Dec 16, 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.
Mar 10, 2019
This module was the maximum. I learned how powerful the use of Regression Models techniques in Data Science analysis is. I thank Professor Brian Caffo for sharing his knowledge with us. Thank you!
By David S•
Nov 4, 2018
needed to consult external resources extensively
By Lei M•
Aug 23, 2017
Some of the materials are too much math for me.
By xuwei. l•
Sep 22, 2016
the lecture notes is a bit confusing
By Marcela Q•
Jan 6, 2020
Terrible professor, good book
By Hani M•
Oct 24, 2017
By 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.
By 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.
By Renata G•
Mar 28, 2021
I came from
Statistical Inference and I felt very sorry when I see the same instructor here at this course.
Regression Models is a very important subject and I am very interested in real learning. However, this course was very, very, very disappointing to me. I am deep sorry for my sincerity... but this was not as high-quality course. Total lack of any support from the TA/instructor team is frustrating. Wikipedia, youtube videos, books... were much more helpful and effective for me. I believe the real issue lay in the teaching style. Brian seemed a very intelligent person, but he does not teach well. His way of explaining things was really bad: he speaks too fast (sometimes he changes terms...), he skips from slide to slide very quickly, he often do not provide adequate explanations. He also does not approach realistic cases to apply Regression Models in day a day basis. His book is only a copy of his teaching slides. I certainly do not recommend this course.
Oct 12, 2020
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.
By Mohamed A•
Nov 2, 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.
By Pedro J•
Jun 6, 2016
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.
By Lee D•
Sep 29, 2016
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.
By B C•
Mar 1, 2016
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.
By Deleted A•
Sep 1, 2017
The concepts behind this course are really important. However, I feel that the material is not up to the needed level.
I am missing a good solid material that explains properly the theory behind these methods. I had to revert to other books (that could have well showed up as references in the course material) to get a proper understanding.
May 13, 2019
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!
By Daniel M•
Jan 20, 2016
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
By Siddharth T S•
Oct 5, 2020
Both the video lectures and the book coast through some important topics that they should have spent more time explaining. The homework exercises and quizzes are definitely useful, but the subpar teaching efforts meant that I had to refer to outside sources for understanding the key concepts.
By Jing Z•
Feb 8, 2016
I just realized that you have to upgrade(pay $49) in order to submit the quiz and receive the feedback. That's depressing since my purpose is to watch the video and check out what I learned so far without getting any certificate. The policy here bring huge inconvenience for people like me.
By Grigory S•
Aug 20, 2018
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.
By Andrew L•
Aug 11, 2022
Poor explanations throughout. You'd be best served by learning the topics from a secondary source and then coming back to decipher the lectures and videos. Needs many more examples, this course is instead full of an equation, a simulation, and a hearty "good luck!"
By Stefano G•
Jul 20, 2017
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?
By Coral P•
Jul 20, 2017
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
By Jorge P•
Jun 7, 2016
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.
By João R•
Aug 20, 2017
Needs more practical examples. Could be rerecorded. I love mathematical theory but past week 2 it is really too theoretical, in my opinion.
Feb 12, 2016
way to much emphasis on non-data science. This one course covers more information that the rest of the courses combined..