First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP4062891, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
SS1736, Degree course structure A.Y. 2014/15, A.Y. 2018/19
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination DATA MINING
Website of the academic structure
Department of reference Department of Statistical Sciences
E-Learning website
Mandatory attendance No
Language of instruction Italian
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

Teacher in charge BRUNO SCARPA SECS-S/01

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses SECS-S/01 Statistics 9.0

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

Type of hours Credits Teaching
Hours of
Individual study
Laboratory 4.0 30 70.0 No turn
Lecture 5.0 34 91.0 No turn

Start of activities 25/02/2019
End of activities 14/06/2019
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
6 Commissione a.a.2019/20 01/10/2019 30/09/2020 SCARPA BRUNO (Presidente)
CANALE ANTONIO (Membro Effettivo)
FINOS LIVIO (Membro Effettivo)
MENARDI GIOVANNA (Membro Effettivo)
5 Commissione a.a.2018/19 01/10/2018 30/09/2019 SCARPA BRUNO (Presidente)
CANALE ANTONIO (Membro Effettivo)
FINOS LIVIO (Membro Effettivo)
MENARDI GIOVANNA (Membro Effettivo)

Prerequisites: Statistical model II
Multivariate analysis
a first class in computer programming
a first class in Linear Algebra
Target skills and knowledge: Understanding and learning how to use data mining tools
Examination methods: The exam will be in 3 parts: theoretical, practical and oral parts
Assessment criteria: Correctness and quality of the exams
Course unit contents: - Motivation, trade off bias-variance, techniques for model selection,
- Regression models: glm; non parametric regression, additive models, trees, mars, projection pursuit, neural networks, deep learning
- Classification models: linear model, logistic and multilogit regression, additive models, trees, poly mars, neural networks, combining classifiers (bagging, boosting, random forests), support vector machines.
- Inner analysis: clustering, association rules, social networks
- Sentiment analysis
Planned learning activities and teaching methods: Lessons and laboratory sessions
Additional notes about suggested reading: R environment
Textbooks (and optional supplementary readings)
  • Azzalini, A. e Scarpa, B., Data analysis and data mining: an introduction. New York: Oxford University Press, 2012. Cerca nel catalogo
  • Azzalini, A. e Scarpa, B., Analisi dei dati e data mining. Milano: Springer-Verlag Italia, 2004. Cerca nel catalogo
  • Hastie, Trevor J.; Tibshirani, Robert, The elements of statistical learningdata mining, inference, and predictionTrevor Hastie, Robert Tibshirami, Jerome Friedman. New York: Springer, 2009. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Case study
  • Working in group
  • Questioning
  • Story telling
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure