Enroll Course: https://www.coursera.org/learn/linear-models-2
Course Overview
As we dive deeper into the realm of data science, understanding the intricacies of linear models becomes paramount. The course, Advanced Linear Models for Data Science 2: Statistical Linear Models, offers a robust foundation for students who already possess some knowledge of linear algebra, statistics, and R programming. Designed to build on prior knowledge, this course introduces least squares from both a linear algebraic and mathematical perspective.
What to Expect
Before embarking on this course, it’s essential to have a grasp of multivariate calculus, foundational statistics, and at least a cursory familiarity with proof-based mathematics. If you check these boxes, you’re all set to take on an insightful journey through advanced linear modeling.
Syllabus Breakdown
The syllabus is divided into four main modules, each focusing on different facets of statistical linear models:
- Introduction and Expected Values – This module lays the groundwork, covering prerequisites and delving into expected values for multivariate vectors, as well as understanding the moment properties of ordinary least squares estimates.
- The Multivariate Normal Distribution – Here, you will explore the development of multivariate and singular normal distributions, starting from independent and identically distributed (iid) normals.
- Distributional Results – This module presents the basic distributional results pertinent to multivariable regression, enhancing your understanding of how these apply to real-world data.
- Residuals – You’ll engage in a detailed analysis of residuals, including an exploration of their distributional results and the calculation of PRESS residuals without the need for model refitting.
Why You Should Enroll
The appeal of this course lies in its rigorous approach to understanding linear models through a statistical lens. It’s perfect for students and professionals looking to strengthen their analytical skills. The inclusion of R programming ensures that learners can apply theoretical knowledge to practical problems.
Moreover, the course is structured not only to impart knowledge but also to enhance your problem-solving skills in the context of data science. With the increasing importance of data-driven decision-making in various sectors, such competencies are invaluable.
Conclusion
If you’re seeking a comprehensive course to advance your knowledge in statistical linear models, look no further than Advanced Linear Models for Data Science 2: Statistical Linear Models. This course is a solid investment in your professional development journey, preparing you to tackle complex data challenges with confidence.
Enroll Course: https://www.coursera.org/learn/linear-models-2