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Отзывы учащихся о курсе Регрессионные модели от партнера Университет Джонса Хопкинса

4.4
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Оценки: 2,975
Рецензии: 502

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

KA

Dec 17, 2017

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.

BA

Feb 01, 2017

It really helped me to have a better understanding of these Regression Models. However, I've noticed that there is a video recording repeated: Week 3, Model Selection. Part 3 is included in Part 2.

Фильтр по:

426–450 из 482 отзывов о курсе Регрессионные модели

автор: Jason M C

Mar 29, 2016

This is a decent class, covering linear regression and a few of its variants in good detail. It's a challenging subject, but presented acceptably here.

автор: Anamaria A

Mar 12, 2017

Lots of material needs additional study (from different sources) as it's only summarily explained. Much math without the link to the praxis :-(

автор: Manuel M M

Feb 10, 2020

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

Sep 26, 2018

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

автор: Hendrik F

Jan 17, 2016

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

Apr 24, 2018

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

автор: Mertz

Mar 20, 2018

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

автор: Andres C S

Mar 02, 2016

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

автор: Erwin V

Dec 20, 2016

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

автор: Prabeeti B

Sep 17, 2019

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

автор: Praveen J

Apr 22, 2020

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

автор: Suleman W

Nov 10, 2017

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

автор: Rafal K

Feb 28, 2017

Many things are not clear enough in multivariable regression part.

автор: Eric L

Feb 03, 2016

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

автор: Ansh T

Mar 22, 2020

Topics like logistic regression were not explained clearly

автор: Angela W

Nov 27, 2017

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

автор: Gareth S

Jul 16, 2017

Expects a level of statistical knowledge already.

автор: David S

Nov 05, 2018

needed to consult external resources extensively

автор: Lei M

Aug 23, 2017

Some of the materials are too much math for me.

автор: xuwei l

Sep 22, 2016

the lecture notes is a bit confusing

автор: Marcela Q

Jan 06, 2020

Terrible professor, good book

автор: Hani M

Oct 24, 2017

was tough

автор: Barry S

Mar 15, 2016

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

Mar 19, 2020

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: Mohamed A

Nov 02, 2016

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.