Thanks U. Michigan..

Spectacular Course!

Good Intro course

very informative

Excelente curso.

good course

Excellent!

very good

excellent

Great!

nice

ok

1

Another interesting course - the final one in this specialisation - but the difficulty really ramped up in Week 3 after the final peer marked assignment. I had been so impressed with the clear explanations, revision and review, and the opportunities to apply new knowledge. However, it all became very abstract - I thought Mark did a good job but perhaps Bayesian is a whole different specialisation. Overall, I really enjoyed the specialisation and I am pleased to have received a good grounding in statistics ahead of my Data Science diploma. Thank you to Brenda and Brady especially but everyone was very strong and the future is bright with some enthusiastic young talent coming through at Michigan. Edward

It is a good introductory course for statistics. The programming assignments were not challenging enough to cement what you have learned. The concepts in week 3 and week 4 were challenging and their approach was not good. I feel like I wasted my time. The focus should have been on multilevel model fitting rather than covering bayesian statistics. Week 4 only added more confusion. However, as an introduction course, they did a good job of presenting the concepts in the prior courses of the specialization.

Overall, this course clearly conveys the general ideas about model fitting. The python labs of week 2 and 3 are helpful. However, the materials for week 3 and week 4 lectures are not as good as others in this series. I understand that the author tend to avoid confusing learners with complicated math. Unfortunately, jumping to piles of conclusions without any necessary justifications leaves learners lost.

I found the course to be good. I don't think it is excellent. Lectures can be a bit too long take some time to get to the point. Instructors are "ok", a lot of talking on most of them not enough math examples. Labs are pretty good but... I guess I can say that there are 5 star courses on this platform and this is not one of them. Its a solid 4. Still recommended.

The videos were really lengthy, above 15 minutes videos are hard to understand for me. Although the overall specialization is really good and gives me very confidence. I would recommend everyone who wants to be a data scientist in future.Thanks Brenda and Brady T West and of course Julie Deeke and other students.

The course is alright. They give a high-level overview of linear and logistic regression, and dip a little into Bayesian statistics.

Note that they use the StatsModel package in their practice assignments. So I was a bit disappointed I didn't get to practice sklearn, which is about x10 as popular in the field.

python codes were pretty tough to undertsand in the end but the concepts though difficult to understand the faculty did there best possible to make it understand. Python codes should have got little bit more time to be explained

this course consist of very important topics , they give you an overview of these topics and you have to dive in .

some information hard to understand and other easy .

i enjoyed learning this course

Overall, the course was a great refresher of statistical theory and application with some great Python exercises. However, some of the Python coding instruction itself could have been more detailed.

A great introduction to regression and bayesian analysis in python. I get that the content is hard, but they sum it all well. I would recommend for those who have prior knowledge of statistics.

The code examples may be more precise with detailed comments. Some codes are not understood, in other words codes can be refactored in a way that can be more suitable for reproducible studies.

Very good course, I like many practices and evaluations focused on database of real cases, perhaps it would be advisable to reproduce results from the same sources .....

JL