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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....

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.

BA

31 янв. 2017 г.

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.

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автор: ALEXEY P

•18 нояб. 2017 г.

If you thought that the previous course (Statistical Inference) with Brian Caffo was a horrible experience -- think twice and get ready for Regression Models. It is way worse. Imagine an instructor starting his explanation by showing you some (rather involved) formula and immediately jumping to the discussion of the various terms without actually telling you clearly what this formula is for and how to use it. Then you will get a pretty good idea about the instructor for this course. He is a horrible teacher, who clearly does not understand what teaching is and how it should be done properly. Total waste of time.

автор: Roman

•10 мар. 2019 г.

Really bad. Worst of the whole Data Science spezialization. Bored me to death. Lecture had nothing to do with the quizzes, quizzes had nothing to do with the final assignment, final assignment had nothing to do with the lectures. Fight through it, there is light at the end of the tunnel.

автор: Nikolai A

•22 дек. 2017 г.

Personally, I am not a fan of this professor. He over-explains all of the topics, just to tell you at the end of the lecture that you don't need to know the specifics and can do it all with one function. He is very unengaging, difficult to follow, and rushes through lectures. And finally, HE BLOCKS THE SLIDES WITH HIS HEAD SO YOU CAN'T SEE THE NOTES. I feel like out of all the professors in this specialization course, there were so many others who could have taught the material better, especially since this is probably the most important course of the entire specialization. I feel like I only began to understand the material once I finished the course project, and even then I have no idea how regression models work.

I'm now going to be taking a month or 2 off from the courses to read more about statistical inference and regression models on my own, since I feel completely unprepared for the upcoming Machine Learning course.

автор: George C

•30 апр. 2018 г.

This is the worst course in the series. Caffo does a terrible job at explaining regression, the final assignment requirements aren't properly addressed, and it appears they didn't quite spend time on how to make it all work (2 pages to test out different regression models, make an inference, and everything else is absurd). I highly recommend avoid this course, and instead go through the R guide on linear regression; in the end, I used those to get through this course.

автор: Ricardo M

•30 янв. 2018 г.

Just like the previous course in the specialization path (Statistical Inference) the course delves into some relevant topics however it doesn't feel as properly structured. While on the first week the lectures seem to try to give a basic and comprehensible learning of linear regression, once we start into the more advanced topics it gets confusing.

Lots of formulas and concepts thrown at you without much clarification. For someone without any knowledge/background on statistics this can be quite difficult to grasp the concepts.

The module for Poisson Regression is very poor in terms of information. just feels like a very light overview of the matter.

The course should be reviewed or at least the indication of "Beginner Specialization.No prior experience required." should be updated to mention that some knowledge in statistics is recommended .

автор: Johnny C

•25 сент. 2018 г.

One video is wrongly edited, half of it is repeated. The instructor gives too much information and is difficult to follow, some information is even trivial.

автор: cleoag1

•29 окт. 2017 г.

Very math heavy and not super useful for psychology students.

Without a tutor, that I had to pay $30 an hour in addition to this course, I would not have passed.

The layout was rather convoluted, there were several spelling mistakes (one that completely changed the meaning of a QUIZ question) and it was not as conceptual as I was hoping for.

The conceptual limitation is big for me as I don't care about the math, I'm a psych undergrad trying to learn statistics for my honors thesis, not a math course.

It also made it difficult to apply what we learned since the data we worked with wasn't that easy to understand and was incredibly boring (car mpg data and insect sprays??).

I'm also slightly upset that coursera signed me up for a subscription when all I wanted was one course, very cheeky.

автор: Deleted A

•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!

автор: Jeffrey G

•18 окт. 2017 г.

I was optimistic about this class because it started out fixing some of the pedagogical mistakes the professor made in Statistical Inference, but by the time we got to week 3, it was pretty clear that the course was trying to accomplish too much in 4 weeks, and instead of focusing on the most important parts of regression and making sure they were taught well and understood clearly, I feel the course tried to do far too much. The only reason I gave it two stars instead of one star was the course project was relatable - choosing the best transmission for maximizing mpg is a real-world problem that I can (and did) have a discussion with my mother about. Too many assignments are about something completely inane, like guinea pig teeth or flower petals. If you're going to inspire students to learn the material, the examples (and data) must be relatable to them.

автор: Joana P

•26 янв. 2018 г.

Honestly the materials of this course are really confusing. So many focus on the mathematical value instead of real examples and scenarios to use the concepts reached. Also it would benefit if there was a clear message coming through, like Machine Learning course where things follow a order.

If it was not by the book of Mr.Field with Statistics in r, I would never be able to understand what was really being said in this course. Or what was the best strategy to effectively do a proper regression analysis and what would be the best models.

