examples, and of practical assignments, which you do in R.

The practical tasks is where the course shines - everything is explained very clearly, there is a lot of content, and the course works with databases that are huge (thousands of cases and hundreds of variables) and have some of the more common problems (eg. missing data). I have little to no prior programming experience, just for the record.

Mind you, this is an introductory course, as the name states, so don't expect to be a master of R or data handling after finishing it, but I feel I learned a lot here.

Excellent course. Classes were intense and the professor was very didactic. It took me around 10 hours a week of dedication, and the Final Project of the last week required around 40 hours of work. I am very pleased with the final result, but I think it is important to let it clear the real time expected of effort here. Unless you are already really familiarized with the concepts and with the R platform, the course requires a strong commitment. My final verdict: I am very grateful to have done a course of this quality from where I am. Thank you: professors, mentors, developers and fellow classmates! Every minute of my time was worthwhile with you.

Introduction to Probability and Data (by Duke University) is an excellent course. It's like a beautifully and clearly presented piece of the history of statistics. This course must be taken by all who are interested in the type and dynamics of relationships between various elements of life. At the same time, the duty and responsibility of mental reality (=ideology) is to reflect the actual reality. The dogmas of the traditional statistics revolve around a traditional family model which excludes present day gender diversity. This makes traditional statistics no longer reflective of the actual reality unless it incorporates gender diversity.

Very rigorous course. No you don't need programming experience to complete, however you will be thanking your self if you spend as much time throughout the week learning R as you do on the content of the course. I was stuck at the week 5 project for a while until I buckled down and spent a few days on Youtube and on Google learning R. So with that said, if you have no prior exposure to programming or Stats, this course is very difficult, however if you stick with it, it is very worth the time spent. My tip is to reset your deadlines as many times as you need to if the content is more difficult than you anticipated.

I really enjoyed taking this course. The accompanying book builds builds and teaches the concepts very well, and the videos are great resources to help solidify the readings. There is very occasionally some tricky wording in a quiz question, but it is barely an issue. Like many other reviewers, the Week 5 project stopped me in my tracks at it felt very daunting, but I then found further information from instructors/mentors on the forums, which were a great help (I would suggest maybe sharing some of the forum information in the project description, or linking to the forum more clearly for further support).

The course was well-structured and the instructor clearly illustrated statistical concepts to students who have no prior experience in the field. Although the lab assignment for each week may seem a bit stressful for beginners, the overall learning is highly inspiring and does prove rewarding as students finally get to apply the technical skills to their final project. Also, the guidance for each lab assignment was very helpful. It would be even better if there are example code answers for the lab questions, since some of the questions are a bit more complicated. Overall, this is a worth-taking course.

Though you may feel at the beginning that the pace is somewhat fast, but you'll learn a lot if you stick to the material and worked on the labs and the hands-on tutorials. Not to mention the project example in week 4, it was incredible I really liked learning through the errors and interpreting what are these errors and why they may arise. In the project I learned a lot, I felt it's not an easy task to start working on a dataset from A-Z with complete freedom to formulate research questions, clear the data and get the appropriate inference! Overall it was a great course that I really enjoyed :)

I really enjoyed this course - the instruction and materials were high quality and very helpful in clarifying statistical concepts that had seemed unnecessarily confusing to me prior to taking this course. The assignments were very helpful in teaching R, with the final assignment requiring slightly more familiarity in R than the first 4 weeks prepare students for. My advice for students who take this course is that if you have the time in the first 4 weeks, try to learn a bit more than is minimally required in R to be best prepared for the final project. Overall a great course!

One of the well made MOOCs. There are many courses in Coursera taught by good professors from good universities but are badly designed for an MOOC environment making it a bad experience. This course is really well designed. The contents is modular and lectures are split into easy to grasp chunks. The weekly lab exercises using R using real datasets is a plus. Though not much of R syntax is taught and it is up to us to explore(understandable since the goal of this course is not to teach R). The final project was a bit challenging but fun. The course 'mentors' are helpful.

