Top/Pred Analy: Regress Model
Durham
Paul College of Business&Econ :: Decision Sciences
Credits: 4.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:
30
CRN: 55227
CRN: 55227
Introduces students to commonly used predictive analytics techniques and necessary programming with focus on regression analysis and model building. The course coverage is supported with real data applications and illustrations. The topics include linear and non-linear regression model building/selection, residual analysis, search algorithms, generalized linear models/classification, and clustering algorithms.
Section Comments: Full Title: Top/Predictive Analytics: Regression Model Also listed as DS 898.01
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Repeat Rule: May be repeated for a maximum of 8 credits.
Only listed classes in section: Junior, Senior
Instructors: Gorkem Turgut (G.T.) Ozer
Times & Locations
| Start Date | End Date | Days | Time | Location |
|---|---|---|---|---|
| 1/24/2023 | 5/8/2023 | M | 5:10pm - 8:00pm | PCBE 215 |
Additional Course Details:
2022-2023 Update: This course covers applied machine learning methods such as logistic regression, random forests, and XGBoost with a focus on solving actual business problems using as recent as 2022 data from companies such as Airbnb, Walmart, Carvana, Spotify, and Boston Bluebikes. The course also introduces artificial intelligence for business use by briefly covering deep learning and neural networks.