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Brian Zaharatos
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.
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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.
Read more
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.
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What's inside
Syllabus
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.
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Syllabus
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.
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Traffic lights
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Read about what's good
what should give you pause and
possible dealbreakers
Explores statistical modeling tools, which are standard in data science and machine learning
Taught by Brian Zaharatos, who is recognized for their work in statistical modeling
Examines generalized linear models (GLMs), nonparametric modeling, and semi-parametric generalized additive models (GAMs), which are highly relevant to data science
Builds a strong foundation for beginners in statistical modeling
Covers unique perspectives and ideas that may add color to other topics and subjects
Requires students to come in with extensive background knowledge
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Reviews summary
Advanced statistical modeling and regression
According to learners, the "Generalized Linear Models and Nonparametric Regression" course provides a deep conceptual understanding of advanced statistical modeling techniques like GLMs, GAMs, and nonparametric methods. Many students praise the instructor's clear explanations, which make complex topics accessible. The curriculum offers a solid theoretical foundation, effectively complemented by practical R labs that reinforce learning. A distinguishing feature highlighted by some is the inclusion of ethical considerations in statistical modeling. While broadly positive, some find the course challenging, suggesting a strong mathematical and statistical background is crucial. Opinions vary slightly on the balance between theory and hands-on application, with a minority desiring more extensive practical projects.
Primarily focuses on conceptual theory over extensive practical projects.
"I was disappointed by the lack of hands-on projects beyond simple R exercises."
"While the content is valuable, I found the explanations sometimes abstract, especially for nonparametric concepts."
"I expected more hands-on implementation and less mathematical proofs."
"This course emphasizes a firm conceptual understanding of these tools, which is exactly what I needed for my research."
Unique inclusion of ethical issues in statistical modeling.
"I appreciate the emphasis on ethical considerations within statistical modeling."
"Ethical discussions were a good addition, providing valuable context."
"I especially liked the ethical considerations module, which is often overlooked in other courses."
"The module on ethical issues was a refreshing and important part of the curriculum."
Includes R labs that reinforce theoretical concepts effectively.
"The R labs were essential for applying the concepts discussed in lectures."
"The blend of theory and R implementation was perfect. I finally feel confident applying these models."
"The R exercises reinforced learning well, allowing me to practice what I learned."
"I found the R code useful, though I wished for more in-depth explanations for non-R experts."
Provides a strong theoretical and conceptual foundation.
"This course was incredibly insightful. The instructor's explanations of GLMs and GAMs were very clear, and the R labs were essential..."
"Excellent theoretical foundation. I particularly enjoyed the modules on Poisson regression. The professor broke down complex ideas effectively."
"The conceptual understanding is prioritized, which is great. Some parts are dense, but rewatching lectures helped."
"Outstanding! The instructor made complex topics like smoothing splines understandable. I finally feel confident applying these models."
Demands a strong background and can be challenging for some.
"I found some parts quite challenging without a very strong math background."
"Prerequisites are important; this isn't for beginners. A solid stats foundation is a must."
"The pace was sometimes too fast, and I struggled with application without more guidance."
"This is a challenging but rewarding course for those serious about data science."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Generalized Linear Models and Nonparametric Regression with these
activities:
Review 'Data Science from Scratch'
Show steps
Broaden your knowledge of statistical modeling by reviewing a book that covers the same topics in even greater detail.
View
Data Science from Scratch: First Principles...
on Amazon
Show steps
-
Obtain a copy of 'Data Science from Scratch'
-
Read the chapters corresponding to the course modules
-
Complete the exercises and activities in the book
Solve Practice Problems on Nonparametric Regression
Show steps
Strengthen your problem-solving skills and reinforce your knowledge of nonparametric regression.
Browse courses on
Nonparametric Regression
Show steps
-
Find practice problems or exercises on nonparametric regression
-
Solve the problems and check your answers
-
Identify areas where you need more practice
Develop a Tutorial on Poisson Regression
Show steps
Solidify your understanding of Poisson regression by explaining it to others.
