Natural Language Processing: NLP tools and tricks used in data science
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Dr. Scott Schwartz is an instructor in the Galvanize Data Science Immersive program at Galvanize SoHo location in downtown New York City. Dr. Schwartz received a BS and BA summa cum laude in Computer Science and Mathematics from Trinity University, and his PhD in Statistics at Duke University. After a postdoctoral fellowship in the program for bioinformatics and nutrition and Texas A&M University, he worked for several years at the Texas A&M AgriLife next generation sequencing core supporting both academic and agribusiness research programs, and then moved to the University of Texas to work on biofuels crops for two years before joining galvanizes instructional staff in Austin, Texas, in early 2016. Dr. Schwartz has since moved to Galvanizes NYC campus.
Always application and data driven, Dr. Schwartz moved from “dry” theoretical statistical methodology work into the domains of nutrition and genetics, eventually pursuing work in agricultural and biofuels crop populations because the problems and questions being addressed in these application areas were among the most cutting edge and exciting around. This was largely driven by the recent advent of incredible advances in molecular genetics which provided an unprecedented ability to characterize the genetic composition of any individual in any species. In a very analogous manner, the recent advent and availability of tremendous quantities of unstructured data, specifically text-data, has led to an explosion of interest within the domain of data science to leverage this data for insight and value for nearly every application area imaginable. The analysis of textual data in this manner has come to be known as Natural Language Processing, or NLP for short, and this talk with introduce the basics of NLP as utilized within the domain of data science.