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Generalized Linear Models and Nonparametric Regression

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Generalized Linear Models and Nonparametric Regression

This course is part of Statistical Modeling for Data Science Applications Specialization

Instructor: Brian Zaharatos

5,360 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

24 reviews

Intermediate level

Recommended experience

Flexible schedule

4 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

24 reviews

Intermediate level

Recommended experience

Flexible schedule

4 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

What you'll learn

Describe how to generalize the linear model framework to accommodate data that is not suitable for the standard linear regression model.
State some advantages and disadvantages of (generalized) additive models.
Describe how an additive model can be generalized to incorporate non-normal response variables (i.e., define a generalized additive model).

Skills you'll gain

Tools you'll learn

Details to know

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Assessments

8 assignments

Taught in English

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This course is part of the Statistical Modeling for Data Science Applications Specialization

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There are 4 modules in this course

In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools. Attention will also be given to ethical issues raised by using complicated statistical models.

This course can be taken for academic credit as part of CU Boulder’s 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 Boulder’s 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. Logo adapted from photo by Vincent Ledvina on Unsplash

Module details

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, including the most common binomial link functions; correctly interpret the binomial regression model; and consider various methods for assessing the fit and predictive power of the binomial regression model.

What's included

7 videos5 readings3 assignments2 programming assignments2 peer reviews1 discussion prompt2 ungraded labs

7 videosTotal 75 minutes

From Linear Models to Generalized Linear Models13 minutes
The Components of a GLM6 minutes
The Exponential Family of Distributions15 minutes
Introduction to Binomial Regression10 minutes
Binomial Regression Parameter Estimation12 minutes
Interpretation of Binomial Regression8 minutes
Binomial Regression in R12 minutes

5 readingsTotal 41 minutes

Course Updates and Accessibility Support1 minute
Earn Academic Credit for your Work!10 minutes
Course Support10 minutes
Assessment Expectations10 minutes
FairML Book, Introduction10 minutes

3 assignmentsTotal 90 minutes

Introduction to Generalized Linear Models30 minutes
Binomial Regression30 minutes
Binomial Regression Inference30 minutes

2 programming assignmentsTotal 300 minutes

Module 1 Autograded Assignment120 minutes
Optional Introduction to Jupyter and R180 minutes

2 peer reviewsTotal 120 minutes

Ethical Issues in Statistics and Data Science (Fair ML Intro)60 minutes
Module 1 Peer-Review Assignment Submission60 minutes

1 discussion promptTotal 10 minutes

Introduce Yourself10 minutes

2 ungraded labsTotal 120 minutes

Assessing the fit of the binomial regression model60 minutes
Module 1 Peer-Review Lab60 minutes

In this module, we will consider how to model count data. When the response variable is a count of some phenomenon, and when that count is thought to depend on a set of predictors, we can use Poisson regression as a model. We will describe the Poisson regression in some detail and use Poisson regression on real data. Then, we will describe situations in which Poisson regression is not appropriate, and briefly present solutions to those situations.

What's included

7 videos2 assignments1 programming assignment1 peer review3 ungraded labs

7 videosTotal 83 minutes

Poisson Regression: A New Model for Count Data13 minutes
Poisson Regression Parameter Estimation7 minutes
Interpreting the Poisson Regression Model7 minutes
Poisson Regression on Real Data in R22 minutes
Goodness of Fit for Poisson Regression I17 minutes
Goodness of Fit for Poisson Regression II5 minutes
Overdispersion12 minutes

2 assignmentsTotal 60 minutes

Poisson Regression Basics30 minutes
Poisson Regression Inference and Goodness of Fit30 minutes

1 programming assignmentTotal 180 minutes

Module 2 Autograded Assignment180 minutes

1 peer reviewTotal 60 minutes

Module 2 Peer-Review Lab Submission60 minutes

3 ungraded labsTotal 180 minutes

Poisson regression on real data in R60 minutes
Poisson regression goodness of fit in R60 minutes
Module 2 Peer-Review Lab60 minutes

In this module, we will introduce the concept of a nonparametric regression model. We will contrast this notion with the parametric models that we have studied so far. Then, we’ll study particular nonparametric regression models: kernel estimators and splines. Finally, we will introduce additive models as a blending of parametric and nonparametric methods.

What's included

6 videos1 assignment1 programming assignment1 peer review3 ungraded labs

6 videosTotal 66 minutes

Introduction to Nonparametric Regression Models12 minutes
Motivating Kernel Estimators6 minutes
Kernel Estimators14 minutes
Smoothing Splines13 minutes
Loess: Locally Estimated Scatterplot Smoothing14 minutes
Kernel Estimation in R6 minutes

1 assignmentTotal 30 minutes

Nonparametric Regression: Theory30 minutes

1 programming assignmentTotal 180 minutes

Module 3 Autograded Assignment180 minutes

1 peer reviewTotal 60 minutes

Module 3 Peer-Review Assignment Submission60 minutes

3 ungraded labsTotal 180 minutes

Smoothing Splines in R60 minutes
The Loess Fit in R60 minutes
Module 3 Peer-Review Lab60 minutes

Some models, such as linear regression, are easily interpretable, but inflexible, in that they don't capture many real-world relationships accurately. Other models, such as neural networks, are quite flexible, but very difficult to interpret. Generalized additive models (GAMs) are a nice balance between flexibility and interpretability. In this module, we will further motivate GAMs, learn the basic mathematics of fitting GAMs, and implementing them on simulated and real data in R.

What's included

6 videos1 reading2 assignments1 programming assignment1 peer review3 ungraded labs

6 videosTotal 81 minutes

Motivating Generalized Additive Models18 minutes
Generalized Additive Models in R16 minutes
Inference with Generalized Additive Models: Effective Degrees of Freedom12 minutes
Inference with Generalized Additive Models: Tests5 minutes
Generalized Additive Models in R: Inference and Interpretation14 minutes
Generalized Additive Models: A Complete Example with Real Data17 minutes

1 readingTotal 10 minutes

Required: Generalized additive models for data science10 minutes

2 assignmentsTotal 60 minutes

Generalized Additive Models: Basics30 minutes
Generalized Additive Models: Inference and Data Analysis30 minutes

1 programming assignmentTotal 180 minutes

Module 4 Autograded Assignment180 minutes

1 peer reviewTotal 180 minutes

Module 4 Peer-Review Assignment Submission180 minutes

3 ungraded labsTotal 180 minutes

Generalized Additive Models in R60 minutes
Generalized Additive Models in R: Inference and Interpretation60 minutes
Module 4 Peer-Review Lab60 minutes

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This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructor

Instructor ratings

(11 ratings)

Brian Zaharatos

University of Colorado Boulder

4 Courses15,217 learners

Offered by

University of Colorado Boulder

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Reviewed on Jan 23, 2026

Can speak highly enough of this professor. He is extremely knowledgeable and can convey concepts in one of the clearest ways I have ever seen in my academic career.

Reviewed on Jun 27, 2023

The pace of instruction is excellent and the assignments make it easy to translate theory to practice.

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