Browse courses on
Poisson Regression
Show steps
-
Review the concepts of Poisson regression
-
Create a presentation or write a blog post explaining the theory and application of Poisson regression
-
Share your tutorial with peers or publish it online
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Tutorials on Generalized Additive Models
Show steps
Expand your knowledge of GAMs by following expert guidance.
Browse courses on
Generalized Additive Models
Show steps
-
Search for tutorials on generalized additive models
-
Follow the tutorials step-by-step
-
Experiment with the code and explore different examples
Implement a GLM with Different Link Functions
Show steps
Enhance your understanding of GLMs by applying them to real-world problems.
Browse courses on
Generalized Linear Models
Show steps
-
Choose a dataset with binary outcomes
-
Fit GLMs with different link functions (e.g., logit, probit, cloglog)
-
Compare the results and interpret the coefficients
-
Write a report summarizing your findings
Review 'Elements of Statistical Learning'
Show steps
Deepen your understanding of statistical modeling by reviewing a comprehensive textbook.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
-
Obtain a copy of 'Elements of Statistical Learning'
-
Read the chapters corresponding to the course modules
-
Work through the exercises and problems in the book
Build a Website to Predict Customer Churn
Show steps
Apply your skills to create a practical tool that addresses a real-world business problem.
Browse courses on
Predictive Modeling
Show steps
-
Gather data on customer churn
-
Build a model to predict customer churn
-
Deploy the model as a web application
-
Validate the model's performance
Develop a Presentation on Ethical Issues in Statistical Modeling
Show steps
Enhance your understanding and raise awareness about ethical considerations in statistical modeling.
Browse courses on
Ethics in Data Science
Show steps
-
Research ethical issues related to statistical modeling
-
Create a presentation or infographic summarizing your findings
-
Present your work to peers or publish it online
Review 'Data Science from Scratch'
Show steps
Broaden your knowledge of statistical modeling by reviewing a book that covers the same topics in even greater detail.
View
Data Science from Scratch: First Principles...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of 'Data Science from Scratch'
- Read the chapters corresponding to the course modules
- Complete the exercises and activities in the book
Solve Practice Problems on Nonparametric Regression
Show steps
Strengthen your problem-solving skills and reinforce your knowledge of nonparametric regression.
Browse courses on
Nonparametric Regression
Show steps
Show steps
Show steps
- Find practice problems or exercises on nonparametric regression
- Solve the problems and check your answers
- Identify areas where you need more practice
Develop a Tutorial on Poisson Regression
Show steps
Solidify your understanding of Poisson regression by explaining it to others.
Browse courses on
Poisson Regression
Show steps
Show steps
Show steps
- Review the concepts of Poisson regression
- Create a presentation or write a blog post explaining the theory and application of Poisson regression
- Share your tutorial with peers or publish it online
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Tutorials on Generalized Additive Models
Show steps
Expand your knowledge of GAMs by following expert guidance.
Browse courses on
Generalized Additive Models
Show steps
Show steps
Show steps
- Search for tutorials on generalized additive models
- Follow the tutorials step-by-step
- Experiment with the code and explore different examples
Implement a GLM with Different Link Functions
Show steps
Enhance your understanding of GLMs by applying them to real-world problems.
Browse courses on
Generalized Linear Models
Show steps
Show steps
Show steps
- Choose a dataset with binary outcomes
- Fit GLMs with different link functions (e.g., logit, probit, cloglog)
- Compare the results and interpret the coefficients
- Write a report summarizing your findings
Review 'Elements of Statistical Learning'
Show steps
Deepen your understanding of statistical modeling by reviewing a comprehensive textbook.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of 'Elements of Statistical Learning'
- Read the chapters corresponding to the course modules
- Work through the exercises and problems in the book
Build a Website to Predict Customer Churn
Show steps
Apply your skills to create a practical tool that addresses a real-world business problem.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Gather data on customer churn
- Build a model to predict customer churn
- Deploy the model as a web application
- Validate the model's performance
Develop a Presentation on Ethical Issues in Statistical Modeling
Show steps
Enhance your understanding and raise awareness about ethical considerations in statistical modeling.
