Johns Hopkins University
Advanced Linear Models for Data Science 1: Least Squares
Johns Hopkins University

Advanced Linear Models for Data Science 1: Least Squares

This course is part of Advanced Statistics for Data Science Specialization

Taught in English

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Brian Caffo, PhD

Instructor: Brian Caffo, PhD

29,102 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(186 reviews)

Advanced level
Designed for those already in the industry
8 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

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Assessments

7 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(186 reviews)

Advanced level
Designed for those already in the industry
8 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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

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 quiz

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 quiz

In this lecture, we focus on linear regression, the most standard technique for investigating unconfounded linear relationships.

What's included

8 videos2 readings1 quiz

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 quiz

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 quiz

Here we give a very useful kind of linear model, that is decomposing a signal into a basis expansion.

What's included

6 videos2 quizzes

Instructor

Instructor ratings
4.7 (20 ratings)
Brian Caffo, PhD
Johns Hopkins University
30 Courses1,614,517 learners

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Recommended if you're interested in Probability and Statistics

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4.5

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