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:
Advanced Linear Models for Data Science 1: Least Squares
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Advanced Linear Models for Data Science 1: Least Squares
This course is part of Advanced Statistics for Data Science Specialization
Instructor: Brian Caffo, PhD
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4.5
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Advanced level
Designed for those already in the industry
8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
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Assessments
7 assignments
Taught in English
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This course is part of the Advanced Statistics for Data Science Specialization
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There are 6 modules in this course
We cover some basic matrix algebra results that we will need throughout the class. This includes some basic vector derivatives. In addition, we cover some some basic uses of matrices to create summary statistics from data. This includes calculating and subtracting means from observations (centering) as well as calculating the variance.
What's included
7 videos4 readings1 assignment
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.
What's included
6 videos2 readings1 assignment
In this lecture, we focus on linear regression, the most standard technique for investigating unconfounded linear relationships.
What's included
8 videos2 readings1 assignment
We now move on to general least squares where an arbitrary full rank design matrix is fit to a vector outcome.
What's included
6 videos1 reading1 assignment
Here we give some canonical examples of linear models to relate them to techniques that you may already be using.
What's included
4 videos1 assignment
Here we give a very useful kind of linear model, that is decomposing a signal into a basis expansion.
What's included
6 videos2 assignments
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