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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6 modules

Gain insight into a topic and learn the fundamentals.

192 reviews

Advanced level

Designed for those already in the industry

8 hours to complete

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

192 reviews

Advanced level

Designed for those already in the industry

8 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

R Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments

Taught in English

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This course is part of the Advanced Statistics for Data Science Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 6 modules in this 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.

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

7 videosTotal 28 minutes

Introduction3 minutes
Matrix derivatives5 minutes
Coding example2 minutes
Centering by matrix multiplication7 minutes
Coding example2 minutes
Variance via matrix multiplication7 minutes
Coding example2 minutes

4 readingsTotal 40 minutes

Welcome to the class10 minutes
Course textbook10 minutes
Grading10 minutes
In this module10 minutes

1 assignmentTotal 30 minutes

Background Quiz30 minutes

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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Instructor

Instructor ratings

(21 ratings)

Brian Caffo, PhD

Johns Hopkins University

30 Courses1,720,549 learners

Offered by

Johns Hopkins University

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Reviewed on Mar 4, 2018

Very thorough and rigorous. A great review for me.

Reviewed on Apr 29, 2020

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

Reviewed on 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...

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