The SMU Data Science Review is a peer-reviewed electronic journal that promotes data-driven scientific discovery and welcomes experimental and theoretical research on advanced data science technologies and their real world applications.
See the Aims and Scope for a complete coverage of the journal.
Current Issue: Volume 2, Number 3 (2019)
A Data Driven Approach to Forecast Demand
Hannah Kosinovsky, Sita Daggubati, Kumar Ramasundaram, and Brent Allen
Predict Missed Ship Date Occurrence and Determine Root Cause of Missed Ship Dates for XYZ Furniture Company
Selwyn Samuel, Shon Mohsin, and Rida Moustafa
A Data Science Approach to Defining a Data Scientist
Andy Ho, An Nguyen, Jodi L. Pafford, and Robert Slater
Personalized Detection of Anxiety Provoking News Events using Semantic Network Analysis
Jacquelyn Cheun PhD, Luay Dajani, and Quentin B. Thomas
Identifying Customer Churn in After-market Operations using Machine Learning Algorithms
Vitaly Briker, Richard Farrow, William Trevino, and Brent Allen
Identifying At-Risk Clients for XYZ Packaging, Co.
Eduardo Carlos Cantu Medellin, Mihir Parikh, Christopher Graves, and Brendon Jones
Analyzing Influences on U.S. Baby Name Trends
Laura Ludwig, Mallory Hightower, Daniel W. Engels, and Monnie McGee
Machine Learning and Deep Learning Applications for International Ocean Discovery Program Geoscience Research
Brandon De La Houssaye, Peter Flaming, Quinton Nixon, and Gary Acton
Increasing Robustness in Long Text Classifications Using Background Corpus Knowledge for Token Selection.
Clovis R. Bass, Brett Benefield, Debbie Horn, and Rebecca Morones
Mapping Relationships and Positions of Objects in Images Using Mask and Bounding Box Data
Jaime M. Villanueva Jr, Anantharam Subramanian, Vishal Ahir, and Andrew Pollock
Quantitative Model for Setting Manufacturer's Suggested Retail Price
Peter Byrd, Jonathan Knowles, Dmitry Andreev, Jacob Turner, Brian Mente, and LaRoux Wallace