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Отзывы учащихся о курсе Регрессионные модели от партнера Университет Джонса Хопкинса

Оценки: 3,307

О курсе

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....

Лучшие рецензии


16 дек. 2017 г.

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.


10 мар. 2019 г.

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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51–75 из 549 отзывов о курсе Регрессионные модели

автор: Christian B

27 мая 2017 г.

One of the better classes of the specialization. I found the quizzes quizzes (in particular week 3 and 4) quite challenging. I took the ML class before, which I do not recommend. Take this class before the ML class.

автор: João F

6 февр. 2019 г.

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.


8 июля 2016 г.

Very concise and good structured course. The new videos are much better than the old ones! Thank you Brian Caffo! However in the discussion forum you find less posts than in the previous format, which is a pitty.

автор: Sai S

9 июля 2017 г.

Thanks much. Good course. Would have loved a tougher final project (eg. using logistic regression). How about adding two variants for all final projects - 1. lots of things to do vs. 2. more technically complex ?

автор: Marco C

24 апр. 2018 г.

I studied Regression Models in other courses, but only now I feel I'm in the matter. Thanks to the Instructor for the really good explanation and especially for the ability to convey the passion for Statistics.

автор: Mikhail M

10 сент. 2016 г.

Extremely useful and exciting. Everything from previous modules fall in places and you may see some practical implementations from the course. By the way R is awesome!

Many thanks to faculty, you do a great job.

автор: Samer A

10 июля 2018 г.

Great Course. Brian Caffo has a way to explain regression without sinking deep into hard math. You obviously need to walk the extra mile and search for yourself, but the course definitely gives you the map.

автор: Massimo M

13 мар. 2018 г.

Great course, very informative, with lots of valuable information and examples. Prof. Caffo and his team did a very good job in my opinion. I've found very useful the course material shared on github.

автор: Kristin A

17 дек. 2017 г.

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.

автор: Keidzh S

3 июля 2018 г.

Strong and effective course. Completely makes better my math skilss. Thank you Brian Caffo and other masters for this course. Looking forward to start tne next course from John Hopkins University.

автор: Alexis C

11 авг. 2017 г.

Regression analysis is something that is kind of easy for people to understand (outcome and predictor - people get that!). It's easy to explain to people. So much practice using the lm function!

автор: Ivan Y

14 февр. 2018 г.

I learned a lot through this course! It's not easy, and there's a lot of technical details that required me to watch the videos 2-3 times through to have a proper grasp, but super helpful stuff!

автор: Snehangshu R

4 янв. 2022 г.

One Star for the Video Lecture, One star for the free E-book, one star for the swirl lesson and two star for the video solutions of the exercises from the ebook (posted in youtube). Thank you.

автор: Alán G

28 мая 2019 г.

It is an excellent initial approach to Regression Models. I was able to apply some of the models in my work. Further analysis of the mathematical and statistical theory is highly recommended.

автор: Camilla J

4 янв. 2018 г.

The best course in my mind, but I am chocked about how Data Science people approach regression type of problems, it is almost 100% data mining and no theory!! I wonder where it will take us..

автор: Lowell R

7 окт. 2016 г.

Excellent overview of a very broad and complex topic with plenty of useful applications within R. The course project does an outstanding job at teaching the pitfalls of omitted variable bias.

автор: Gabriel V O G

26 апр. 2021 г.

I have been involved with regression models for a long time.

I was amazed on the capabilities that have been developed in R. I think that an open Source software is the way to build knowledge

автор: sanjeev i

29 февр. 2016 г.

The course content was very brief and well structured, Regression being a rather vast topic demands a lot more time. 4 weeks seemed a bit less! Overall satisfied by what the course offered.

автор: Alex B

28 июля 2021 г.

I liked this because I have almost no background on this sort of thing and it forced me to go waaay back and revisit and deepen my knowledge of modeling and statistics as well. I loved it.

автор: manuel s g

7 дек. 2021 г.

This was one of the hardest for me (considering I am not a math person). Very well explained and please do a lot of practice and try/invent other excercises to validate your knowledge

автор: Vinicio D S

23 апр. 2018 г.

Great course to get the basics on Linear Models and Inference. Great Introduction to Logistic Regression and Poisson Regression. Good emphasis in Diagnostics of the main assumptions

автор: Steven C

15 мар. 2017 г.

Good course on the theories behind regression, followed by significant applications and how to use them in R. Lectures are very dry, but the information within them is very useful.

автор: Boris K

29 окт. 2019 г.

Along with the Statistical Inference Class and Building Predictive Models Class this is one of the best in this Specialization. It is reasonably tough, well-taught, overall great.

автор: Yadder A

27 мар. 2019 г.

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.

автор: Juan V

16 окт. 2017 г.

It is very interesting, however is difficult to follow the math explanations, it could be more easy with practical examples.... like the final assignment, it was difficult to me.