University of Colorado Boulder
Resampling, Selection and Splines
University of Colorado Boulder

Resampling, Selection and Splines

This course is part of Statistical Learning for Data Science Specialization

Taught in English

Osita Onyejekwe

Instructor: Osita Onyejekwe

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

15 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.

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Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

15 hours (approximately)
Flexible schedule
Learn at your own pace

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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 5 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

In this module, we will turn our attention to generalized least squares (GLS). GLS is a statistical method that extends the ordinary least squares (OLS) method to account for heteroscedasticity and serial correlation in the error terms. Heteroscedasticity is the condition where the variance of the errors is not constant across all levels of the predictor variables, while serial correlation is the condition where the errors are correlated across time or space. GLS has many practical applications, such as in finance for modeling asset returns, in econometrics for modeling time series data, and in spatial analysis for modeling spatially correlated data. By the end of this module, you will have a good understanding of how GLS works and when it is appropriate to use it. You will also be able to implement GLS in R using the gls() function in the nlme package.

What's included

1 video1 reading1 programming assignment1 ungraded lab

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

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

For our final module, we will explore bootstrapping. Bootstrapping is a resampling technique that allows us to gain insights into the variability of statistical estimators and quantify uncertainty in our models. By creating multiple simulated datasets through resampling, we can explore the distribution of sample statistics, construct confidence intervals, and perform hypothesis testing. Bootstrapping is particularly useful when parametric assumptions are hard to meet or when we have limited data. By the end of this week, you will have an understanding of bootstrapping and its practical applications in statistical learning.

What's included

1 video1 reading1 programming assignment

Instructor

Osita Onyejekwe
University of Colorado Boulder
2 Courses663 learners

Offered by

Recommended if you're interested in Probability and Statistics

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