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Отзывы учащихся о курсе Регрессионные модели от партнера Университет Джонса Хопкинса

Оценки: 3,165
Рецензии: 529

О курсе

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing....

Лучшие рецензии

12 мар. 2018 г.

Great course, very informative, with lots of valuable information and examples. Prof. Caffo and his team did a very good job in my opinion. I've found very useful the course material shared on github.

16 дек. 2017 г.

Excellent course that is jam-packed with useful material! It is quite challenging and gives a thorough grounding in how to approach the process of selecting a linear regression model for a data set.

Фильтр по:

451–475 из 510 отзывов о курсе Регрессионные модели

автор: Manuel M M

10 февр. 2020 г.

The content was exposed in a very confused manner. I did not like how the teacher explained. It seemed more difficult than it really is

автор: LU Z

26 сент. 2018 г.

Starting from the first week swirl practice, course content is poorly organized making even simple concept difficult to understand.

автор: Hendrik F

17 янв. 2016 г.

I find it very tough to understand everything. Buying the course book helps to overcome this. You have to dedicate a lot of time.

автор: Mark S

23 апр. 2018 г.

Lots of math, but it would be more productive to focus more on the output of R and better understand the results

автор: Mertz

20 мар. 2018 г.

Bad audio and video quality. Too fast on some complex ideas and too slow when come repetitions between videos...

автор: Andres C S

1 мар. 2016 г.

I think this course needs more emphasis on practical applications and less mathematical background.

автор: Erwin V

20 дек. 2016 г.

Very interesting course, yet course content could be spread more evenly (week 4 is really a lot)

автор: Prabeeti B

17 сент. 2019 г.

Course has more theoretical concept than application.. It has to be more application based

автор: Praveen J

22 апр. 2020 г.

I think a revamping of the concepts in a more ellabroate way is required in the course

автор: Suleman W

9 нояб. 2017 г.

I did find it difficult to follow and understand some of the materials.

автор: Rafal K

28 февр. 2017 г.

Many things are not clear enough in multivariable regression part.

автор: Eric L

2 февр. 2016 г.

good quick overview, could have more actual R examples in lectures

автор: Ansh T

22 мар. 2020 г.

Topics like logistic regression were not explained clearly

автор: Angela W

27 нояб. 2017 г.

I learned a lot, but it was so much content for 4 weeks!

автор: Gareth S

16 июля 2017 г.

Expects a level of statistical knowledge already.

автор: David S

4 нояб. 2018 г.

needed to consult external resources extensively

автор: Lei M

23 авг. 2017 г.

Some of the materials are too much math for me.

автор: xuwei l

22 сент. 2016 г.

the lecture notes is a bit confusing

автор: Marcela Q

6 янв. 2020 г.

Terrible professor, good book

автор: Hani M

24 окт. 2017 г.

was tough

автор: Barry S

15 мар. 2016 г.

This course is the first one in the Data Science series to lapse in terms of the clarity of the lectures, and the sense of cohesiveness of the material. Brian Caffo's lectures in Statistical Inference were good; in this course they seem to veer left and right rather than get straight to the essence of whatever subject he is lecturing about.

A more structured final project would have been helpful. The instructions on this project weren't quite so blunt as to say "Take this data set, do some regression-y stuff and come back with something about these two variables," but that's basically as far as our instructions went. It could have been a great learning experience to have a more detailed guide through the construction of a regression analysis, but instead an assignment which was 40% of our grade was put together as an afterthought. It was the assignment equivalent of stopping in the 7-11 a block away from a birthday party to buy a card.

Also, in terms of delivering the content: Mr. Caffo needs to structure his slide/video arrangements so that he is not standing in front of the text. Think of it from the point of view of somebody wanting to listen and read at the same time.

автор: R. H

19 мар. 2020 г.

The timing on this course is very inaccurate - it should take much longer than 4 weeks, 6 weeks at the absolute minimum. I say this because Week 4 has so much information crammed in of all different types of General Linear Models (i.e. models that are not necessarily a straight line). Binomials, Poisson, splines - each of these topics could have their own weeks, but instead they are quickly summarized for one week with the student expect to understand them for the quiz. The other issue, which has been a problem with all courses in this specialization, is the discussion boards. They are totally abandoned by mods; good luck finding any post that isn't "grade my project? I'll grade yours!" despite a mod post that says such requests will be deleted. The board is totally flood with those requests, and makes me wonder how many people are passing these classes wrongly because "if u give me 100 i will grade yours too!" It totally devalues the program. The creators seemingly abandoning Coursera have made this certificate a waste.

автор: Kaspar M

12 окт. 2020 г.

There's some useful material in the course. There were some major issues though: 1) there is so much to cover that this really ought to be broken into two courses or more. It is not a 4-week course. It would really be helpful to break it into chunks and include some more comprehensive exercises so the learner can get a full grasp of the subject. The quizzes, particularly the final one, were curiously disconnected from the course material. The final project as assigned was just straight-out baffling. I noticed some learners submitting garbage solutions for review, presumably just so they could look at what other people were doing to figure out what they were supposed to be doing. Oh one more thing: Caffo never explains what ANOVA is, he just starts using it. Overall: I would like to know who is doing a well-designed MOOC on this, because I would like to take it.

автор: Mohamed A

2 нояб. 2016 г.

This course failed greatly to balance the workload by week. The third week which I think was the most important one have too many information to learn and assimilate whereas the first two weeks could be rearranged to start multivariate regression earlier. Another proof of week 3 issue: the related swirl exercises start in week2 (2 of them) and finish in week4 (2 more exercises) !!!!!

I think one of the most important expertise and knowledge that a data scientist must know and master was unfairly squeezed in one week leaving no time for the learner/student to do more search/exercises on the subject.

автор: Pedro J

6 июня 2016 г.

The professor doesn't explain clearly as part of the videos is his correcting himself or saying the same thing two or three times. And why must the videos show the teacher? It distracts from the slides and seeing him move doesn't help understand anything better

Concepts like VIF or hat values are not very well explained by the teacher, at least the SWIRL lesson explains it correctly. ANOVA and ANCOVA are mentioned in the description but they aren't explained anywhere. ANOVA is used without any explanation of what it is.

I found myself searching online for other sources to understand the concepts.