BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20181221T160907Z
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:Tutorial\nData Analytics, Introductory, Parallel Programming L
 anguages, Libraries, and Models, Tools, Tutorial Reg Pass\n\nIntroduction 
 of Practical Approaches to Data Analytics for HPC with Spark\n\nTaufer, Jo
 hnston, Herbein, Rorabaugh, Wyatt...\n\nThis tutorial provides a practical
  introduction to big data analytics, blending theory (e.g., of clustering 
 algorithms and techniques for dealing with noisy data) and practice (e.g.,
  using Apache Spark, Jupyter Notebooks, and Github). Over the course of fi
 ve modules, participants will become familiar with modern data science met
 hods, gain comfort with the tools of the trade, explore real-world data se
 ts, and leverage the power of HPC resources to extract insights from data.
  Upon completing the tutorial, participants will have: used Jupyter notebo
 oks to create reproducible, explanatory data science workflows; learned a 
 modern MapReduce implementation, Apache Spark; implemented parallel cluste
 ring methods in Spark; studied strategies for overcoming the common imperf
 ections in real-world datasets, and applied their new skills to extract in
 sights from a high-dimensional medical dataset.
URL:https://sc18.supercomputing.org/presentation/?id=tut140&sess=sess261
END:VEVENT
END:VCALENDAR