Predictive Analytics

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Description:

Predictive analytics describes a range of analytical and statistical techniques used for developing models that may be used to predict future events or behaviors. There are different forms of predictive models, which vary based on the event or behavior that is being predicted. Nearly all predictive models produce a score; a higher score indicates that a given event or behavior is very likely to occur.

Predictive analytics, along with data mining techniques and predictive models, relies on multivariate analyzing techniques, including time-series or advanced regression models. These techniques allow organizations to decide on relationships and trends and predict future behaviors or events.
See Alsohttp://www.techopedia.com/definition/180/predictive-analytics

Projects:
DTDI Project LogoDeep Time Data Infrastructure (DTDI)
Principal Investigator: Peter Fox
Description: Earth’s living and non-living components have co-evolved for 4 billion years through numerous positive and negative feedbacks. Yet our ability to document, model, and explore these complex intertwined changes has been hampered by a lack of data synthesis and integration from many complementary disciplines—mineralogy, petrology, paleobiology, geochronology, proteomics, geochemistry, and more. The rise of oxygen exemplifies the co-evolution of rocks and life, and underscores both the tantalizing opportunities and technical challenges of deciphering transient characteristics of Earth’s storied past.
MBVL Project LogoMarine Biodiversity Virtual Laboratory (MBVL)
Principal Investigator: Heidi Sosik, Stace Beaulieu, David Mark Welch, and Peter Fox
Description: This research effort brings together computational and information scientists, oceanographers and microbiologists to develop a Marine Biodiversity Virtual Laboratory (MBVL). In addition to research investigations of marine ecosystems, the Virtual Laboratory provides a platform for education via student diversity programs at the three institutions. The important learning opportunities will be two-fold for students: (1) to learn about, model, and make predictions for biodiversity in natural systems, and (2) to be exposed to working in an interdisciplinary team that includes both natural scientists and computer scientists.