Back to Advanced Linear Models for Data Science 1: Least Squares

4.5

stars

140 ratings

•

36 reviews

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:
- A basic understanding of linear algebra and multivariate calculus.
- A basic understanding of statistics and regression models.
- At least a little familiarity with proof based mathematics.
- Basic knowledge of the R programming language.
After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models....

JL

May 17, 2020

I really enjoyed the course. It was well explained and the quizzes at regular intervals were helpful. It would be great if there were some practice exercises though...

DL

Jun 08, 2016

We need more advanced, theoretical courses on Coursera, like this one, in order to deeply understand the more general courses like Regression Models and Linear Models.

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By Andrea G

•Feb 11, 2018

This course is very interesting and Professor Caffo is very good at teaching. The proposed material is not very well organised and even if there are multiple sources available (videos, book, YT videos) they all say the exact same things (literally): it isn't helpful, only redundant. Moreover, I felt a lack of context: yes, it is only a 6 weeks course and yes there is a strict relationship with at least other 5 courses on Coursera (as a prerequisite) so it may be hard to contextualise. Nevertheless the material seems to be taken here and there from other courses/specialisations so you often have the feeling that you are missing something that may have been said in a previous lesson that does not belong to this course (and I am not talking about basic linear algebra stuff) or you wonder "what are we trying to prove? And why?". Material desperately lacks homogeneity, and it's easy to lose focus. Last but not least: R is a prerequisite, which is a bit strange since the topic is very theoretical (and there are no practical references throughout the course). R is mainly used by the professor to prove that theory is right (Wouldn't be more interesting to take advantage of R's plotting capabilities to have a visual result of theory?) or there are quiz questions that require the usage of R to get the answer (Why? Am I supposed to learn R or Least Squares?). Sometimes you feel like this is not a standalone, focused course, but an appendix of other specialisations. Overall it's a good course, very interesting topic, made harder by material that is a bit collected here and there and put together without the care the subject would require. My suggestion is to enroll only after completing Statistical Inference and Regression Models (both by B. Caffo) so that language and context are the same.

By Rumian R

•Aug 08, 2020

This course was revelation upon revelation (in addition to refreshing/re-envisioning some basics). I appreciated the connections between Principal Component Decomposition, Eigenvalue Decomposition, and Singular Value Decomposition.

My only issue was with vector notation, but otherwise I recommend this course for anyone who wants an intimate understanding in Least Squares regression.

By Jayson L

•May 17, 2020

I really enjoyed the course. It was well explained and the quizzes at regular intervals were helpful. It would be great if there were some practice exercises though...

By Do H L

•Jun 08, 2016

We need more advanced, theoretical courses on Coursera, like this one, in order to deeply understand the more general courses like Regression Models and Linear Models.

By Sarvesh P

•Apr 30, 2017

Good mathematical rigour for the analysis of linear models. Builds some good intuition for the geometry of least squares which helps in model result interpretation.

By 김은산

•Jun 17, 2020

This is a very good lecture for a understanding the regression in the view of linear algebra. However, a prior understanding of some linear algebra is needed.

By Hernán M

•Jun 12, 2016

As the name says it's an advanced course. Take the challenge though! In my opinion the content is a must if you want to perform competently in data science.

By Soutik H

•Sep 13, 2020

Excellent experience. I have learnt a lot in different aspect of linear models as well as the coding skills from this course. Thank you.

By Yasmine G

•Jul 25, 2020

Great refresher of linear algebra

understood many things about linear models that I just knew superficially from its cores

By Srikanth K S

•Sep 27, 2016

chapter on bases showing four equivalent forms was brilliant! Hoping to learn BLUE, GAMs in part 2.

By Brady H

•Mar 21, 2017

good course that teaches these kind of hard work in an easier understanding way.

By Debarya J

•Apr 23, 2017

A wonderful course to study! Prof. Brian Caffo explains so well!

By Ian K

•Aug 12, 2020

Thank you. A very challenging and deeply insightful course.

By John C

•Mar 05, 2018

Very thorough and rigorous. A great review for me.

By Dmitriy

•Apr 25, 2017

Good and not overloaded. Recommended.

By Roney A B

•Jun 06, 2017

Very helpful! Tanks!

By RAMAKRISHNA R

•Jul 01, 2020

Excellent course

By Lyu m

•Sep 18, 2016

good course!

By Chad W L

•Apr 17, 2018

Very good

By Herson N M

•Dec 18, 2016

Advanced

By Ryan G

•Jun 12, 2019

The coding videos with R are outdated. But this is to be expected since R is open-source and changing rapidly. The code videos should be updated frequently to coincide with the latest release of R and R Studio. I like that code example content is placed into separate videos. The videos are very clear and easy to see. If lecture segments are re-worked, I'd suggest writing in a single column, and keeping the new content always in the center of the screen. There is some inconsistencies in the notation, and some content is repeated too often. But it's not like salt: too much isn't nearly as bad as not enough.

By Christoph L

•Sep 03, 2020

A good course that has some insights (especially for regression) but that feels towards the end very cut together from other existing materials. Thus, there are some jumps in the topics and some repetitions of subjects. It feels like some aspects such as the partitioning of variability (week 6) could have been explained more easily.

By Xinpeng H

•May 07, 2017

I enjoyed the math and it helped me to review my linear algebra and got new intuitions on linear regression. But there are a few typos that need to be fixed. It would be better to open a forum and let student discuss with each other.

By Jean p A

•Sep 09, 2020

This is an excellent course that enabled me to understand how multiple regression in linear models works behind the hood. The practical examples shown by the professor were very helpful. Thank you

By Jens L R

•Nov 07, 2017

Great, detailed walk-through of least squares. Linear Algebra is a must for this course. To follow the last part requires knowledge of matrix (eigen?)decomposition, which derailed me somewhat.

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