23741 - Statistical Methods for Data Mining

Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Industry, innovation and infrastructure Reduced inequalities

Academic Year 2021/2022

Learning outcomes

This course will present statistical methods that have proven to be of value in the field of knowledge discovery in databases, with special attention to techniques that help managers to make intelligent use of these repositories by recognizing patterns and making predictions.

In particular, this course seeks to enable the student:

- to correctly plan a data mining process

- to choose the best suited methodology for the problem at hand

- to critically interpret the results

Course contents


Elements of descriptive and inferential statistics. Elements of probability calculus. Multiple linear regression model.

Course content:

Part I

- Intoduction: Data Mining and Statistics

- Data preparation: data discovery, data characterization, descriptive and exploratory statistics.

- Data cleaning: outliers and missing values.

- Variable transformations. Volume and dimension reduction techniques.

- Association rules

- Introduction to statistical learming methods: regression and classification problems.

- Parametric prediction methods: linear models in regression problems; logistic regression.

- Clustering methods: hierarchical and partitioning methods.


Part II

- Nonparametric regression methods: smoothers, Generalized Additive Models. Nonparametric classifiers: knn classifier, Naive Bayes classifier.

- Recursive partitioning methods and decision tree.

- Artificial neural networks: multilayer perceptrons; regularization techniques.

- Aggregation of prediction models.

- Model assessment criteria in regression and classification problems (ROC curve and LIFT curve).

Some additional computer laboratory sessions about SAS Enterprise Miner and R are planned.


Teaching material provided by the lecturer (and available at IOL) the following reference is recommended as additional readings:

Hastie T. Tibshirani R., Friedman J. The Elements of Statistical Learning. Data Mining, Inference and Prediction , Springer-Verlag, New York, 2008

Andrea Cerioli, Mauro Zani, Analisi dei dati e data mining per le decisioni aziendali. Giuffrè Editore, 2007

Giudici P. Data Mining: Modelli informatici, statistici e applicazioni, McGraw Hill, 2005 •

Azzalini A., Scarpa B. Data analysis and data mining. An introduction, Oxford University Press, 2012

Teaching methods

The course consists of lectures and computer laboratory activities in SAS and R: lectures deal with methodological issues about the statistical tools listed in the course content, while computer laboratory sessions focus on the application of data mining algorithms on specific case studies.

The laboratory exercise hav the aim to strengthen the knowledge acquired by students during the lectures, and to develop students' skills in choosing the most adequate methods for a given problem and in interpreting results.

Assessment methods

Assessment is based on a single final written exam. It consists of open and multiple choice questions on theoretical aspect and questions requiring to interpret and comment the output of a Data Mining analysis.

The on-line exam will occur on the application Exam On Line  (EOL) and platorm Zoom for cechking procedures.

The oral exam is non-compulsory and can be done after passing the written exam in the same exam session. The overall grade is expressed in thirtieths and takes into account the outcome of the written test and the oral one: the evaluation obtained in the written test can increase or decrease to more than 3/30

Teaching tools

Blackboard; PC; videoprojector; computer laboratory

Office hours

See the website of Stefania Mignani

See the website of Matteo Farnè