First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
COMPUTER SCIENCE
Course unit
DATA MINING
SC01111799, A.A. 2015/16

Information concerning the students who enrolled in A.Y. 2015/16

Information on the course unit
Degree course Second cycle degree in
COMPUTER SCIENCE
SC1176, Degree course structure A.Y. 2014/15, A.Y. 2015/16
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination DATA MINING
Website of the academic structure http://informatica.scienze.unipd.it/2015/laurea_magistrale
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge BRUNO SCARPA SECS-S/01

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 6.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Laboratory 2.0 16 34.0 No turn
Lecture 4.0 34 66.0 No turn

Calendar
Start of activities 01/03/2016
End of activities 15/06/2016
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
8 a.a. 2018/2019 01/10/2018 28/02/2020 CATTELAN MANUELA (Presidente)
SCARPA BRUNO (Membro Effettivo)
BRESOLIN DAVIDE (Supplente)
CRAFA SILVIA (Supplente)
SPERDUTI ALESSANDRO (Supplente)
7 a.a. 2017/2018 01/10/2017 28/02/2019 GUOLO ANNAMARIA (Presidente)
BRESOLIN DAVIDE (Membro Effettivo)
CRAFA SILVIA (Membro Effettivo)
SCARPA BRUNO (Membro Effettivo)
SPERDUTI ALESSANDRO (Membro Effettivo)

Syllabus
Prerequisites: Basic knowledge of computer science, Databases
Target skills and knowledge: The course will provide an overview of concepts and advanced methods and tools for analysis of large amounts of data, often used as a support to the business decision process.
Examination methods: Written/Practice (possibly with a project)
Assessment criteria: The exam will measure (a) the notions learnt by each student and (b) to what extent he is able to apply what she learnt.
Course unit contents: - Data analysis as a tool for decision support and business intelligence. Motivations and context for data mining.
- Statistical models: linear and GLM models, estimation and adaptation to the data
- General notions for data mining: the contrast between adherence to data and complexity of the model i.e., contrast between bias and variance, general techniques for model selection (AIC, BIC, cross-validation, in addition to classical statistical tests), breaking the data into a working and a verification set.
- Methods for regression: non-parametric regression, additive models, trees, mars, projection pursuit, neural networks (overview).
- Classification methods: linear regression, logistic regression and multilogit, additive models, trees, polymars, neural networks, combination of classifiers (bagging, boosting, random forests).
- Methods for internal analysis: clustering methods, analysis of the associations between variables and Apriori algorithm. Social Networks (hints).
Planned learning activities and teaching methods: Lectures, laboratory exercises on real data
Additional notes about suggested reading: Textbook and material provided by the instructor.
Textbooks (and optional supplementary readings)
  • Azzalini A., Scarpa B., Analisi dei dati e data mining. --: Springer, 2004. Cerca nel catalogo
  • Azzalini A., Scarpa B., Data analysis and data mining. --: Oxford University Press, 2012. Cerca nel catalogo