"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.
Resampling, Selection and Splines
Resampling, Selection and Splines
This course is part of Statistical Learning for Data Science Specialization
Taught in English
Instructor: Osita Onyejekwe
Included with 
Course
Gain insight into a topic and learn the fundamentals
Intermediate level
Recommended experience
10 hours (approximately)
Flexible schedule
Learn at your own pace
What you'll learn
Apply resampling methods in order to obtain additional information about fitted models.
Optimize fitting procedures to improve prediction accuracy and interpretability.
Identify the benefits and approach of non-linear models.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Course
Gain insight into a topic and learn the fundamentals
Intermediate level
Recommended experience
10 hours (approximately)
Flexible schedule
Learn at your own pace
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is part of the Statistical Learning for Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
Welcome to our Resampling, Selection, and Splines class! In this course, we will dive deep into these key topics in statistical learning and explore how they can be applied to data science. The module provides an introductory overview of the course and introduces the course instructor.
What's included
6 videos2 readings1 discussion prompt
This week, we will be exploring the concept of cross-validation, a crucial technique used to evaluate and compare the performance of different statistical learning models. We will explore different types of cross-validation techniques, including k-fold cross-validation, leave-one-out cross-validation, and stratified cross-validation. We will discuss their strengths, weaknesses, and best practices for implementation. Additionally, we will examine how cross-validation can be used for model selection and hyperparameter tuning.
What's included
1 video1 reading1 programming assignment
In this module, we will explore ridge regression, LASSO, and principal component analysis (PCA). These techniques are widely used for regression and dimensionality reduction tasks in machine learning and statistics.
What's included
7 videos3 readings3 programming assignments
Instructor
Offered by
Recommended if you're interested in Probability and Statistics
University of Colorado Boulder
University of Colorado Boulder
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