Advanced Linear Models for Data Science 2: Statistical Linear Models

Johns Hopkins University

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. 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.

Certificate Available ✔

Get Started / More Info
Advanced Linear Models for Data Science 2: Statistical Linear Models
More Probability and Statistics Courses

Data Science Methods for Quality Improvement

University of Colorado Boulder

Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for...

Forecasting US Presidential Elections with Mixed Models

Coursera Project Network

In this project-based course, you will learn how to forecast US Presidential Elections. We will use mixed effects models in the R programming language to build a...

Managing, Describing, and Analyzing Data

University of Colorado Boulder

In this course, you will learn the basics of understanding the data you have and why correctly classifying data is the first step to making correct decisions. You...

Statistical Inference for Estimation in Data Science

University of Colorado Boulder

This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators,...