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Отзывы учащихся о курсе Регрессионные модели от партнера Университет Джонса Хопкинса

4.4
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Оценки: 3,185
Рецензии: 536

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

MM
12 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.

KA
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.

Фильтр по:

401–425 из 516 отзывов о курсе Регрессионные модели

автор: Ankush K

16 янв. 2018 г.

really informative with helpful examples.

автор: Mehul P

3 окт. 2017 г.

Easy way to understand Regression.

автор: David E L B

18 мая 2017 г.

Really helpful and well presented.

автор: Teppakorn

22 июня 2016 г.

Advance topic in regression model.

автор: Serg C

31 окт. 2017 г.

Not an easy one, definitely !

=)

автор: Norman B

7 февр. 2016 г.

A decent overview of regression

автор: Nicolas H

9 нояб. 2020 г.

Muy buenas herramientas!!

автор: Manpreet S

23 окт. 2019 г.

Good Course for beggining

автор: Daniiar B

27 сент. 2018 г.

Very hard to understand

автор: Prabesh S

6 мая 2016 г.

Very intuitive course

автор: Yogesh A

13 окт. 2017 г.

Good course content

автор: Vincent G

9 окт. 2017 г.

fantastic course

автор: Nevon L D

27 сент. 2018 г.

Builds Heavil

автор: Mariano F

12 июня 2016 г.

Great course.

автор: Anup K M

22 окт. 2018 г.

good content

автор: Dora M

30 мар. 2019 г.

Good class.

автор: Khairul I K

23 мар. 2017 г.

2 thumbs up

автор: Manojkumar P

8 нояб. 2016 г.

Nice Course

автор: Rohit K S

21 сент. 2020 г.

Nice one!!

автор: Johnnery A

12 февр. 2020 г.

Excellent!

автор: Mohamed A E M

3 янв. 2018 г.

Great Deal

автор: Timothy V B

19 мая 2017 г.

good intro

автор: Yuekai L

7 мар. 2016 г.

Nice.

автор: Normand D

1 февр. 2016 г.

As for the Statistical Inference course, this course is amazing but is presented in a more complex way than it should be. Once again the concepts are simple and the math not so hard, yet I had to do a lot of research outside the course to be able to understand these simple concepts and derive the not so hard mathematics.

Brian Caffo is clearly brilliant and, I would say, seem to be a good lad too, but something is missing. Too often the details are thrown at us without being properly framed in the context or without having the proper concept being introduced progressively.

I have a theory about teaching since I was 15, and so far it has proven to be true. Imagine that learning is about climbing a mountain in which tall steps have been carved. Each step is taller than the student. The teacher is somewhere higher than the students (not necessarily at the top, if there is such a thing).

The job of the teacher is to throw boxes (concepts) and balls (details) of different size, shape and colors. The job of the student is to catch these boxes and balls and to put the right balls in the right boxes in order to make a staircase out of it to climb (at least) one of the giant stair up.

A good teacher makes sure to throw the concepts first than the details and to clearly specify which balls go into which box, as well as which boxes go inside/over which other boxes.

But most teacher simply throw the balls and boxes in an not so well structured manner, so the poor students try to catch as many as he can, but also miss a lot of them. His hands can hold a limited amount of balls. If he doesn't have the right box to put them, he would either miss the next balls, or put the one he hold in his hand in the wrong box.

Bottom line, the best teachers are those who focus on the concepts (and context) and make sure that the concepts are well understood before introducing details to stuck in these concepts. From my experience our brain (or at least mine) better learn this way. It is as if our brain need first to establish a category-pattern (the concept/context) to which it will associate detail-patterns. But without a proper category-pattern, our brain is having a hard time to properly remember the detail-patterns or miss-associate them to the wrong category-pattern (which create even more confusion).

Hope it was helpful somehow...

автор: Will J

22 сент. 2019 г.

Pros: The instructors of this course are absolutely knowledgable on the content here. The content itself is challenging and applicable to real-world data science challenges. Using R makes this a good course for today's (2019) current programming world as many professional statisticians will use this language day-to-day.

Cons: The content feels mismanaged. Sometimes the Lectures don't prep you for the practice assignments, and sometimes neither of those prep you for the quizzes particularly well. I had also hoped for some more engaging video content from a course this expensive. Having a professor in his office hastily work through material while there are police sirens outside isn't exactly pro-level instruction (It is in Baltimore, so I get it).

Overall, it's worth it if you've got the time to power through relatively dull lectures. The R based practice assignments are wonderful and the final project incorporates things together nicely.