Nice course,enjoyed it the most till now out of previous courses of Data Science Specialization. But is tough for people from non-Statistics background. I am a Statistics Major and I have studied all these topic in great detail so I didn't need to watch much videos.

Excellent course. Brian is a very good lecturer. Recommendation to students...as he's lecturing and showing you the R code, type it in yourself and get it to work. This makes the lectures take twice as long to get through, but it's 100% worth it.

I found this course really good introduction to statistical inference. I did find it quite challenging but I can go away from this course having a greater understanding of Statistical Inference

The information is so important and useful, but I found the presentation of the material to be fast and not very interesting, and therefore it was hard for me to retain the material. I learned a lot, but I would need to invest a lot more time to realllyyy grasp everything in the course. It wasn't presented in a way that made it easy to learn, so I need to spend more time going back over things to really get it.

There're lots of practice on manually construct statistics. I'm not sure if it's necessary to do that since we could just use R code to do it. I think how to interpret it and use these statistics in examples would be more important. There're some examples, but could be more and interpret more in depth if there were less focus on the calculation.

I found that the materials given or the lectures never allow you to clearly follow a structure.

I understand that are so many contents to present, but jumping around from one to another is not the way.

Quite frequently a lot of the slides are just useless. Not all of us have the time to go behind every mathematics, so I would like to see more real examples of how to use the contents you teach us, than knowing all the mathematics and have a lot of slides to show how to deduct mathematically the probability of something to happen. But might be my opinion because I had other expectations.

The material covered is quite a lot, but the course content is disorganized and the delivery is not engaging. At most, you can use videos and slides as a reference and learn from other sources (as I did).

I completed this a while back and now just reviewing the swirl exercises. Brian Caffo is the best instructor of the dozens of classes I've taken online. He is a little challenging at times, but that is what makes him such a good teacher. Many teachers get high ratings because they water down the material so much and give simple quizes and assignments. Too many courses are like that and that is bad quality.

Observe how Caffo teaches - this is great teaching. The other instructors in this specialization are OK, but Caffo is the best.

He teaches the whole Specialization, Advanced Statistics for Data Science. I have completed the first of four courses, but plan to continuing with that after completing this one.

Thanks Professor Brian Caffo for one of the best courses on Coursera!

This course is tough, informative. Good for people who want a summary of all the statistical concepts you can use for data science. You'll get the most out of this course not by expecting it to be beginner, because it is not. This course is best supplemented by having background knowledge in statistics. Meaning, learners would be much better off if he/she did some statistical course before. This course will provide the practical experience of implementing statistical concepts in R.

This is so far the most difficult course in the specialization, but also the most useful. In this course you are taught to think like a scientist, to test hypothesis and provide evidence for your analysis. The lectures are succint and clear, the quizzes are clever and useful and the final project will make you create a very beautiful report while doing scientific work, which is the reason I started studying data science in the first place!

I really liked this course, especially the course project at the end - the second part felt like (a really simplified version of) a task one might actually have to do as a data scientist, and I liked that through this course and the previous ones, I knew exactly what I had to do. The course itself is pretty mathematical and I think intellectually the most challenging so far, especially since it's a lot of content for 4 weeks.

Excelent for people from other areas (engineering for ex.) to get the initial grip on these valuable tools. The companion book and the interactive swirl exercises are great to complement the pragmatic explanation of Dr. Brian. I sincerely recomend.

This course covers the very basics of statistical inference which will help to strengthen your base concept. I loved doing the course especially the practice assignments on swirl.

Thanks.

Excellent structure and great refresher for any Statistician.

It is a good introductory course

super useful in our work

Course well outlined!

Interesting

Great cours

Very good

good

This course will give you a good basic understanding of statistical inference. However, if you are a complete beginner I highly recommend first doing a basic statistics course (such as the one presented by the University of Amsterdam on coursera). Also, do use the recommended textbook and take your time to understand concepts. This course will also work better in context of other courses in the Data Science specialisation (for example R Programming and Exploratory Data Analysis). This course took me 4 months to complete with regular effort (not 4 weeks as suggested!), so factor in extra time for completion.

This is an excellent course, though it is fast-paced. I didn't have time to watch the lectures and also do the practice exercises in Swirl in the time allotted. As usual, the time estimates for completion are wonky. I ended up just watching the lectures and taking the tests, which is far from ideal (I am taking some time to do those valuable exercises now that the course is done). Although I got 100% in the course, I felt the learning experience could have been better as a result.

Quite useful to most scientists that rely on data (real/from simulations) to draw conclusions. The fact that the course was generic and widely applicable to all fields was the highlight!

Thorough course. A bit difficult to follow the lectures at times.