Back to Advanced Linear Models for Data Science 1: Least Squares
Learner Reviews & Feedback for Advanced Linear Models for Data Science 1: Least Squares by Johns Hopkins University
190 ratings
About the Course
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.
Top reviews
JR
Nov 6, 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.
CL
Apr 29, 2020
The course is interesting; but is more theoretical in nature than applied.
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