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

Brian Zaharatos

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Part of the Master of Science in Data Science degree
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Statistical Modeling for Data Science Applications SpecializationUniversity of Colorado Boulder

About this Course

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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

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Course 3 of 3 in the

Statistical Modeling for Data Science Applications Specialization

Intermediate Level

Calculus, linear algebra, and probability theory.

Approx. 41 hours to complete

English

Skills you will gain

Calculus
and probability theory.
Linear Algebra

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 3 in the

Statistical Modeling for Data Science Applications Specialization

Intermediate Level

Calculus, linear algebra, and probability theory.

Approx. 41 hours to complete

English

Instructor

Instructor rating

4.59/5 (5 Ratings)

Brian Zaharatos

Director, Professional Master’s Degree in Applied Mathematics

Department of Applied Mathematics

6,557 Learners

3 Courses

Offered by

University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Week1

Week 1

12 hours to complete

An Introduction to Generalized Linear Models Through Binomial Regression

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.

12 hours to complete

7 videos (Total 75 min), 3 readings, 7 quizzes

7 videos

From Linear Models to Generalized Linear Models12m The Components of a GLM6m The Exponential Family of Distributions14m Introduction to Binomial Regression9m Binomial Regression Parameter Estimation11m Interpretation of Binomial Regression7m Binomial Regression in R11m

3 readings

Welcome and Where to Find Help 10mInformation on Peer Reviews10mFairML Book, Introduction10m

3 practice exercises

Introduction to Generalized Linear Models30mBinomial Regression30mBinomial Regression Inference30m

Week2

Week 2

9 hours to complete

Models for Count Data

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.

9 hours to complete

7 videos (Total 83 min)

Week3

Week 3

9 hours to complete

Introduction to Nonparametric Regression

9 hours to complete

6 videos (Total 66 min)

Week4

Week 4

12 hours to complete

Introduction to Generalized Additive Models

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.

12 hours to complete

6 videos (Total 81 min), 1 reading, 4 quizzes

6 videos

Motivating Generalized Additive Models17mGeneralized Additive Models in R16mInference with Generalized Additive Models: Effective Degrees of Freedom12mInference with Generalized Additive Models: Tests4mGeneralized Additive Models in R: Inference and Interpretation13mGeneralized Additive Models: A Complete Example with Real Data16m

1 reading

Required: Generalized additive models for data science10m

2 practice exercises

Generalized Additive Models: Basics30mGeneralized Additive Models: Inference and Data Analysis30m

About the Statistical Modeling for Data Science Applications Specialization

Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.

This specialization 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

Statistical Modeling for Data Science Applications

Frequently Asked Questions

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Generalized Linear Models and Nonparametric Regression

About this Course

Skills you will gain