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 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.

I give this 5 stars as the material taught is essential for a Data Scientist, and was presented well. I already work as a Data Scientist but wanted to do more Data Science courses to ensure my knowledge was sound ( I studied something else when younger: Engineering and Applied Mathematics). I don't recommend someone with a beginner understanding of mathematics to necessarily do this course; at minimum an intermediate level would be required. However, for those with the right background knowledge this is a good course, and a great way to increase confidence when coding in R.

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!

As a professor in orthodontics, it was great fun to listen to all the lectures, solve quizzes and prepare the project. Before hearing from Galton's research, I had a question by myself "Can human jaw grow extremely when people with protruded jaws continue to give birth to the same trait offsprings?" Now, I can answer that human jaws won't grow over to the particular limit due to "Regression to the mean."

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.

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.

This review is bias as i really like the Brian's teaching style :) What i also like about this course is that dives in equal parts in theory and practice. Also has a great book that can be used as an external follow along resource. Really good to get to terms with regression inference. Recommend.

I definitely had to go beyond the lectures to be able to understand the quiz questions, but that is what I expected. Being 50 years out of school, this has been a great experience for me. I do recommend two books that helped me a lot: The Book of R, and R In Action. Filled in a lot of gaps.

Fantastic course. Brian Caffo is an excellent professor. During my professional life i have worked a lot with regressions, but this course open my mind and gave lots of ideas and different perspectives about that matter. I truly recommend other people to take this course.

It was a wonderful course for regression models, the full import of which I realized when I took up the next course on machine learning. The concepts learned here enhanced by confidence to venture into more advanced machine learning. A highly recommended specialization.

I loved studying Regression Models taught by Prof. Brian Caffo. I think these are very important techniques that I will be able to use for my research and analysis.

I found the teaching to be very in depth in explaining various aspects of regression model development.

I really enjoyed this course. I think the toughest for a newbie like me was the second course R programming. But the following courses including this one flow very well and are easy to follow with real life examples. It does get easier after the second course

As good as it could be given the limited amount of time. I have done some coursework on regression models before, but in my opinion the course could not have shown anything more without delving into technicalities. I would recommend it to anyone interested!

Love the whole course approach on the importance of linear models and how one should interpret them to get a better grasp of the data one possesses - one should definitely take the statistical inference course before attempting this course beforehand.

Awesome course. Handling a complex topic in a very lucid manner. However, be prepared of finishing in more than 1 class because it will really take time to grasp the concepts especially if you are not from statistical background. Great job Brian.

Good foundation in the Data Science Certification for Practical Machine Learning. There are 3 areas that I would like to dig deeper so far: Statistical Inference, Regression Models and Practical Machine Learning (perhaps + Deep Learning).

This is a fantastic course for new learners of regression models. I have seen so many courses which charges more money but the content and rich knowledge JHU has shared here is great. I highly recommend new people for this course

If this was an on-campus course, I would have been a little worried about the quiz grades on the 1st try. However, with the ability to re-take this quizzes, I think this was an Excellent and well thought-out course.