25 oct. 2018
Course is compressed with lots of statistical concepts. Which is very good as most must know concepts are imparted. Lots of extra reading is required to gain all insights. Very good motivating start .
24 sept. 2020
the teachers were awesome in this course. I liked this course a lot.Understood it properly.Thanks to all the beloved teachers and mentors who toiled hard to make these course easy to handle.Gracious!
par Joana P•
22 févr. 2018
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.
par Christoph G•
13 sept. 2016
I have to admit, that most of the videos confused more than they helped. For most of the topics I simply went to KhanAcademy and got it easily there and I was asking myself, why a topic was made so complicated. I have the same issue with the next course. I highly recommend to start visual explanations of what you want to achieve instead of throwing formulars at people and confuse them. IN KhanAcademy e.g. they do exactly this and you got the point and if you got the point, the formular wasn't such a big thing anymore.
par Ramesh N•
18 mai 2020
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).
par Huynh L D•
17 juin 2016
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.
par Boris K•
12 oct. 2019
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!
par Angela W•
19 oct. 2017
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.
par Kaie K•
16 janv. 2016
Even as a mathematician I found it super useful to participate this class. I have learned similar material in an undergrad course, but I forgot most of it. In fact this course is so much better than the undergrad course I took, because quizzes and the project help me to learn the material by practical exercises. I am really thankful for the Data Science team for this course and all the Data Science Specialization!
par Ritwik V•
8 juin 2020
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.
par Justin H•
13 juin 2021
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.
par Long H•
31 janv. 2016
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
par Pankaj K•
16 oct. 2018
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.
par Don M•
1 févr. 2019
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.
par Audun T H B•
1 oct. 2019
Thorough course. A bit difficult to follow the lectures at times.
5 mai 2019
Not my favorite course in the series, but I did learn a lot. I highly recommend following along with the course book provided in the course. The videos alone are not enough. I also recommend printing out a sheet with statistical formulas to use (not provided from the course, but you can find easily on the web). The stat sheet with formula helped me connect all the dots and better understand when to use a formula.
par Mingda W•
5 juin 2018
My most recent experience with statistics was about 2 years ago, and it was college level statistics. Still, I find this class is hard to keep up sometimes. In general, I felt like the professor explaining too much on the mathematical meaning behind equations instead of talking about the real-world meaning of equation components, and why those calculation make sense.
par Stefan K•
2 mai 2020
I found the lectures hard to follow, they didn't help me one bit. If you get his book, read it, and do the exercises, you can save yourself some time.
10 déc. 2019
The materials offered from this course is far away enough from understand the content :(
par Robert K•
16 avr. 2019
A lot of material to cover - can be a strain, but well explained for the most part.
par Tomasz S•
18 janv. 2020
Very fast course... Additional reading required.
par Vincenc P•
11 févr. 2016
I am left feeling this course needs work. I don't know if it's the pain of switching to the new platform or what, but the total lack of any support from the TA/instructor team is frustrating. Add to that the fact that Brian skips from slide to slide very quickly often not providing adequate explanations and you'll be re-watching the videos many times over.
Several of the videos have blatant errors in them, like the fast that the fourth video of a week also contains the entire third video... again.
Such things should not have passed a half decent QA test.
More than anything this specialization should not be marketed as "no previous experience needed". You need to know some statistics. And by some, I mean do the whole thing on Khan Academy first.
par Anant C•
6 mai 2020
The content of the course was well organized and structured, but the content delivery in the videos was terrible. I was unable to understand even the basic concepts from the professor due to his fragmented sentence structure, lack of lecture planning and an emphasis on evaluating R code more than on explaining the concept. I am now hesitant to go on to the next course in this specialization. The Swirl() exercises, however, were very thorough and did a good job in explaining the concepts.
par John M•
29 sept. 2019
This course was very hard to complete. The lectures were harder to follow than the previous courses.
par Alexander D•
31 janv. 2020
Wouldn't recommend for those learning stats. Try Duke's course instead. This one was poorly taught.
par Tongke Z•
7 oct. 2020
The most boring and nonsense course I have on the Coursera so far. I have a PhD degree in Stem, and had taken statistics courses during my undergraduate, and also had some teaching experience. I can't believe they can made a course like this quality. It downgrades the reputation of the department of biostatistics at the JHU. I saw some criticizing comments before I took the course, but I thought it would be OK and I would get through it. But after taking two weeks' courses, I just feel so frustrated and furious and can't help to write down my comments.
The format of this course is like, first, read out the parameter, and then read out the notation, without giving any explanation about how to calculate this out, why we want to introduce this parameter, and how we use this parameter. And then the instructor gives an example, but I don't see any of the examples emphasize the notions.
I just can't help to write down my comments. I don't want to give even one star to this course!!!!! Such a shame.
There should be some teaching centers at the JHU where some teaching professionals can help to improve the structure of these courses and give instructions about how to deliver the contents organically. I beg you to have some improvements.
par Renata G•
28 mars 2021
I hate this course. The instructor's way of explaining things was not that good. Could not understand most of the concepts.
This course was
very, very, very disappointing to me. It were hard to complete, hard to follow the slides. Wouldn't recommend for those learning stats.
A lot of the concepts, although simple when you think about it and used pretty much every day, I felt it were really difficult to understand at first. Wikipedia and some other online sources, and youtube videos, were more helpful but I think the real issue lay in the teaching style.
Brian seemed a very intelligent person, but he does not teach well. His way of explaining things was really bad: he speaks too fast (sometimes he changes terms...), he skips from slide to slide very quickly, he often do not provide adequate explanations.