Browse courses on
Ethics in Data Science
Show steps
Show steps
Show steps
- Research ethical issues related to statistical modeling
- Create a presentation or infographic summarizing your findings
- Present your work to peers or publish it online
Career center
Learners who complete Generalized Linear Models and Nonparametric Regression will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing large datasets to uncover trends and patterns. The skills learned in Generalized Linear Models and Nonparametric Regression would be highly valuable in this role, as they would provide you with the ability to build complex models to extract meaningful insights from data. The course covers techniques such as logistic regression, kernel estimation, and generalized additive models, which are commonly used by Data Analysts to solve real-world problems.
Data Scientist
Data Scientist
Data Scientists use their expertise in statistics, programming, and machine learning to solve complex business problems. The Generalized Linear Models and Nonparametric Regression course would be a valuable addition to your skillset, as it would provide you with the foundation to build and evaluate sophisticated statistical models. The course covers topics such as binomial regression, Poisson regression, kernel estimators, and generalized additive models, which are essential for Data Scientists who want to develop accurate and reliable models.
Statistician
Statistician
Statisticians apply statistical methods to collect, analyze, interpret, and present data. The Generalized Linear Models and Nonparametric Regression course would be a valuable asset to your toolkit, as it would provide you with the skills to analyze complex data and draw meaningful conclusions. The course covers topics such as generalized linear models, binomial regression, Poisson regression, kernel estimators, and generalized additive models, which are essential for Statisticians who want to develop robust and reliable statistical models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. The Generalized Linear Models and Nonparametric Regression course would be a valuable addition to your skillset, as it would provide you with the foundation to build and evaluate sophisticated machine learning models. The course covers topics such as logistic regression, kernel estimation, and generalized additive models, which are commonly used by Machine Learning Engineers to develop accurate and reliable models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and develop trading strategies. The Generalized Linear Models and Nonparametric Regression course would be a valuable addition to your skillset, as it would provide you with the foundation to build and evaluate sophisticated statistical models. The course covers topics such as logistic regression, kernel estimation, and generalized additive models, which are commonly used by Quantitative Analysts to develop accurate and reliable models.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data to understand consumer behavior and market trends. The Generalized Linear Models and Nonparametric Regression course would be a valuable addition to your skillset, as it would provide you with the skills to analyze complex data and draw meaningful conclusions. The course covers topics such as generalized linear models, binomial regression, Poisson regression, kernel estimators, and generalized additive models, which are essential for Market Researchers who want to develop robust and reliable statistical models.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology and medicine. The Generalized Linear Models and Nonparametric Regression course would be a valuable addition to your skillset, as it would provide you with the skills to analyze complex data and draw meaningful conclusions. The course covers topics such as generalized linear models, binomial regression, Poisson regression, kernel estimators, and generalized additive models, which are essential for Biostatisticians who want to develop robust and reliable statistical models.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in human populations. The Generalized Linear Models and Nonparametric Regression course would be a valuable addition to your skillset, as it would provide you with the skills to analyze complex data and draw meaningful conclusions. The course covers topics such as generalized linear models, binomial regression, Poisson regression, kernel estimators, and generalized additive models, which are essential for Epidemiologists who want to develop robust and reliable statistical models.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. The Generalized Linear Models and Nonparametric Regression course would be a valuable addition to your skillset, as it would provide you with the foundation to build and evaluate sophisticated statistical models. The course covers topics such as logistic regression, kernel estimation, and generalized additive models, which are commonly used by Actuaries to develop accurate and reliable models.
Risk Analyst
Risk Analyst
Risk Analysts use statistical methods to assess and mitigate risk. The Generalized Linear Models and Nonparametric Regression course would be a valuable addition to your skillset, as it would provide you with the skills to analyze complex data and draw meaningful conclusions. The course covers topics such as generalized linear models, binomial regression, Poisson regression, kernel estimators, and generalized additive models, which are essential for Risk Analysts who want to develop robust and reliable statistical models.
Financial Analyst
Financial Analyst
Financial Analysts use statistical methods to analyze financial data and make investment recommendations. The Generalized Linear Models and Nonparametric Regression course would be a valuable addition to your skillset, as it would provide you with the foundation to build and evaluate sophisticated statistical models. The course covers topics such as logistic regression, kernel estimation, and generalized additive models, which are commonly used by Financial Analysts to develop accurate and reliable models.
