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.
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.
par Elena C
•A very intense course, where a lot of concepts are introduced. In order for all the new information to be metabolized, it took me much more than four weeks.
par Pedro C D
•Impressive! Very detailed in statistics and Mathematics, I would like an extensive course in logistic regression, it was short compared with lm course.
par Maxim M
•A very good course, goes deeply into the material. The pace of the professor is ok. It's nice that he uses some practical cases to explain the theory.
par Jorge B S
•I have loved this introductory course about Regression. The swirl exercises are especially useful to revise the course content and apply the theory.
par 20e
•Helpful!
If there is more introduction about the common problems people may encounter during working in the real world, the course will be better!
par Paul F G
•Excellent, highly focused course with current R libraries for learning various regression methods and methodology. I highly recommend this course.
par Juliusz G
•Very practical/hands-on intro to regression models. You will definitely be able to apply those methods after this course whenever you need them.
par Hernan D S P
•This course is perfect to get started with Regression Models in R! I think you would need some familiarity with the statistical concept though.
par Reza M
•Excellent course on regression modelling it showcases the power of R. quite a heavy module though for people with none statistical background
par Kumar G G
•I think this is the best course I have ever came across in the coursera. Everything is discussed in the most simple manner with great depth.
par Shivendra S
•In-depth and detailed, this one month course will provide aspirants with the knowledge and skills required to conduct efficient regressions.
par Lopamudra S
•The Regression Models is an excellent course for a beginner.I would recommend the enthusiastic students for a great start in Data science.
par Emanuele M
•It's a great course and tought very well. It required effort, you apply many of previously teach concept and requires a lot of excercise
par Abhinav G
•Very Helpful course. I am from a non -stats background and this has helped me a lot in understanding such deep concepts of Statistics.
par MEKIE Y R K
•Really interesting and full of advices.
But would like to dig more into the Logistic and poisson regression residuals explanations :)
par Matthew C
•Week 4 was a lot harder than the other weeks (specifically the quiz). Overall, a lot of great information packed into this month.
par Sandra M
•Everything you need to know to have a clear understanding of regression models and learn how to use their basic functions in R.
par Damien C
•Great ressources. Usefull presentations, maybe too rich for a newbie.
It was too fast for me. Could be done in 2x more time :/
par Richard F
•This is the most challenging course so far - new concepts, new approaches and application to a wide variety of situations.
par Connor B
•Learned a lot and enjoyed the course project. Would like to have two course projects because I gain the most out of them.
par Carlos B
•Thank you for the chance to review all the fundamental and applied mathematical and statistical aspects of data analysis.
par Stefan S
•Not the easiest course, but very rewarding if you hang in there. The material is very well explained with ample examples.
par Nino P
•Similarly to statistical inference, this is a bit harder course in the specialization. Still passable and recommendable.
par Rafael M
•Excelente curso, requiere de esfuerzo y dedicación, ademas de una solida base estadística. Práctico y de mucha utilidad.
par Vitor P B
•Very detailed and complete course with heavy theorical concepts which are all very useful for data science applications