Kara Karpman is an Adjunct Assistant Professor of Data Science and Statistics at Cornell University, as well as an Assistant Professor of Mathematics at Middlebury College in Middlebury, VT. Her research focuses on statistical modeling techniques for studying biological and financial data. Professor Karpman holds a B.S. in Mathematics from Duke University and an M.S. and Ph.D. in Applied Mathematics from Cornell University.
In this course, you will examine classification questions then use summarization and visualization to obtain preliminary answers to those questions. While summarizing and visualizing data can be extremely helpful for classification problems, logistic regression is a more rigorous way to answer those questions, so you will practice performing and interpreting logistic regression. You will also use logistic regression to evaluate which variables improve your model, make predictions, and evaluate those predictions.
System requirements: This course contains a virtual programming environment that does not support the use of Safari, Edge, tablets, or mobile devices. Please use Chrome, Firefox, or Internet Explorer on a computer for this course.
The following courses are required to be completed before taking this course:
- Exploring Data Sets With R
- Summarizing and Visualizing Data
- Measuring Relationships and Uncertainty
- Data Cleaning With the Tidyverse
Key Course Takeaways
- Summarize and visualize data to examine classification questions
- Use logistic regression to answer classification questions
- Evaluate models built with logistic regression
How It Works
Who Should Enroll
- Current and aspiring data scientists and analysts
- Business decision makers
- Marketing analysts
- Anyone seeking to gain deeper exposure to data science