MATH 838 (1SY) - Data Mining and Predictive Analytics

Data Mining & Pred Analytics

Durham   Engineering&Physical Sciences :: Mathematics&Statistics
Credits: 3.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 52533
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 Tress (CART), Random Forests, Neural Nets, Support Vector Machines, Logistic 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. Students must have completed a calculus-based introductory statistics course.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Attributes: Scheduled meeting time, Online (no campus visits), EUNH
Instructors: Philip Ramsey

Times & Locations

Start Date End Date Days Time Location
1/24/2023 5/8/2023 MW 12:40pm - 2:00pm ONLINE