Big DataMasters Degree in Informatics Engineering

Masters Programme in ICT Innovation: Data Science (EIT ICT Labs Master School)Academic year 2015-2106

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

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

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

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)

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

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

Contents

1. Introduction and fundaments2. Data storage

Practice 13. Data analysis

Practice 24. Information visualization

Practice 3

Examination

Regular session (January): Theory exam: 20% Practice works: 80%

Extra session (July): Delivery of practice works 15 days before the exam date

Detailed calendar

Temas

IF Introduccin y fundamentos

DS Data Storage

DA Data AnalysisVI VisualizacinPDS Prctica de DSPDA Prctica de DAPVI Prctica de VI

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