Advanced Linear Models for Data Science 2
This course is part of Advanced Statistics for Data Science.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Advanced Statistics for Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Master multivariate expected values and covariance matrices
Understand multivariate normal distribution properties
Apply distributional results in regression analysis
Develop confidence intervals and prediction intervals
Analyze residuals and PRESS statistics
Skills you'll gain
This course includes:
2.6 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This advanced course provides a comprehensive exploration of statistical linear models with a focus on least squares from a linear algebraic and mathematical perspective. Students learn about multivariate expected values, the multivariate normal distribution, distributional results in regression, and residual analysis. The curriculum emphasizes mathematical rigor and theoretical foundations while building practical understanding of regression modeling for data science applications.
Introduction and expected values
Module 1 · 1 Hours to complete
The multivariate normal distribution
Module 2 · 1 Hours to complete
Distributional results
Module 3 · 1 Hours to complete
Residuals
Module 4 · 1 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Advanced Statistics for Data Science
Instructor
Leading Biostatistics Expert and Educator at Johns Hopkins University
Dr. Brian Caffo is a distinguished professor in the Department of Biostatistics at the Johns Hopkins University Bloomberg School of Public Health. He earned his PhD in Statistics from the University of Florida in 2001 and has since made significant contributions to the fields of computational statistics and neuroinformatics. Dr. Caffo is a co-founder of the SMART (Statistical Methods and Applications for Research Teams) working group, which focuses on enhancing statistical methodologies in research. His dedication to teaching and mentorship has been recognized with multiple awards, including the Presidential Early Career Award for Scientists and Engineers (PECASE) and the Bloomberg School of Public Health's Golden Apple Award.In addition to his academic achievements, Dr. Caffo teaches a wide range of courses on Coursera, including "A Crash Course in Data Science," "Practical Machine Learning," and "The Data Scientist’s Toolbox." His courses are designed to equip learners with essential skills in data analysis and statistical modeling, making complex concepts accessible to students from various backgrounds. Through his work, Dr. Caffo continues to influence the fields of biostatistics and data science, fostering a new generation of data-savvy professionals.
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