First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SC01111799, A.A. 2017/18

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
SC1176, Degree course structure A.Y. 2014/15, A.Y. 2017/18
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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
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction English
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 ANNAMARIA GUOLO 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 of
Individual study
Laboratory 2.0 16 34.0 No turn
Lecture 4.0 34 66.0 No turn

Start of activities 26/02/2018
End of activities 01/06/2018

Examination board
Board From To Members of the board
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)

Prerequisites: Basic knowledge of computer science, Databases
Target skills and knowledge: The course is intended to provide an overview of concepts and methods for data analysis, as well as of instruments useful for a critical evaluation of the results.
Examination methods: Written examination / Practice
Assessment criteria: The exam has the aim of evaluating the knowledge acquired by the students and its application to the analysis of a dataset.
Course unit contents: - Introduction to the course: Data analysis as a tool for decision support. Motivations and context for data mining.
- Linear and generalised linear predictive models
- Classification methods: logistic regression, linear discriminant analysis and extensions
- Cross validation
- Model selection and regularisation
- Nonlinear models: semi-parametric and non-parametric regression
- Tree-based methods
Planned learning activities and teaching methods: The course consists of lectures and laboratory exercises on real data using the R programming language.
Additional notes about suggested reading: Textbooks. Material provided by the instructor and available through the Moodle platform.
Textbooks (and optional supplementary readings)
  • Azzalini A., Scarpa B., Analisi dei dati e data mining. --: Springer, 2004. Cerca nel catalogo
  • Gareth, J., Witten, D., Hastie, T., Tibshirani, R., An Introduction to Statistical Learning with Applications in R. --: Springer, 2013. Cerca nel catalogo