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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Data Scientist’S Analysis Toolbox: Comparison Of Python, R, And Sas Performance, Jim Brittain, Mariana Cendon, Jennifer Nizzi, John Pleis
Data Scientist’S Analysis Toolbox: Comparison Of Python, R, And Sas Performance, Jim Brittain, Mariana Cendon, Jennifer Nizzi, John Pleis
SMU Data Science Review
A quantitative analysis will be performed on experiments utilizing three different tools used for Data Science. The analysis will include replication of analysis along with comparisons of code length, output, and results. Qualitative data will supplement the quantitative findings. The conclusion will provide data support guidance on the correct tool to use for common situations in the field of Data Science.
Seismology And Volcanology: Exploration Of Volcanoes, Long-Periods, And Machines - Predicting Volcano Eruption Using Signature Seismic Data, Kyle Killion, Rajeev Kumar, Celia J. Taylor, Gabriele Morra
Seismology And Volcanology: Exploration Of Volcanoes, Long-Periods, And Machines - Predicting Volcano Eruption Using Signature Seismic Data, Kyle Killion, Rajeev Kumar, Celia J. Taylor, Gabriele Morra
SMU Data Science Review
Abstract. Seismo-volcanologists manually isolate and verify long-period waves and Strombolian events using seismic and acoustic waves. This is a very detailed and time-consuming process. This project is to employ machine learning algorithms to find models which locate long-period and Strombolian signatures automatically. By comparing the timing of seismic and acoustic waves, clustering techniques effectively isolated big volcanic events and aided in the further refinement of techniques to capture the hundreds of typical daily Strombolian events at Villarrica volcano. Within the research, we utilized the unsupervised machine learning environment to locate a group of signatures for customizing machine learned long-period signature …