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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)

Articles

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A Data Driven Approach to Forecast Demand
Hannah Kosinovsky, Sita Daggubati, Kumar Ramasundaram, and Brent Allen

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Achieving Optimal Horizontal Drill Operations
Daniel J. Serna, James Vasquez, and Donald Markley

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A Data Science Approach to Defining a Data Scientist
Andy Ho, An Nguyen, Jodi L. Pafford, and Robert Slater

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Identifying Customer Churn in After-market Operations using Machine Learning Algorithms
Vitaly Briker, Richard Farrow, William Trevino, and Brent Allen

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Identifying At-Risk Clients for XYZ Packaging, Co.
Eduardo Carlos Cantu Medellin, Mihir Parikh, Christopher Graves, and Brendon Jones

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Analyzing Influences on U.S. Baby Name Trends
Laura Ludwig, Mallory Hightower, Daniel W. Engels, and Monnie McGee

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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

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Quantitative Model for Setting Manufacturer's Suggested Retail Price
Peter Byrd, Jonathan Knowles, Dmitry Andreev, Jacob Turner, Brian Mente, and LaRoux Wallace