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DTSTAMP:20260628T044641Z
UID:68cdc794-c20b-486d-a903-70f827fa5cd0
DTSTART:20141215T080000Z
DTEND:20141216T170000Z
DESCRIPTION:\n	Description\n\n	The increasing amount of scientific data bei
 ng collected through sensors or computational simulations may take advanta
 ge of new analytics techniques for being processed in order to extract new
  meanings out of raw data. The purpose of this workshop is to present scie
 ntists tools and techniques\, open issues\, recent developments\, applicat
 ions and enhancements for MapReduce\, and similar systems. Over the years\
 , MapReduce has become one of the main programming models of choice for pr
 ocessing large data sets. Although it was originally developed for process
 ing web information\, the technique has gained a lot of attention from the
  scientific community for its applicability in large parallel data analysi
 s. Participants will learn how to combine tools and techniques from statis
 tics and computer science to solve their problems more efficiently. The co
 urse will consist of introductory lectures held by guest data-analyst expe
 rts\, and hands-on sessions.\n\n	 \n\n	Target Audience\n\n	Students\, PhD
 \, and researchers in computational sciences and scientific areas with dif
 ferent backgrounds\, looking for new technologies and methods to process a
 nd analyse large amount of data. Participants must have basic knowledge in
  programming with Python.\n\n	 \n\n	Topics\n\n	● Basic principles of Py
 thon\, MapReduce\, and technologies like Hadoop and Spark.\n\n	● Basic u
 nderstandings for problem analysis and optimization.\n\n	● Project desig
 n and strategies for building a scalable data analysis application.\n\n	 
 \n\n	About half of the course will consist of practical hands-on sessions.
  The programme will include one invited talk from a guest speaker working 
 in the field.\n\n	 \n\n	Benefits\n\n	After the course the participants sh
 ould be able to work with Hadoop and related libraries\, writing applicati
 on in Python with the basic features to execute and optimize on the descri
 bed system. \n\n	 \n\n	By the end of this course students should be able
  to:\n\n	● understand the MapReduce algorithm\n\n	● run a Python MapRe
 duce program\n\n	● improve development skills on Python language\n\n	●
  improve their prospects when submitting project applications for requesti
 ng resources from providers such as PRACE or other agencies.\n\n	 \n\n	Lo
 ng description\n\n	Data deluge is a main focus problem for data analytics.
  Big Data is real and spreading over all the data fields: science\, commer
 ce and any other information-handling activity.  \n\n	Many solutions have
  been proposed by the most advanced data-oriented companies (e.g. Google\,
  Amazon\, Yahoo\, etc.) and some open-source projects have reached a level
  of maturity high enough to be integrated into your own data cluster or in
 frastructure. This workshop is oriented to any person with development ski
 lls and expertise on UNIX systems which would like to explore the new para
 digms and forefront technologies available in the data analytics field.\n\
 n	 \n\n	The agenda includes two days of sessions. Solving Big Data proble
 ms requests at first understanding what the new challenges and the real ba
 ckground of data analytics are. For this reason the first session gives an
  introduction and basic definitions of the arguments. As Python is recogni
 zed to be one of the most powerful high-level programming languages availa
 ble for data science\, it will be used for hands on and examples.\n\n	 \n
 \n	Running data analytics collaboratively for processing Big Data requires
  the knowledge of MapReduce algorithm and its most famous implementation A
 pache Hadoop. The second day of the workshop aims to introduce real Python
  implementations of MapReduce examples. Also YARN  and HARP  will be des
 cribed as fundamental bricks of the basic stack of a Big Data application.
 \n\n	 \n\n	Case studies covering different scientific fields\, including 
 Genomic and Bioinformatics\, will be presented and further discussed.Grant
 \n		The lunch for the two days will be offered to all the participants and
  some grants are available.\n	\n		The only requirement to be eligible is t
 o be not funded by your institution to attend the course and to work in an
  institute outside the Bologna area.\n	\n		The grant  will be 200 euros f
 or students working outside Italy and 100 euros for students working in It
 aly.\n	\n		 \n	\n		Some documentation will be required and the grant will
  be paid only after a certified presence of minimum 80% of the lessons.\n	
 	Further information about how to request the grant\, at the confirmation 
 of the course: about 3 weeks before the starting date.\n	\n		WARNING: UNFO
 RTUNATELY WE REACHED THE MAXIMUM OF STUDENTS ADMITTED\, PLEASE WRITE TO TH
 E SUPPORT TO BE ADDED IN THE RESERVE LIST. \n\n\n	 \n\nhttps://events.pr
 ace-ri.eu/event/314/
SUMMARY:Tools and techniques for massive data analysis@CINECA
URL;VALUE=URI:https://events.prace-ri.eu/event/314/
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