Comentários e feedback de alunos de Modelos Regressivos da instituição Universidade Johns Hopkins

I again found many of the lectures to be difficult to follow along, there seems to be lots of different styles of videos in the way that the person was superimposed on the slides. In fact it was often impossible to read the text in the slide due to the size of the presenters head which obscured the text. Honestly this data science course is getting worse as the months progress, you really should think of updating the content of the course if you want to continue to charge money for it. 2 stars as I did actually learn something despite the quality of the material and its delivery.

To fast pace and missing lot of content to make this lesson enjoyable!!!

The instructor is not good and doesn't explain things clearly.

Should cover a lot of dfificuties when the model assumptions are violated and should be for a longer time or having a second course about this theme.

Needs more practical examples. Could be rerecorded. I love mathematical theory but past week 2 it is really too theoretical, in my opinion.

Un curso difícil de entender si no tienes la base matemática de regresión. Uno no sabe por dónde empezar, cualquiera de los cursos de esta serie (Statistical Inference, R programming...) pareciera que te saturan de información. Es bueno para curiosos con bases en R y que quieren saber más de Regresión

way to much emphasis on non-data science. This one course covers more information that the rest of the courses combined..

Overall okay course but the lectures are too focused on theory with some applications to the real world. I think this course needs to be reconfigured and taught from an applied focus instead of 30% applied 70% theory.

Also the new format is horrible and TAs are nonexistent as are discussions in general on the forums now. The TAs were a critical learning component before especially considering that unlike on EdX where course staff actually participates in the forums, on Coursera I do not think I have ever observed course staff actively participating in the forums.

With all respect, Professor, if you are reading all our comments, I think you are a really smart person and you should take all the negative feedback from your students here, positively and constructively. As having good knowledge will never be equals to able to produce good students. Personally, I feel that you should lower down yourself and speak to the level of your students/audience. Use more simple examples, draw a big picture in our minds on what is this course all about, what are we going to achieve, in each of the topics, what are we going to look at and what methods available.

For foundation class like this, I think few simple examples and introducing one or two useful methods in each topic would be more than sufficient to us. The objective should be providing us with the basic knowledge, get us interested in this subject, and able to apply those well taught basic knowledge. As when we are interested, for sure we will go and do more research, and some might even would like to move on to intermediate or advance levels.

The key point here is "speak to the level of your audience". Even if you are able to talk everything above the sky and up to the moon. If no one able to understand you, it is useless.

Coursera keeps changing my rating. Not cool.

Difficult, fast peaced and not well explained. Requires a lot of work.

Appears more like a revision for those who already know the content than geared towards those new to the subject.

I would like to propose that instead of putting the optional reading materials at the back, it should be put up front and mandatory. Else we can't follow the videos

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.

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.

Some topics that are important, are obviated

I love the content but:

imprecision (a lot),

lack of explanation

...

for one of the most difficult subject in the specialization.

Last commit/update for the video from the teacher 1/2 year ago: are the materials update?

Slightly better than the Statistical Inference course, but many of the same technical and delivery defects persist. With an otherwise high quality program, I recommend re-producing the inference and regression lectures to increase the overall value of the curriculum.

Material is too dense for the time spent engaged in class. Difficult to stay engaged with lectures, which spend a lot of time on the underlying mathematical concepts. The conceptual underpinnings are very important, but due to the limited timeframe available to present the material, the application of the concepts was done quickly, almost as an aside. The bridges from concept to practical application are very weak.

One of the most difficult courses in the whole programme. From my point of view it is very important, but not so well explained. I had to go through other training sessions in order to understand the concept based on numerous practical examples and then return to Coursera to finish it up.

not effective for new learnners

Worst teaching by Brian Caffo! typos in quizes after 4 years even. And brian has put very littel effort into making it digestable for students. Look at his lectures on youtube and I have commented at each lecture! So bad. A simple googling outside of his notes was so much more better for understanding regression!

The course is awfully simplified.

Terrible. If you want to learn about regression, even in R, go elsewhere. This course damages the brands of Johns Hopkins and Coursera...anybody heard of quality control?

Poorly designed, executed and instructed. Too much is left off the materials.