Data Mining & Pred Analytics
Durham
Engineering&Physical Sciences :: Mathematics&Statistics
Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 4.0
Term: Spring 2022 - Full Term (01/25/2022 - 05/09/2022)
Grade Mode: Letter Grading
Term: Spring 2022 - Full Term (01/25/2022 - 05/09/2022)
Grade Mode: Letter Grading
Class Size:
20
CRN: 52825
CRN: 52825
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. Prereq: MATH 539 (or MATH 644); or permission.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Mutual Exclusion : IT 630
Classes not allowed in section: Freshman, Sophomore
Instructors: Philip Ramsey
Times & Locations
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
1/25/2022 | 5/9/2022 | MW | 12:40pm - 2:00pm | ONLINE |