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존스홉킨스대학교의 Regression Models 학습자 리뷰 및 피드백

4.4
2,775개의 평가
467개의 리뷰

강좌 소개

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....

최상위 리뷰

KA

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.

BA

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.

필터링 기준:

Regression Models의 447개 리뷰 중 151~175

교육 기관: 李俊宏

Sep 04, 2017

I like Brian Caffo's lectures. He is genius and calm

교육 기관: Tine M

May 11, 2018

Definitely a difficult course but a very interesting one.

교육 기관: Larry G

Feb 07, 2017

Nice

교육 기관: Felipe L

Jan 23, 2016

Excellent introductory course!

교육 기관: Noelia O F

Jul 19, 2016

Great course!

교육 기관: Jared P

Apr 10, 2017

With the first few videos, I was concerned I would be re-living the nightmare that was the Statistical Inference course. (I gave a long review of that one. To summarize Statistical Inference: I hated it. But I learned things. Those things stuck. I used them in real life. That's good.)

But wow, after getting through this course, I loved it. Very practical and useful stuff. It had me thirsting for more information and I found myself reading unassigned material. I became particularly interested in Anova and continuing to read up on it even though I am done the course.

I would take this course again. I would recommend it to those wanting to learn more about data science. It's got some quirks and room for improvement, but overall it's a good course.

교육 기관: Carlos

Feb 25, 2016

This class, along with "Statistical Inference" and "Machine Language" , are the meat and potato's for data science. I had taken most, if not all of these classes as an undergrad many years ago . The tools for stats have changed significantly and these classes being taught with the open source R language, really put you at the forefront of this new field.

교육 기관: Kevin

Jul 08, 2016

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.

교육 기관: Paula L

Dec 02, 2016

good review of the fundamentals

교육 기관: Kpakpo S M

Jul 26, 2017

Perfect course toward the data science specialization. It gives good understanding and improve my knowledge of inference statistic. I have the opportunity to explore all the plotting concept and apply them in regression models arena.Good to take this course to step in the concept of machine learning.

교육 기관: Sergio R

Jul 30, 2017

Thank you! it is a very enrichment course.

교육 기관: Sandhya A

Jun 02, 2018

Learned a lot about various regression model, concept like fitting and overfitting

교육 기관: João P S

Nov 03, 2016

Very good course

교육 기관: Ivana L

Jan 30, 2016

One of the most valuable course in series. Also one of the hardest, expecially if you are newbie to regression models.

교육 기관: Carlos M B B

Jun 20, 2017

Thank you for the chance to review all the fundamental and applied mathematical and statistical aspects of data analysis.

교육 기관: Ilan L

Jul 10, 2016

very usful

교육 기관: Conner M

Apr 05, 2018

Caffo is excellent!

교육 기관: E. M

May 12, 2016

Very relevant!

교육 기관: Daniel C J

Aug 02, 2017

Great introductory course on Regression Models. Super practical and well explained. Definitely doing the exercises and final project is a must to get all the learnings!

교육 기관: Juliusz G

Nov 21, 2016

Very practical/hands-on intro to regression models. You will definitely be able to apply those methods after this course whenever you need them.

교육 기관: Jose A R N

Nov 06, 2016

My name is Jose Antonio from Brazil. I am looking for a new Data Scientist career (https://www.linkedin.com/in/joseantonio11)

I did this course to get new knowledge about Data Science and better understand the technology and your practical applications.

The course was excellent and the classes well taught by teachers.

Congratulations to Coursera team and Instructors

교육 기관: Chris

Nov 02, 2016

great introduction to regression models

교육 기관: Arcenis R

Jan 18, 2016

This course is packed with great lessons and Prof. Caffo puts it all together very cogently.

교육 기관: Manuel X D

Feb 09, 2016

Data, our “raw” material, becomes plentiful. Let’s learn form it.

Thanks to constant progress in information technologies, this increasing production of data is an outstanding opportunity to improve our knowledge of subject matters we care about, e.g. environment, health, markets…

Properly analyzing these data in the scope of addressing specific questions is not trivial. But it can be learn. And if there were one place where one could acquire these skills and become anxious to grow in that field, this would be the Coursera Regression Models course. Data analysts, like any professionals, need her/his set of tools. Good tools make good patricians. The Coursera Data Science Specialization that includes this Regression Models class is where one can learn how to use the right tools and reduce them into practice. Passionate instructors who obviously take great care in communicating effectively the knowledge they master teach these courses admirably. Highly recommended course and specialization,

There are so many unanswered questions, so many new relationships to uncover. Learn how.

교육 기관: Christian H

Aug 23, 2017

Great course; practical introduction to regression models at the university level.