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 is part of the Statistical Modeling for Data Science Applications Specialization
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About this Course
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Course 3 of 3 in the
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
Intermediate Level
Calculus, linear algebra, and probability theory.
Approx. 41 hours to complete
English
Offered by
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.
Syllabus - What you will learn from this course
12 hours to complete
An Introduction to Generalized Linear Models Through Binomial Regression
12 hours to complete
7 videos (Total 75 min), 3 readings, 7 quizzes
9 hours to complete
Models for Count Data
9 hours to complete
7 videos (Total 83 min)
9 hours to complete
Introduction to Nonparametric Regression
9 hours to complete
6 videos (Total 66 min)
12 hours to complete
Introduction to Generalized Additive Models
12 hours to complete
6 videos (Total 81 min), 1 reading, 4 quizzes
About the Statistical Modeling for Data Science Applications Specialization

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