Resampling, Selection and Splines
Master statistical learning techniques including resampling methods, model selection, and spline analysis for advanced data science applications.
Master statistical learning techniques including resampling methods, model selection, and spline analysis for advanced data science applications.
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 Statistical Learning 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.
Instructors:
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What you'll learn
Apply resampling methods to evaluate and improve model performance
Implement ridge regression and LASSO for feature selection
Use cross-validation techniques for model validation
Master bootstrapping for statistical inference
Develop expertise in generalized least squares methods
Skills you'll gain
This course includes:
3.6 Hours PreRecorded video
3 programming assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course explores advanced statistical learning techniques essential for data science professionals. The curriculum covers crucial topics including resampling methods, model selection techniques, and spline analysis. Through hands-on programming assignments and theoretical foundations, students learn to optimize model fitting procedures, implement cross-validation techniques, and apply bootstrapping methods. The course emphasizes practical applications while building a strong theoretical understanding of statistical learning concepts.
Welcome and Review
Module 1 · 1 Hours to complete
Generalized Least Squares
Module 2 · 2 Hours to complete
Shrink Methods
Module 3 · 5 Hours to complete
Cross-Validation
Module 4 · 3 Hours to complete
Bootstrapping
Module 5 · 2 Hours to complete
Fee Structure
Instructor
Assistant Professor at the University of Colorado Boulder
Dr. Osita Onyejekwe is an Assistant Professor at the University of Colorado Boulder, where he specializes in multivariate regression models and machine learning techniques. His research focuses on estimating weather patterns, analyzing glacier recession behavior, and developing financial models related to profit gains, losses, and revenue. In addition to his quantitative research interests, Dr. Onyejekwe explores topics in planetary systems, abiogenesis, philosophy, and theology, reflecting a diverse academic curiosity that bridges the sciences and humanities. His interdisciplinary approach aims to contribute valuable insights across various fields while enhancing the understanding of complex systems and their interactions.
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