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May 05, 2024
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ACS 3420 - Linear Models and Predictive Analytics3 lecture hours 0 lab hours 3 credits
Course Description
This course is designed for actuarial science majors to provide them with a solid statistical foundation. It will introduce the theory and practical application of linear models and predictive analytics techniques commonly used for insurance modeling work. In particular, this course will cover generalized linear models, data visualization, principal component analysis, and decision tree. This course meets the following Raider Core CLO requirement: Integrate Learning. (prereq: MTH 2130 and ACS 3410 or Actuarial Science program director consent) (quarter system prereq: MA 2323 and MA 2411 or Actuarial Science program director consent)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Employ fundamental exploratory data analysis on data
- Use appropriate linear models such as generalized linear models and generalized additive models for analyzing the data
- Understand the key concepts of dimension reduction using principal components analysis
- Apply predictive analytics techniques on real-life problems
- Use R or other statistical software to solve problems in topics covered in this course
- Interpret results for various linear models and predictive analytics
Prerequisites by Topic
- Multivariable calculus
- Probability theory and application
- Statistics
Course Topics
- Explanatory data analysis
- Generalized linear model
- Generalized additive models
- Principle component analysis
- Penalized regression
- Decision tree and cluster analysis
Coordinator
Dr. Won Chul Song
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