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Opiniones y comentarios de aprendices correspondientes a modelos de regresión por parte de Universidad Johns Hopkins

4.4
estrellas
3,298 calificaciones
567 reseña

Acerca del Curso

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing....

Principales reseñas

KA

16 de dic. de 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.

DA

10 de mar. de 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!

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351 - 375 de 548 revisiones para modelos de regresión

por Miguel C

3 de may. de 2020

I really enjoyed the course. Even though I had already learned linear regression and logistic regression from a computer science perspective, I still learned a lot since the course approaches these subjects from a statistical view. The content was interesting and challenging, so I am really glad that I took this course.

por Max M

19 de nov. de 2017

Really appreciate the depth of this course, as well as the changes Prof. Caffo made in his teaching style since his Statistical Inference course. However, the reasoning behind some of the more complex topics, like GLMs, aren't adequately explained, and the Swirl lessons are presented in a strange and disorienting order.

por Cesar L

1 de nov. de 2016

Great course. The content might be improved to be more clear. I feel that sometimes the instructor assumes we are familiar with some concepts we have not seen in previous courses. Also, some times he does a very good job explaining the WHY before the HOW, and some other times he does not. Very knowledgeable instructor.

por Kevin H

9 de nov. de 2016

Something was missing from this course. I cam away with an increased understanding of regression but I still feel like I struggle with many concepts and had to put in much more time than the recommended.

Still when I found the answer it was all still contained and maybe the material itself is just advanced.

my 2c

por Jeremy J

12 de nov. de 2016

Like the way the Prof uses media. This is a very light touch on a very deep subject so it has to balance analytical work with the light "trust me and just do it" approach. The balance was mostly there although on a couple items I don't know that I had a good enough grip to know what I don't know.

por Wei W

23 de oct. de 2017

Brian did better job in this course to elaborate and demonstrate with examples. No doubt Brian is extremely knowledge about this subject. Once again, this and Statistical Inference courses are very challenging to truly completed with insightful understanding. That's why I take one star away.

por Vidya M S

14 de mar. de 2017

The concepts are well explained and precise. I think it depends on the individual to dive deeper into the topic by independent learning. Good data examples. Also following the suggested book of the author helps with some extra excercises. However , I feel extra practice questions would help .

por Linda W

3 de jun. de 2016

This course will give you a good basic foundation in regression models. However, do be prepared to do a good amount of work besides just viewing the videos. I would recommend at the very least to go through the exercises in the 'recommended textbook' to gain a better understanding.

por Kim K

8 de ago. de 2018

You will need to know the subject before taking this class in order to understand or be able to put in a large amount of time to learn. The book "Introduction to Statistical Learning" is an excellent supplement to the course. Rigorous and rewarding when you put the work in.

por Scot M

31 de mar. de 2021

The course materials are engaging and thorough and the quizzes are at a reasonable level of difficulty. The concluding course project instructions are unclear and it takes some creative thinking to understand how to fully relate these to the course content.

por Ada

14 de nov. de 2016

Regression models was almost just as difficult as statistical inference. Again, the swirls and exercises were of great help. The pace, as always, was quite fast, but in the end all the pieces fitted together. Congratulations on a job well done!

por Peter G

10 de feb. de 2016

First 3 weeks give very reasonable overview of the subject - topics of linear / polynomial / multivariate regression are covered quite well.

Week 4 is a bit sloppy and ad-hoc, comparing to first 3 weeks - GLMs are given poorly.

por Utkarsh Y

28 de sep. de 2016

It is a good course for learning regression model implementation in R. You may need to have a basic understanding of popular regression models like linear & logistic as the course doesn't cover mathematical aspects in detail

por Tim M

5 de oct. de 2020

The course is informative & well taught. I would have liked to spend more time on GLM models, such as logistic regression. The Swirl assignments seem a bit outdated method of learning code and a bit of a hassle.

por Jim M

23 de jul. de 2020

Content is excellent and in depth. Structure could be better to present materials in a more organized fashion, particularly on how all the concepts and tools relate, and complex results interpretation.

por Andrew W

20 de feb. de 2018

Great subject, was a bit frustrated with some of the material (seemed rushed and not well prepared). Great assignment, but too restrictive on the max number of pages allowed. Wasted a lot of time.

por Diego C

4 de may. de 2019

Very good course. Though basic, it provides you with the first tools and knowledge. The forums aren't what they used to be it seems, but you can find almost any answer there from past courses.

por Andrew W

5 de abr. de 2018

Very good at presenting basic concepts. I highly reccomend saving the quiz questions as a good guide as to what you should know. I wish there were more material on generalized linear models.

por Arturo M K

10 de dic. de 2016

I was hoping to learn about PROBIT models. I know they are very similar to LOGIT ones, but still... the pace is a little bit too fast and I think it requires more time than what it says.

por Bill K

10 de feb. de 2016

This was a tough class covering a lot of material. The last week on logistic regression completely lost me. If you're new to stats like me you might want to take it more than once.

por Manny R

22 de mar. de 2019

Really Fun Course. There is a lot to learn in this topic and this could be studied for a lifetime. I feel like I could apply this to discover solutions for issues at work.

por Vlad

20 de abr. de 2018

Good course, worth taking. It points out the importance of looking deeper into the world of regression models and creates right mindset and anchors for future development.

por Samirou T

26 de may. de 2018

I appreciate coefficients interpretation and variance influence to choose among models.

Running code takes a few seconds, understanding the model's outputs is a much hard

por David J B

19 de feb. de 2019

Probably the most conceptually challenging and practically useful course in the JH data science certification series (so far... I have a few more courses to complete).

por Fernando L B d M

29 de sep. de 2017

This time the professor Brian Caffo was more helpfull, explained better the concepts, and sometimes repeated some of the most important information... Good course!