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.
Centering first
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Skills You'll Learn
Mathematics, Regression, Probability & Statistics, Linear Regression, General Statistics, Linear Algebra, R Programming, Statistics
Reviews
4.5 (187 ratings)
- 5 stars63.10%
- 4 stars25.13%
- 3 stars7.48%
- 2 stars3.20%
- 1 star1.06%
SP
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.
HM
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.
From the lesson
One and two parameter regression
In this module, we cover the basics of regression through the origin and linear regression. Regression through the origin is an interesting case, as one can build up all of multivariate regression with it.
Taught By
Brian Caffo, PhD
Professor, Biostatistics