Statistical Inference for Estimation in Data Science(으)로 돌아가기

4.3
별점
22개의 평가
6개의 리뷰

## 강좌 소개

This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators, method of moments estimation, maximum likelihood estimation, and methods of constructing confidence intervals that will extend to more general settings. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash....
필터링 기준:

## Statistical Inference for Estimation in Data Science의 5개 리뷰 중 1~5

교육 기관: Daniel C

2021년 12월 9일

I feel like complaining it was very hard, but I can´t, it´s necessary. Jem Corcoran thruly wants you learn and share her experience with you. I recommend to go through the chapters again when finished, the concepts are not easy to digest at first.

교육 기관: Óscar L R F

2022년 3월 4일

I​t was a tough one for me... completely worth it.

교육 기관: Hidetake T

2022년 4월 19일

In depth understanding

교육 기관: Derek B

2022년 6월 24일

This class is okay. I think there are some good things about it. I'll start with those. First, the instructor does not dumb the math down, which I respect. It does feel like some of the other MOOCs soften the math to make customers happier, and this course does not do that. My brain hurt at a few points, but I'm assuming this is good pain. You will definitely feel more confident in your math abilities after completing this course. I also really appreciated having someone explain why we use things like the t-distribution or the chi-2 distribution, rather than just presenting them as magic. So that stuff is great.

My complaints are mostly connected with the non-credit version of the course. Basically, this course charges a lot, even if you are not taking it for credit. The average I've seen is \$39 a month, and this charges twice that at \$79. I would say that is not a big deal, except that Boulder also provides less support than your average Coursera course. And by less support I mean none. Most other courses I've taken do seem to have some moderator who will answer questions in the discussion forum. I have not seen any moderator for this course. I have not had any of my questions answered. Boulder is not interested.

This wasn't really a problem for the first course in the series, "Probability Theory," because that course provided a lot of different kinds of assignments to help you master and understand the concepts. So I felt like the course was still worth the extra price. But the assignments here are almost entirely quizzes, and a lot of the material is much harder to understand. Despite passing all the quizzes in weeks 2 and 3 on the first try, I don't really feel like I have a good handle on what was going on in those modules. And the course does not recommend any additional resources--problem sets you can work on on your own, reading, etc--to help get a better understanding of what's going on. A lot of the difficult mathematics in the quizzes felt more like proving you could do mental acrobatics than anything that would help you get the concepts.

I also think the instructor for this course and the instructor for the previous course in the series need to coordinate more. The first module of this course seems like it is supposed to be review of the topics covered in the previous one. But actually we are asked to do things that are much more complicated than the previous course covered, and we go through the material much more quickly. Either the previous course should be a bit harder, or this one needs to be toned down a bit.

So there are definitely things to like about this course. But I think Boulder needs to do more to justify the price tag.

교육 기관: Parth K

2021년 12월 29일

Quite a few technical issues with labs and programming assignments which prevent you from progressing in the course.