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Advanced Linear Models for Data Science 1: Least Squares

Approx. 8 hours to complete

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Course Summary

Learn the fundamentals of linear models, including linear regression, logistic regression, and more. Discover how to use these models to make predictions and gain insights from data.

Key Learning Points

Understand the basics of linear models and their applications in data analysis
Learn how to build and analyze linear regression models
Explore logistic regression and other types of linear models
Apply linear models to real-world data sets
Gain practical experience with R programming language

Learning Outcomes

Understand the principles of linear models and their applications
Gain practical experience in building and analyzing linear regression models
Apply linear models to real-world data sets and make predictions

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics and probability
Familiarity with R programming language

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Video lectures
Hands-on exercises
Quizzes and assessments

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Applied Data Science with Python
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Related Education Paths

Notable People in This Field

Andrew Ng
Deborah Berebichez
Kirk Borne

Related Books

Description

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.

Outline

Background
Introduction
Matrix derivatives
Coding example
Centering by matrix multiplication
Coding example
Variance via matrix multiplication
Coding example
Welcome to the class
Course textbook
Grading
In this module
Background Quiz

One and two parameter regression
Regression through the origin
Centering first
Coding example
Connection with linear regression
Coding example
Fitted values and residuals
Before you begin
Before you begin
One Parameter Regression Quiz

Linear regression
Least squares
Coding example
Prediction
Coding example
Residuals
Coding example
Generalizations
Generalizations example
Before you begin
Generalizations
Linear Regression Quiz

General least squares
Least squares
Coding example
Second derivation of least squares
Projections
Third derivation of least squares
Coding example
Before you begin
General Least Squares Quiz

Least squares examples
Basic examples of design matrices and fits
Group effects
Change of parameterization
ANCOVA
Least Squares Examples Quiz

Bases and residuals
Bases, introduction
Bases 2, Fourier
Bases 3, SVDs
Bases, coding example
Introduction to residuals
Partitioning variability
Bases Quiz
Residuals Quiz

Summary of User Reviews

Discover the power of linear models with Coursera's Linear Models course. Students praise the comprehensive material and hands-on exercises, while instructors guide you through the ins and outs of linear modeling without needing prior knowledge. Learn how to analyze data and make accurate predictions through this comprehensive course.

Key Aspect Users Liked About This Course

Many users enjoyed the hands-on exercises and comprehensive material offered in the course.

Pros from User Reviews

Instructors are knowledgeable and provide clear explanations
Course material is comprehensive and covers a wide range of topics
Hands-on exercises help students apply what they've learned in real-world settings
Well-structured course with clear objectives and goals

Cons from User Reviews

Some users found the course to be too basic and lacking in depth
The pacing of the course may be too slow for advanced learners
Limited interaction with other students and instructors
Some users experienced technical difficulties with the platform
No certificate of completion offered for the free version of the course

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