University of Colorado Boulder - Resampling, Selection and Splines

Resampling, Selection and Splines
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Course 2 of 3 in the Statistical Learning for Data Science Specialization
Intermediate Level Completion of Regression and Classification, the first course in the Statistical Learning for Data Science specialization.
Approx. 15 hours to complete
English Subtitles: English

Skills you will learn

More about this course

"Statistical Learning for Data Science" is an advanced course designed to equip working professionals with the knowledge and skills necessary to excel in the field of data science. Through comprehensive instruction on key topics such as shrink methods, parametric regression analysis, generalized linear models, and general additive models, students will learn how to apply resampling methods to gain additional information about fitted models, optimize fitting procedures to improve prediction accuracy and interpretability, and identify the benefits and approach of non-linear models. This course is the perfect choice for anyone looking to upskill or transition to a career in data science.
This course can be taken for academic credit as part of CU Boulders Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulders departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.