MATH 738 (01) - Data Mining and Predictive Analytics

Data Mining & Pred Analytics

Durham   Engineering&Physical Sciences :: Mathematics&Statistics
Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 53387
An introduction to supervised and unsupervised methods for exploring large data sets and developing predictive models. Unsupervised methods include: market basket analysis, principal components, clustering, and variables clustering. Important statistical and machine learning methods (supervised learning) include: Classification and Regression Trees (CART), Random Forests, Neural Nets, Support Vector Machines, Logistics Regression and Penalized Regression. Additional topics focus on metamodeling, validation strategies, bagging and boosting to improve prediction or classification, and ensemble prediction from a set of diverse models. Required case studies and projects provide students with experience in applying these techniques and strategies. The course necessarily involves the use of statistical software and programming languages. Undergraduate students are required to have junior or senior status to in enroll in this course.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): MATH 539 or MATH 644
Cross listed with : MATH 838.01
Mutual Exclusion : IT 630
Classes not allowed in section: Freshman, Sophomore
Instructors: Philip Ramsey

Times & Locations

Start Date End Date Days Time Location
1/23/2024 5/6/2024 MW 12:40pm - 2:00pm KING S320