Data Modeling and Prediction with R
Master linear and logistic regression in R. Build models, quantify uncertainty, and make data-driven predictions.
Master linear and logistic regression in R. Build models, quantify uncertainty, and make data-driven predictions.
Learn to move from exploring data to modeling it with confidence in this comprehensive R programming course. Build and interpret linear and logistic regression models to uncover relationships, make predictions, and quantify uncertainty. The course covers simple and multiple linear regression, logistic regression for categorical outcomes, and methods for communicating uncertainty through bootstrapping and hypothesis testing. Designed by Duke University experts, this course provides hands-on experience with real-world datasets including fish modeling, loan interest rates, spam filtering, and forest classification. You'll master the RACCCA framework, learn to evaluate model performance, recognize limitations like overfitting, and develop skills to communicate findings transparently. Perfect for learners seeking foundational data science skills in R, this course emphasizes practical application and prepares you to draw clear, data-driven conclusions from complex datasets.
(7,818 ratings)
Instructors:
English
Powered by
What you'll learn
Fit and interpret linear and logistic regression models to examine relationships between predictors and outcomes
Evaluate model performance and recognize limitations such as overfitting
Apply bootstrapping and hypothesis testing to quantify and communicate uncertainty in model results
Develop skills in simple and multiple linear regression with numerical and categorical predictors
Master logistic regression for modeling categorical outcomes and classification tasks
Learn to interpret coefficients, visualize patterns, and make predictions from regression models
Skills you'll gain
This course includes:
4.1 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This course provides comprehensive training in data modeling and prediction using R programming. Students begin with simple linear regression to describe relationships between variables, learning to fit models, interpret coefficients, and visualize patterns. The curriculum advances to multiple linear regression with multiple predictors and interaction effects, teaching how to improve model accuracy while avoiding overfitting. Learners explore logistic regression for modeling categorical outcomes like binary classification problems, discovering how to calculate probabilities and assess model performance. The final module focuses on quantifying and communicating uncertainty through bootstrapping and randomization methods, hypothesis testing, and transparent communication of findings. Throughout the course, participants work with real-world datasets and gain practical experience in making data-driven conclusions.
Building and Interpreting Simple Linear Models
Module 1 · 3 Hours to complete
Expanding to Multiple Linear Regression
Module 2 · 2 Hours to complete
Modeling Categorical Outcomes with Logistic Regression
Module 3 · 3 Hours to complete
Quantifying and Communicating Uncertainty
Module 4 · 2 Hours to complete
Fee Structure
Instructors
Associate Professor of the Practice at Duke University
Dr. Mine Çetinkaya-Rundel is an Associate Professor of the Practice in the Department of Statistical Science at Duke University. She earned her Ph.D. in Statistics from the University of California, Los Angeles, and holds a B.S. in Actuarial Science from New York University's Stern School of Business. Dr. Çetinkaya-Rundel is dedicated to innovative statistics pedagogy, focusing on developing student-centered learning tools for introductory statistics courses. Her recent work emphasizes teaching computation at the introductory level with a strong commitment to reproducibility and addressing the gender gap in self-efficacy within STEM fields. Additionally, her research interests include spatial modeling of survey, public health, and environmental data. She is a co-author of OpenIntro Statistics and actively contributes to the OpenIntro project, which aims to create open-licensed educational materials that reduce barriers to education. Dr. Çetinkaya-Rundel also co-edits the Citizen Statistician blog and contributes to the "Taking a Chance in the Classroom" column in Chance Magazine.
Assistant Teaching Professor at Duke University
Dr. Elijah Meyer is an Assistant Teaching Professor in the Department of Statistical Science at Duke University, where he focuses on enhancing the teaching and learning experiences in statistics and data science. He aims to inspire students to discover their passion for working with data through innovative course creation, curriculum development, and instrument development. Dr. Meyer has a keen interest in sports analytics and enjoys playing basketball, tennis, and disc golf in his spare time. He earned both his Master's and Ph.D. in Statistics with a focus on education from Montana State University. Recently, he transitioned to North Carolina State University after completing a postdoctoral position at Duke University, where he continued his work in statistics education and data science pedagogy. For more information about his teaching and research, you can visit his personal website.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.