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.

Learn a solid foundation covered all key topics in inferential statistics. It explain in more depth, the statistical treatment is rigorous, eg, what assumption needed to apply. Instructor is passionate in teaching, explain good and clear, and is very effective to use output from running command to relate theory of which item referring to which item of output, which make it easier to understand. Both quiz and lab exercise are important part of learning, as it need to apply to learn better. There is a final project, which provide a dataset and come up with research topic, which is very useful to consolidate what was learnt throughout the course and apply it in real life. It is good to gain some experience to run inference command to do hypothesis testing or get confidence interval, not just solve it in paper and pen.

This is a little tough compare to the 1st one but the challenge is just right to keep me engaged with the course material so I will never leave without writing my feedback. I always want to enhance my knowledge in Mathematics and it seems that I am loving statistics even more that I am so eager to dedicate some time to this continuous educationeducation despite I am pre-occupied with teaching task in the physical classroom. It may have taken me so long before I was able to finish this but I will see you on the next three more chapters and hope to learn more. Thank you so much Coursera, Thank you Professor. Wish I can apply all these learning in research and teaching too.

Thank you so much! I learnt what I came to learn. Well curated and organized. Explores all potential areas of statistical inference and hypothesis testing and gives you a framework to relate between the two. Only feedback is to have one final conclusion/summary video where you can summarize how to use everything that we have learned based on the type of variables and the question at hand, i.e. mapping the learned approaches to types of problems. It was available as a scoring criteria in reviewing peer assignments but would be great to have it as a separate concluding video/note as well.

I feel like I gained a solid foundation on inferential statistics from this course. I found the videos useful, with many well-explained examples. I would say that the readings should not be treated as optional - they were where I did the bulk of my learning. The scope of the final project is reasonable.

It may be relevant that I also took the previous course in this specialization, and I can see that not having done the previous labs would have made the project a little harder. As other reviews have said, the forums also seem much less moderated than the last course's.

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.

This course was more intense than the first one in the Statistics with R specialization. Lessons are logically organized and well constructed. Instruction was generally clear. I would have preferred submission of the project with peer evaluation as opposed to self evaluation only, although the instructions provided were clear and well written. Going through all the material (listening to lectures, working exercises, quizzes, and project) takes much more time than advertised, and is well worth the time investment though.

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 R skills.

Many students have technical difficulties, sometimes unusual. Is it possible to address them by expanding an 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.

Very good explained course, with lot of useful exercise, so you can be sure to understand the theory. Th practical examples in R are well designed and explained. This is definitely a must for someone interested in statistics, with beginning concepts that you need to keep in mind for further coursers. The teaches is also excellent, explanation and examples are very good. Recommended!

I really enjoyed this course, it is pitched at just the right level for someone who is sometimes busy with part time work. It does ask that when you study, you study well. I've not learned any programming before these courses, so sometimes my knowledge of R is lacking. But it is rewarding to learn R to really see these statistical ideas come alive.

If you are new to this, get ready to sweat. This course teaches many many concepts and if you think that it tiptoes around it you will be surprised. You might walk away and forget some of the methods and tests since there are too many, but you won't get away without learning to interpret and reasoning absolutely everything that you compute or do.

The course is well-paced, covers the relevant topics from the basics and gives you a bit of freedom on the project. The comments made for the first course in this series still stand: there is a lot of statistics and a little R. However, it is not as painful to code as it was during the first course (although still need to google a lot).

Great course, the professor is able to explain and articulate complex ideas into a digestible manner. If you ever taken a statistics class you know would know how incredibly rare it is to have such a great professor able to explain the material as well as she does. Thanks you very much Dr. Mine Cetinkaya-Rundel for teaching this course.

Nice refresher course on inference testing, with a broad coverage of the types of variables / analyses.

Not heavy at all on the math side.

Students have to find their tips for R-coding on the Internet for the most part. It is a good way to learn! But we could use more standard tips from the course itself, especially on EDA.

The best online course I have taken so far. It teaches you all the statistical methods you need to do for inference. The lessons are well taught and organized in a way where each lesson builds off the previous. The final project is also a great way to put everything you learned throughout together.

Had a great learning experience with in depth knowledge of statistics, inference and hypothesis. Structure of the course helped me grasp things in an organized way. The use of real time data to explain concepts had a great impact in making things easier to understand and relate to things around us.

Excelente curso. Vale totalmente la pena tomarlo. Es muy importante atender la lectura sugerida y tomar notas. Complementando la platica junto con la lectura sugerida estoy seguro que tendrás las herramientas necesarias para iniciar un desarrollo en el ámbito de análisis de datos.

The course is structured very well and has some great content, suitable for anyone who is just beginning with statistics in R or if you want a refresher on statistics. Dr. Milne is a great instructor, i have taken some other courses of her's and was never disappointed.

Great course, really useful and amazing explanations by Dr. Mine Çetinkaya-Rundel. I learnt a lot and the data we are given to analyse in the final project is really interesting. Totally recommend this course to anyone interested in working with stats in R

Really good course on inference. The statistical tools and the reasons why those tools are used, are explained well. I am looking forward to the last week's exercise and the next courses of the series. This one is a bit more difficult than the first course.

The content of the course of was informative and clear. The lab sessions were really useful in evaluating one's understanding of the topic while introducing useful functions in R though more emphasis could have been placed on ANOVA during the lab sessions.

Great course, good for begginners or those who (like me) wants to gather separate pieces of knowledge into a solid picture.

Good examples, well explained, not too much calculus, appropriate quizzes. Reading a book accompanying course is also very helpful.