Data Mining Analyst
Data Mining Analyst
Data Mining Analysts use statistical methods to extract patterns and insights from large datasets. The Generalized Linear Models and Nonparametric Regression course would be a valuable addition to your skillset, as it would provide you with the skills to analyze complex data and draw meaningful conclusions. The course covers topics such as generalized linear models, binomial regression, Poisson regression, kernel estimators, and generalized additive models, which are essential for Data Mining Analysts who want to develop robust and reliable statistical models.
Business Analyst
Business Analyst
Business Analysts use statistical methods to analyze business data and make recommendations. The Generalized Linear Models and Nonparametric Regression course would be a valuable addition to your skillset, as it would provide you with the foundation to build and evaluate sophisticated statistical models. The course covers topics such as logistic regression, kernel estimation, and generalized additive models, which are commonly used by Business Analysts to develop accurate and reliable models.
Consultant
Consultant
Consultants use statistical methods to solve problems for clients in a variety of industries. The Generalized Linear Models and Nonparametric Regression course may be useful in this role, as it would provide you with the skills to analyze complex data and draw meaningful conclusions. The course covers topics such as generalized linear models, binomial regression, Poisson regression, kernel estimators, and generalized additive models.
Teacher
Teacher
Teachers use statistical methods to teach students about math and science. The Generalized Linear Models and Nonparametric Regression course may be useful in this role, as it would provide you with the skills to analyze complex data and draw meaningful conclusions. The course covers topics such as generalized linear models, binomial regression, Poisson regression, kernel estimators, and generalized additive models.
For more career information including salaries, visit:
OpenCourser.com/course/fwsu0h/generalized
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Generalized Linear Models and Nonparametric Regression.
Comprehensive treatment of statistics from a Bayesian perspective. It provides a detailed discussion of a variety of statistical methods, including GLMs, nonparametric regression models, and GAMs.
Classic text on generalized linear models (GLMs). It provides a comprehensive treatment of the theory and application of GLMs, including a detailed discussion of the binomial regression model.
Comprehensive treatment of nonparametric regression methods. It provides a detailed discussion of the theory and application of nonparametric regression methods, including a variety of real-world examples.
Comprehensive treatment of spline smoothing methods. It provides a detailed discussion of the theory and application of spline smoothing methods, including a variety of real-world examples.
Comprehensive treatment of generalized additive models (GAMs). It provides a detailed discussion of the theory and application of GAMs, including a variety of real-world examples.
Comprehensive treatment of statistical modeling. It provides a unified perspective on statistical modeling, including a discussion of GLMs, nonparametric regression models, and GAMs.
Comprehensive treatment of statistical learning methods. It provides a detailed discussion of a variety of statistical learning methods, including GLMs, nonparametric regression models, and GAMs.
Comprehensive treatment of logistic regression models. It provides a detailed discussion of the theory and application of logistic regression models, including a variety of real-world examples.
Comprehensive treatment of regression modeling with a focus on actuarial and financial applications. It provides a detailed discussion of a variety of regression models, including GLMs, nonparametric regression models, and GAMs.
Save
Comprehensive review of statistical software. It provides a detailed discussion of a variety of statistical software packages, including R, SAS, and Stata.
Comprehensive tour of R programming. It provides a detailed discussion of a variety of R programming topics, including data manipulation, statistical modeling, and graphics.
Comprehensive treatment of modern applied statistics with a focus on S-PLUS. It provides a detailed discussion of a variety of statistical methods, including GLMs, nonparametric regression models, and GAMs.
More accessible introduction to GLMs than Nelder and Wedderburn's book. It provides a clear and concise overview of the theory and application of GLMs, with a focus on practical examples.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fwsu0h/generalized
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Rating
4.2
Effort
Approx. 42 hours
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Brian Zaharatos
Language
English
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This course belongs to a
collection
called
Statistical Modeling for Data Science Applications. It's comprised of three courses:
- Generalized Linear Models and Nonparametric Regression (viewing)
- ANOVA and Experimental Design
- Modern Regression Analysis in R
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