Durham
Engineering&Physical Sciences :: Mathematics&Statistics
MATH 838 (01) - Data Mining and Predictive Analytics
Data Mining & Pred Analytics
Credits: 3.0
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Class Size:
10
CRN: 52728
CRN: 52728
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.
Cross listed with : MATH 738.01
Instructors: Philip Ramsey
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
1/21/2025 | 5/5/2025 | MW | 12:40pm - 2:00pm | KING S320 |