# 学生对 约翰霍普金斯大学 提供的 Advanced Linear Models for Data Science 1: Least Squares 的评价和反馈

4.4
160 个评分
39 条评论

## 课程概述

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....

## 热门审阅

JL
May 16, 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 7, 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.

## 26 - Advanced Linear Models for Data Science 1: Least Squares 的 40 个评论（共 40 个）

Jun 11, 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The coding videos with R are outdated.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.

Sep 3, 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.

Feb 7, 2021

I would appreciate more practical exercises with R. But Prof. Caffo was very good at explaining concepts and give the nuisances behind a model, I do really appreciate his style of putting things together. Highly recommend this course and the professor.

May 7, 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.

Sep 9, 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

Nov 7, 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.

Sep 10, 2019

El curso es bueno, sin embargo me gustaría que pusieran notas sobre las ecuaciones o un pequeño resumen, ya que yo al menos no tengo dinero para comprar los materiales.

May 9, 2017

Good course. Quite hard. Linear algebra should be your second language as it is assumed to be mastered. Exams should include some personal work.

Feb 23, 2017

Hard Topic, You must take all the basics in multivariate statistical analysis first.

Apr 30, 2020

The course is interesting; but is more theoretical in nature than applied.

Feb 15, 2019

Not an advanced level course.

Jul 22, 2020

Nice Course.

Jul 28, 2017

Great Course

Apr 28, 2021

The material was incredibly interesting but especially for weeks 4-6 the lectures seemed to have been pieces of much broader lectures and therefore were difficult to follow. I spent more time researching the material than I did on the course.

Feb 15, 2021

dropped