MATH 738 (1SY) - Data Mining and Predictive Analytics

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
Class Size:   20  
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.
You must sign up in the Dept Office before registering 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