big_data_presentation.pdf
Transcript of big_data_presentation.pdf
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Big DataMasters Degree in Informatics Engineering
Masters Programme in ICT Innovation: Data Science (EIT ICT Labs Master School)Academic year 2015-2106
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Calendar
Monday: 18-20 Classroom 6101 Thursday: 19-21 Classroom 6302 Period: September 15 January 16 (16 weeks) Exams:
January 20, 2015 (Tuesday) at 6 pm. Room 6206 July 6, 2015 (Monday) at 6 pm. Room 6206
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Requirements
Pre-requirements: Database management Interactive systems:
Integration of user-centered design in the development process Sciences and Engineering Computing
ICT-LABs: According to general prerequisites for ICT KIC master programs this is the first course for
enrolled students in the DS Master Degree. Students should have finished their Degree Project and also participated in the Initial Week.
Co-requirements: Ecosystems design for cloud computing and big data
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Learning outcomes
RA1. Be capable of processing and analyzing massive data RA2. Be acquainted with visual analytics techniques RA3. Acquainted with how to apply computational data analysis
techniques in some specific field of science or engineering
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Indicators of achievement
T1. Know the methods that allow to perform big data analysis (THEORY)
T2. Be capable to design and implement prototypes for interactive data analysis of big data (PRACTICE)
T3. Apply interactive techniques to big data analysis in different fields of science and engineering (PRACTICE)
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Teaching resources
Subject website: http://laurel.datsi.fi.upm.es/docencia/asignaturas/bd Slides, wordings, calendar, tutorials, news, links of interestUpdate frequently!
Teaching material: books, papers, web Own laptops installing free distribution tools for the practices
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Course structure
Mandatory assistance Theory classes ( 2 h/week approx.) Practice classes: individual work at the classroom ( 2 h/week
approx.) Using tools Developing prototypes
Individual work out of classroom: 6 h/week Deadlines
http://laurel.datsi.fi.upm.es/docencia/asignaturas/bd Continuous work
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Contents
1. Introduction and fundaments2. Data storage
Practice 13. Data analysis
Practice 24. Information visualization
Practice 3
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Examination
Regular session (January): Theory exam: 20% Practice works: 80%
Extra session (July): Delivery of practice works 15 days before the exam date
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Detailed calendar
Temas
IF Introduccin y fundamentos
DS Data Storage
DA Data AnalysisVI VisualizacinPDS Prctica de DSPDA Prctica de DAPVI Prctica de VI
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References Data mining: concepts and techniques 2 edicin, J. Han, M. Kamber, 2006 Introduction to data mining P.-N. Tan, M. Steinbach, V. Kumar, 2005 Data mining: Practical Machine Learning Tools and Techniques, 2nd Ed., I.
Witten, E. Frank, 2005 Data mining: Practical Machine Learning Tools and Techniques, 3rd Ed., I.
Witten, E. Frank, M. Hall, 20011 Mastering the information age. Solving problems with visual analytics, D. Keim,
J. Kholhammer, G. Ellis, F. Mansmann, 2010 Interactive data visualization: foundations, techniques, and application, M.
Ward, G.G. Grinstein, D. Keim, 2010 Designing the user interface: strategies for effective human, B. Shneiderman, C.
Plaisant, M. Cohen, S. Jacobs, 2010
Big DataCalendarRequirementsLearning outcomesIndicators of achievementTeaching resourcesCourse structureContentsExaminationDetailed calendarReferences