If you thought that the previous course (Statistical Inference) with Brian Caffo was a horrible experience -- think twice and get ready for Regression Models. It is way worse. Imagine an instructor starting his explanation by showing you some (rather involved) formula and immediately jumping to the discussion of the various terms without actually telling you clearly what this formula is for and how to use it. Then you will get a pretty good idea about the instructor for this course. He is a horrible teacher, who clearly does not understand what teaching is and how it should be done properly. Total waste of time.

Really bad. Worst of the whole Data Science spezialization. Bored me to death. Lecture had nothing to do with the quizzes, quizzes had nothing to do with the final assignment, final assignment had nothing to do with the lectures. Fight through it, there is light at the end of the tunnel.

This is the worst course in the series. Caffo does a terrible job at explaining regression, the final assignment requirements aren't properly addressed, and it appears they didn't quite spend time on how to make it all work (2 pages to test out different regression models, make an inference, and everything else is absurd). I highly recommend avoid this course, and instead go through the R guide on linear regression; in the end, I used those to get through this course.

Just like the previous course in the specialization path (Statistical Inference) the course delves into some relevant topics however it doesn't feel as properly structured. While on the first week the lectures seem to try to give a basic and comprehensible learning of linear regression, once we start into the more advanced topics it gets confusing.

Lots of formulas and concepts thrown at you without much clarification. For someone without any knowledge/background on statistics this can be quite difficult to grasp the concepts.

The module for Poisson Regression is very poor in terms of information. just feels like a very light overview of the matter.

The course should be reviewed or at least the indication of "Beginner Specialization.No prior experience required." should be updated to mention that some knowledge in statistics is recommended .

One video is wrongly edited, half of it is repeated. The instructor gives too much information and is difficult to follow, some information is even trivial.

Personally, I am not a fan of this professor. He over-explains all of the topics, just to tell you at the end of the lecture that you don't need to know the specifics and can do it all with one function. He is very unengaging, difficult to follow, and rushes through lectures. And finally, HE BLOCKS THE SLIDES WITH HIS HEAD SO YOU CAN'T SEE THE NOTES. I feel like out of all the professors in this specialization course, there were so many others who could have taught the material better, especially since this is probably the most important course of the entire specialization. I feel like I only began to understand the material once I finished the course project, and even then I have no idea how regression models work.

I'm now going to be taking a month or 2 off from the courses to read more about statistical inference and regression models on my own, since I feel completely unprepared for the upcoming Machine Learning course.

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!

Honestly the materials of this course are really confusing. So many focus on the mathematical value instead of real examples and scenarios to use the concepts reached. Also it would benefit if there was a clear message coming through, like Machine Learning course where things follow a order.

If it was not by the book of Mr.Field with Statistics in r, I would never be able to understand what was really being said in this course. Or what was the best strategy to effectively do a proper regression analysis and what would be the best models.

I was optimistic about this class because it started out fixing some of the pedagogical mistakes the professor made in Statistical Inference, but by the time we got to week 3, it was pretty clear that the course was trying to accomplish too much in 4 weeks, and instead of focusing on the most important parts of regression and making sure they were taught well and understood clearly, I feel the course tried to do far too much. The only reason I gave it two stars instead of one star was the course project was relatable - choosing the best transmission for maximizing mpg is a real-world problem that I can (and did) have a discussion with my mother about. Too many assignments are about something completely inane, like guinea pig teeth or flower petals. If you're going to inspire students to learn the material, the examples (and data) must be relatable to them.

Very math heavy and not super useful for psychology students.

Without a tutor, that I had to pay $30 an hour in addition to this course, I would not have passed.

The layout was rather convoluted, there were several spelling mistakes (one that completely changed the meaning of a QUIZ question) and it was not as conceptual as I was hoping for.

The conceptual limitation is big for me as I don't care about the math, I'm a psych undergrad trying to learn statistics for my honors thesis, not a math course.

It also made it difficult to apply what we learned since the data we worked with wasn't that easy to understand and was incredibly boring (car mpg data and insect sprays??).

I'm also slightly upset that coursera signed me up for a subscription when all I wanted was one course, very cheeky.

Data, our “raw” material, becomes plentiful. Let’s learn form it.

Thanks to constant progress in information technologies, this increasing production of data is an outstanding opportunity to improve our knowledge of subject matters we care about, e.g. environment, health, markets…

Properly analyzing these data in the scope of addressing specific questions is not trivial. But it can be learn. And if there were one place where one could acquire these skills and become anxious to grow in that field, this would be the Coursera Regression Models course. Data analysts, like any professionals, need her/his set of tools. Good tools make good patricians. The Coursera Data Science Specialization that includes this Regression Models class is where one can learn how to use the right tools and reduce them into practice. Passionate instructors who obviously take great care in communicating effectively the knowledge they master teach these courses admirably. Highly recommended course and specialization,

There are so many unanswered questions, so many new relationships to uncover. Learn how.

