Ementorhub

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

Advanced Analytics

Probability Distribution

Statistical Inference

Data Ethics

Statistical Modeling

Classification And Regression Tree (CART)

Machine Learning

Statistical Methods

R Programming

Data Analysis

Regression Analysis

There are 4 modules in this course

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

Models for Count Data

Introduction to Nonparametric Regression

Introduction to Generalized Additive Models