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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20260522T150117Z
LOCATION:C154
DTSTART;TZID=America/Chicago:20181112T083000
DTEND;TZID=America/Chicago:20181112T120000
UID:submissions.supercomputing.org_SC18_sess261_tut140@linklings.com
SUMMARY:Introduction of Practical Approaches to Data Analytics for HPC wit
 h Spark
DESCRIPTION:Michela Taufer (University of Tennessee); Travis Johnston (Oak
  Ridge National Laboratory); Stephen Herbein (Lawrence Livermore National 
 Laboratory, University of Tennessee); Danny Rorabaugh (University of Tenne
 ssee); and Michael Wyatt and Dylan Chapp (University of Delaware, Universi
 ty of Tennessee)\n\nThis tutorial provides a practical introduction to big
  data analytics, blending theory (e.g., of clustering algorithms and techn
 iques for dealing with noisy data) and practice (e.g., using Apache Spark,
  Jupyter Notebooks, and Github). Over the course of five modules, particip
 ants will become familiar with modern data science methods, gain comfort w
 ith the tools of the trade, explore real-world data sets, and leverage the
  power of HPC resources to extract insights from data. Upon completing the
  tutorial, participants will have: used Jupyter notebooks to create reprod
 ucible, explanatory data science workflows; learned a modern MapReduce imp
 lementation, Apache Spark; implemented parallel clustering methods in Spar
 k; studied strategies for overcoming the common imperfections in real-worl
 d datasets, and applied their new skills to extract insights from a high-d
 imensional medical dataset.\n\nTag: Data Analytics, Introductory, Parallel
  Programming Languages, Libraries, and Models, Tools\n\nRegistration Categ
 ory: Tutorial Reg Pass\n\n
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