With the first few videos, I was concerned I would be re-living the nightmare that was the Statistical Inference course. (I gave a long review of that one. To summarize Statistical Inference: I hated it. But I learned things. Those things stuck. I used them in real life. That's good.)

But wow, after getting through this course, I loved it. Very practical and useful stuff. It had me thirsting for more information and I found myself reading unassigned material. I became particularly interested in Anova and continuing to read up on it even though I am done the course.

I would take this course again. I would recommend it to those wanting to learn more about data science. It's got some quirks and room for improvement, but overall it's a good course.

I found this part of the course one of the hardest. But at the same time, probably the best course about linear regression I have ever seen. Is it difficult? Yes! Is it super exciting? Well... :) not necessarily. But I have come back to these course materials many times for a good reason. It's what you need to understand and use all the time. It is the absolute essential and necessary to know for any data scientist. While it might appear to be boring and basic compared to fancy deep learning models... trust me. It's not. It goes a long way in understanding what can be done with data. I am very grateful to the course instructors that they have spent their time and effort to make this course what it is. Please keep up the good work!

I like this course a lot because it solidified my understanding of regression. I have often read about regression when reading scientific articles however, i never took the time to really investigate the mechanics on how it is done. Thanks to this course i can now appreciate better the journals that i read. Furthermore, the course project for this course was quite interesting, not too hard, and was bit challenging. There was plenty of time to finish the project and some extra time to make it even better than a simple submission that meets the basic requirements of the course. Thanks to the my classmates and the nice people in Coursera and R, i had a great time learning during this course.

This course is great, if you want to get into it. This was the first time I have been exposed to linear and generalized linear regression. I was overwhelmed by so much information and knowledge that I needed extra time to understand and to bring the pieces from week 1 to week 4 together.

The peer reviews weren't (in my case) not very helpful. I missed concrete feedback on my approach and the result. I would have appreciated some kind of assurance that the achieved results are of quality and on the level of data scientist.

Excellent course. The instructor is very knowledgeable and covers the most important aspects of regression models. I found myself relying a lot on the text book; unfortunately it contains a lot of typos but its short and easy to follow. The final course project is very open-ended in the sense that its up to the student to make his/her own analysis of the data. A lot of students complain about it but i thought this was great, as it allowed me to push myself to understand the subject better.

This course gives a very thorough and rigorous treatment to the topic of regression models.

It teaches you how to derive from the ground, how regression models are made and how to interpret every information available through regression models.

Although the lectures are very lengthy and dry, the course offers a very rich well of information that is not readily available else where.

Thanks to Brian Caffo for the wealth of information about regression models taught through this course!

This was great. However, to follow it more precisely, you need the following: Read the book of linear regression from the same teacher. Usually a useful strategy would be to read each chapter first from the book, then watch the video associated to it, and finally do the swirl exercice.

You may need to follow the course notes of this class, they are published in github, and they can help a lot, especially for the quizzes.

This course was a great as an intro to regression models, material was good but needs some update on the links, for the structure of topics it would be better if it was more coherent as many topics were covered randomly in different weeks like residuals.

Thanks for the instructor Brian Caffo for the good material and and clarification of concepts for a better understanding for students.

My name is Jose Antonio from Brazil. I am looking for a new Data Scientist career (https://www.linkedin.com/in/joseantonio11)

I did this course to get new knowledge about Data Science and better understand the technology and your practical applications.

The course was excellent and the classes well taught by teachers.

Congratulations to Coursera team and Instructors

This class, along with "Statistical Inference" and "Machine Language" , are the meat and potato's for data science. I had taken most, if not all of these classes as an undergrad many years ago . The tools for stats have changed significantly and these classes being taught with the open source R language, really put you at the forefront of this new field.

The level of this course is a lot higher than the other courses. The course contains a lot of material and exercises which makes it hard to finish the course within the time period of one month. Nevertheless did I like Brian's way of teaching. He's a perfectionist and takes his time to explain everything in detail. I really liked the challenge, 5 stars.

I felt I had to do a lot of investigation and research into the course topics on my own.... the material is not fed to you spoonful by spoonful. But coming at it this way, I learned a lot. The more effort you invest in this course, the bigger the payoff. The knowledge gained in this course has tremendous value in the data science workplace.

Challenging but highly rewarding course. Prof. Caffo does an excellent job presenting the material in a way that does not require previous background or expertise. The lectures were thorough and the Swirl exercises were very useful. I think the best part of this class is that it truly highlights how powerful and important regression is.

Perfect course toward the data science specialization. It gives good understanding and improve my knowledge of inference statistic. I have the opportunity to explore all the plotting concept and apply them in regression models arena.Good to take this course to step in the concept of machine learning.