автор: Liew W P

•29 авг. 2016 г.

With all respect, Professor, if you are reading all our comments, I think you are a really smart person and you should take all the negative feedback from your students here, positively and constructively. As having good knowledge will never be equals to able to produce good students. Personally, I feel that you should lower down yourself and speak to the level of your students/audience. Use more simple examples, draw a big picture in our minds on what is this course all about, what are we going to achieve, in each of the topics, what are we going to look at and what methods available.

For foundation class like this, I think few simple examples and introducing one or two useful methods in each topic would be more than sufficient to us. The objective should be providing us with the basic knowledge, get us interested in this subject, and able to apply those well taught basic knowledge. As when we are interested, for sure we will go and do more research, and some might even would like to move on to intermediate or advance levels.

The key point here is "speak to the level of your audience". Even if you are able to talk everything above the sky and up to the moon. If no one able to understand you, it is useless.

автор: Steffen R

•6 окт. 2018 г.

I found this part of the course one of the hardest. But at the same time, probably the best course about linear regression I have ever seen. Is it difficult? Yes! Is it super exciting? Well... :) not necessarily. But I have come back to these course materials many times for a good reason. It's what you need to understand and use all the time. It is the absolute essential and necessary to know for any data scientist. While it might appear to be boring and basic compared to fancy deep learning models... trust me. It's not. It goes a long way in understanding what can be done with data. I am very grateful to the course instructors that they have spent their time and effort to make this course what it is. Please keep up the good work!

автор: BOUZENNOUNE Z E

•22 сент. 2019 г.

This was great. However, to follow it more precisely, you need the following: Read the book of linear regression from the same teacher. Usually a useful strategy would be to read each chapter first from the book, then watch the video associated to it, and finally do the swirl exercice.

You may need to follow the course notes of this class, they are published in github, and they can help a lot, especially for the quizzes.

автор: Matt S

•24 февр. 2019 г.

Challenging but highly rewarding course. Prof. Caffo does an excellent job presenting the material in a way that does not require previous background or expertise. The lectures were thorough and the Swirl exercises were very useful. I think the best part of this class is that it truly highlights how powerful and important regression is.

автор: Siddharth C

•19 авг. 2020 г.

This is a fantastic course to study the basics. Please give more "case study" projects as assignments instead of just ONE final project like the final one involving mtcars. I realized thinking about an end to end solution to a problem required me to combine all the concepts I had learned instead of knowing them in isolation (eg: statistical concepts, R codes to study residuals, r sqrd, F statistic, etc etc). Moreover, I had to also use intuition instead of a purely statistical approach which is how EVERY business problem works. "More case study problems as assignments please!!"

автор: Gayathri N

•1 сент. 2020 г.

Nice material .A suggestion would be to demarcate topics into 1) Theory version teaching the statistics part of it 2) R version showing the functions/Libraries in R that help doing the same 3) Some sample problems in the video explaining how to calculate sensitivity,specificity etc.. More of these will be helpful for folks like me who are from non statistics background and have been out of touch from Maths for past 20 years.

автор: Paul K

•28 мар. 2017 г.

Slightly better than the Statistical Inference course, but many of the same technical and delivery defects persist. With an otherwise high quality program, I recommend re-producing the inference and regression lectures to increase the overall value of the curriculum.

автор: ritu b

•6 февр. 2016 г.

Appears more like a revision for those who already know the content than geared towards those new to the subject.

автор: louis d

•10 июня 2016 г.

Content and quizz are not aligned.

Mentors answer to 0% of the forum posts.

Poor student community.

Do not pay for this course, just follow the swirl and/or get some tuto about regressions.

автор: Martin L

•26 июля 2017 г.

Very poor - the worst of the specialization courses by far. The lectures are confusing and poorly presented. If you want to understand regression you'll have to look elsewhere.

автор: Matt G

•15 февр. 2016 г.

Poorly designed, executed and instructed. Too much is left off the materials.

автор: Eric T

•21 февр. 2017 г.

Important material, poorly taught.

автор: Tejus S

•24 окт. 2020 г.

I really liked the way the professors teach a concept; starting from the need for it to step by step logical derivation for it. These courses of Data Science specialization have really inculcated the habits of scientific thinking in data science. Coming from a Biology research background it's really assuring for my skillset and confidence. Thank you.

автор: Benjamin S

•12 янв. 2018 г.

Material is too dense for the time spent engaged in class. Difficult to stay engaged with lectures, which spend a lot of time on the underlying mathematical concepts. The conceptual underpinnings are very important, but due to the limited timeframe available to present the material, the application of the concepts was done quickly, almost as an aside. The bridges from concept to practical application are very weak.

автор: Manuel D

•8 февр. 2016 г.

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.

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