The SMU Data Science Review is a repository of SMU Masters student work in the field of data science as well as an 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 7, Number 2 (2023)
Traditional vs Machine Learning Approaches: A Comparison of Time Series Modeling Methods
Miguel E. Bonilla Jr., Jason McDonald, Tamas Toth, and Bivin Sadler
Using Geographic Information to Explore Player-Specific Movement and its Effects on Play Success in the NFL
Hayley Horn, Eric Laigaie, Alexander Lopez, and Shravan Reddy
Static Malware Family Clustering via Structural and Functional Characteristics
David George, Andre Mauldin, Josh Mitchell, Sufiyan Mohammed, and Robert Slater
Forecasting Accessory Demand in the Automotive Industry
Eric Cadena, Kevin Albright, Harry Wang, and Satvik Ajmera