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.
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.
Mar 10, 2019
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.
por ALEXEY P•
Nov 18, 2017
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.
por George C•
Apr 30, 2018
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.
por Ricardo M•
Jan 30, 2018
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 .
por Johnny C•
Sep 25, 2018
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.
por Nicholas A•
Dec 22, 2017
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.
por Claudio F S•
Mar 11, 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!
por Joana P•
Jan 26, 2018
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.
Oct 29, 2017
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.
por Jeffrey G•
Oct 18, 2017
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.
por André C L•
Dec 13, 2018
very good practical approach, with good theoretical coverage of most important principles of regression
por David R•
Dec 05, 2018
Great course, well taught with very useful examples
por Antonio V•
Jan 09, 2019
very good course
Jan 23, 2019
Amazing course ! finally I have learned how to implement regression in real world analysis
por João F•
Feb 06, 2019
Excellent but difficult course. Complex concepts are well presented but it still requires many hours of studying. The topics taught are essential to anyone working or aspiring to work in the field of Data Science.
por Matthew S•
Feb 25, 2019
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.
por Yadder A G•
Mar 28, 2019
The course was incredible. You can learn a lot of skills about regression models and even more. It would be incredible if the course could have more examples or little excercises.
por Vibhutesh K S•
Feb 09, 2019
Nice outline for regression models.
por Sanjeev K•
Feb 02, 2019
Great Course well designed
por Bruno R d C S•
Mar 05, 2019
A deep review on linear, logistic and regression models. The critical tool for modelling.
por Seyedeh M M•
Mar 14, 2019
Awesome class! Highly recommended.
por Georgios P•
Mar 07, 2019
Great course for beginners, but definitely not for people with no mathematical background!
por Eric Y•
Dec 09, 2018
good course, nice teaching!
por Jose F V G•
Dec 23, 2018
So far so good; key concepts explained in detail.
por Javier E S•
Jul 16, 2018
Excellent course. Thank you Brian, Jeff and Roger.