Very clear and simple to follow along with. This allowed me to brush up on and formalize my stats knowledge a bit more, but I have no doubt that it would be good for a complete newbie. The lectures are succinct and comprehensive and there is a free online textbook for reinforcement as needed. Moreover, the R labs and project really add a LOT to the course, jumpstarting my knowledge of R, R studio, and how to perform some exploratory data analysis. For prospective data science folks, I recommend taking this in conjunction with The Data Scientist's Toolbox.

Great intro course into both stats and R. I especially like how the videos succinctly explain all the concepts in such short lessons, supplemented by the thorough readings that provide more details and the lessons in R.

The data analysis project, while challenging at first, did a good job of providing an interesting data set and forcing us to come up with the rest. If I had my hand held all the way through, I wouldn't have learned as much as I did, as I was forced to look throughout the internet for code to perform functions I was anxious to use.

This is an excellent course to lay down the ground works for further courses within the specialization. You'll get the necessary introduction to statistics along with beginner level knowledge of the statistical tool R package. While certain area of the course, especially week 5 data analysis project, can be challenging. Know that the discussion forum is always there to help you if you are stuck. The quiz and project is never outside the scope of the course materials. Totally would recommend this course for anyone who interested in statistics.

This course made principles of probability interesting, going beyond the usual examples of coin flips, dice rolls, and card draws. The discussion about the limits of which observed trends can be applied to a greater population of interest was clear and the project gave us an opportunity to put it into practice ourselves. I especially liked the opportunity to ask questions of a large dataset and generate tables of data and graphs to illustrate these tables a bit more clearly. I feel I now have a good foundation upon which to build!

Excelente equilíbrio entre teoria e prática. O material de leitura é muito bem elaborado e bastante completo. As aulas são excelentes, vídeos curtos que focam diretamente no assunto em questão, com uma apresentação bastante visual e compreensível. Exercícios bem equilibrados entre as funções matemáticas e seus correspondentes na linuagem R. O material de leitura possui mais de 400 páginas, mas não se assuste, esse curso cobre pouco mais de 150 e o restante se refere aos demais cursos da série. Curso altamente recomendável.

I highly recommend this instructor and this course! Excellent teaching, good practical examples that show you why statistics are such a useful technique, very clear lectures, good step-by-step explanations of solving example problems. The free textbook (written by the instructor) that accompanies the course is likewise excellent. If you watch the lectures, read the book, do the problem exercises at the end of each section of the book, you will pick up an excellent grounding in statistics.

I enjoyed the course very much. I appreciated that the course was teaching practical skills that we could immediately apply to solve problems using real data. I was pleasantly surprised that I was able to explore a big dataset from a US survey, formulate my own questions and actually get some answers! Although I found some of the assignments challenging, and I spent on them more time than expected, it was worth it. I guess it helps to know some basics of R programming beforehand.

Interesting, information-dense and well presented lectures by someone who obviously has a deep understanding of the topics and who is passionate about teaching the subject. Added to that: a great course textbook and useful R tutorials with a focus on commonly used libraries such as dplyr and ggplot. Beginner and intermediate statistics students, as well as teachers interested in the presentation of statistics theory and practice, can't go wrong with this course.

Great course! The tutor is exceptional and the hands on practicals were very thorough. It was almost unbelievable that a person like me with no prior knowledge in any programming language could have so much fun learning one for the very first time. However, I feel it might be a little more helpful if the introductory instructions at the Week 1 lab could be a little more comprehensive as some of us actually have absolutely no prior knowledge with R.

The lecture were great and it was easy to digest and remember. At first, it was hard trying to familiarise with R and understanding the technical aspects of R programming but I survived the course and was able to complete the assignment. I would say that the most learning came from my own peers through the discussion forums and peer-review assignment. This course has definitely increased my interest in learning R and performing data analysis with R.