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Generalized Linear Models and Nonparametric Regression
  • Level Professional
  • Duration 42 hours
  • Course by University of Colorado Boulder
  • Offered by

About

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

Modules

Optional Introduction to Jupyter and R

    • 1 Programming
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1 Programming

  • Optional Introduction to Jupyter and R

Introduction to Generalized Linear Models

    • 3 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Quiz
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1 Discussions

  • Introduce Yourself

3 Videos

  • From Linear Models to Generalized Linear Models
  • The Components of a GLM
  • The Exponential Family of Distributions

3 Readings

  • Earn Academic Credit for your Work!
  • Course Support
  • Assessment Expectations

1 Quiz

  • Introduction to Generalized Linear Models

Binomial Regression

    • 4 Videos
    • 1 Quiz
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4 Videos

  • Introduction to Binomial Regression
  • Binomial Regression Parameter Estimation
  • Interpretation of Binomial Regression
  • Binomial Regression in R

1 Quiz

  • Binomial Regression

Binomial Regression and Goodness of Fit

    • 1 Quiz
    • 1 Lab
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1 Labs

  • Assessing the fit of the binomial regression model

1 Quiz

  • Binomial Regression Inference

Ethical Issues in Statistics and Data Science

    • 1 Readings
    • 1 PeerReview
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1 Peer Review

  • Ethical Issues in Statistics and Data Science (Fair ML Intro)

1 Readings

  • FairML Book, Introduction

Assignments: GLMs for Binomial Data

    • 1 PeerReview
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 1 Autograded Assignment

1 Peer Review

  • Module 1 Peer-Review Assignment Submission

1 Labs

  • Module 1 Peer-Review Lab

Poisson Regression Basics

    • 4 Videos
    • 1 Quiz
    • 1 Lab
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1 Labs

  • Poisson regression on real data in R

4 Videos

  • Poisson Regression: A New Model for Count Data
  • Poisson Regression Parameter Estimation
  • Interpreting the Poisson Regression Model
  • Poisson Regression on Real Data in R

1 Quiz

  • Poisson Regression Basics

Poisson Regression Inference and Goodness of Fit

    • 3 Videos
    • 1 Quiz
    • 1 Lab
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1 Labs

  • Poisson regression goodness of fit in R

3 Videos

  • Goodness of Fit for Poisson Regression I
  • Goodness of Fit for Poisson Regression II
  • Overdispersion

1 Quiz

  • Poisson Regression Inference and Goodness of Fit

Assignments: GLMs for Count Data

    • 1 PeerReview
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 2 Autograded Assignment

1 Peer Review

  • Module 2 Peer-Review Lab Submission

1 Labs

  • Module 2 Peer-Review Lab

Nonparametric Regression: Theory

    • 5 Videos
    • 1 Quiz
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5 Videos

  • Introduction to Nonparametric Regression Models
  • Motivating Kernel Estimators
  • Kernel Estimators
  • Smoothing Splines
  • Loess: Locally Estimated Scatterplot Smoothing

1 Quiz

  • Nonparametric Regression: Theory

Nonparametric Regression: Data Analysis

    • 1 Videos
    • 2 Lab
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2 Labs

  • Smoothing Splines in R
  • The Loess Fit in R

1 Videos

  • Kernel Estimation in R

Assignments: Nonparametric Regression and Smoothing Functions

    • 1 PeerReview
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 3 Autograded Assignment

1 Peer Review

  • Module 3 Peer-Review Assignment Submission

1 Labs

  • Module 3 Peer-Review Lab

Generalized Additive Models: Basics

    • 2 Videos
    • 1 Readings
    • 1 Quiz
    • 1 Lab
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1 Labs

  • Generalized Additive Models in R

2 Videos

  • Motivating Generalized Additive Models
  • Generalized Additive Models in R

1 Readings

  • Required: Generalized additive models for data science

1 Quiz

  • Generalized Additive Models: Basics

Generalized Additive Models: Inference and Data Analysis

    • 4 Videos
    • 1 Quiz
    • 1 Lab
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1 Labs

  • Generalized Additive Models in R: Inference and Interpretation

4 Videos

  • Inference with Generalized Additive Models: Effective Degrees of Freedom
  • Inference with Generalized Additive Models: Tests
  • Generalized Additive Models in R: Inference and Interpretation
  • Generalized Additive Models: A Complete Example with Real Data

1 Quiz

  • Generalized Additive Models: Inference and Data Analysis

Assignments: GAMs

    • 1 PeerReview
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 4 Autograded Assignment

1 Peer Review

  • Module 4 Peer-Review Assignment Submission

1 Labs

  • Module 4 Peer-Review Lab

Auto Summary

Explore advanced statistical modeling tools in "Generalized Linear Models and Nonparametric Regression," focusing on GLMs, logistic regression, and nonparametric methods like kernel estimators and smoothing splines. Ideal for data science professionals, this CU Boulder course emphasizes conceptual understanding and addresses ethical considerations. Part of the MS-DS degree, it offers flexible subscription options and caters to those with diverse educational backgrounds.

Brian Zaharatos
Instructor

Brian Zaharatos