DATA MINING

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

Single discipline educational activity

Course Sheet

Course code:

000217L

Academic Year of enrolment:

2018

Year of supply:

2020/2021

Type of Course:

Characterising

Degree:

Course of 1st Cycle Degree in ECONOMICS AND BUSINESS INFORMATICS

Disciplinary Sector:

Statistics

Location:

PESCARA

Credits:

Programme Year:

Professor and Collaborators:

BUCCI Andrea

Cycle:

Second semester

Hours of classroom activity:

Individual study hours:

102

Prerequisites:

Mathematics (calculus), linear algebra and statistical inference (estimation and statistical test)

Objectives

The course provides knowledge about the analysis of data in the business environment. The course aims to provide the student with the tools to
extract relevant information from large amounts of data, with particular attention to statistical learning (statistical learning) both in a predictive
and non-supervised context (supervised and non-supervised learning).

LEARNING OUTCOMES
The course aims at completing student's training with notions and tools useful to deepen the aspects of statistical analysis in the business
environment. The training will then be completed and enriched by the following skills:

Knowledge and understanding / Applying knowledge and understanding
- Knowledge of statistical concepts for multivariate analysis and related specialized terminology
- Ability to apply the principles of statistical reasoning in the preparation and interpretation of company reports
- Ability to use R software for statistical analysis

Making judgements
- To learn the logical and statistical concepts that are indispensable for working independently in the research, selection and processing of
company data and using official statistical sources.

Communication skills
- Learn the terminology and statistical techniques of multivariate analysis to communicate or correctly discuss the results of the analysis of
company data and business reports

Contents

The following topics are considered as important parts of the teaching program for the fulfilment of the objectives: introduction to Data Mining
and Statistical Learning, data visualization, regression and classification, Non-supervised learning (principal component analysis and clustering)

Extended Syllabus

The course aims to introduce methods and models to extract relevant information from large amounts of data, with particular attention to
statistical learning (statistical learning) both in a predictive and nonpredictive context (supervised and non-supervised learning). In order to
provide the skills for the analysis and modeling of real data, the lessons will be supplemented by R exercises in the computer room.

Program:
Introduction to data mining and statistical learning.

Data visualization techniques

Regression and Classification: multiple linear regression, discriminant analysis and K-nearest
neighbors.

Non-linear methods (flexible regression): polynomial regression and generalized additive models.

Unsupervised learning: association rules, principal component analysis, grouping methods (hierarchical Clustering and mixtures).

Recommended Bibliography

Slides and handouts for students not attending the course will be available from the professor
James, Witten, Hastie, Tibshirani (2013) An Introduction to Statistical Learning (with Applications in R), Springer-Verlag
Hastie, Tibshirani, Friedman (2009) The elements of statistical learning: data mining, inference and prediction. 2nd edition, Springer-Verlag
Wickham (2016) ggplot2. Elegant Graphics for Data Analysis. 2nd Edition, Springer-Verlag
Maindonald, Braun (2010) Data Analysis and Graphics Using R: An Example-Based Approach . 3rd edition, Cambridge University Press

For Italian reading we suggest James, Witten, Hastie, Tibshirani (2020) Introduzione all'apprendimento statistico (con applicazioni in R),
Azzalini, Scarpa (2004) Analisi dei dati e data mining, Springer-Verlag

Methods of Provision

Conventional

Teaching Methods

Frontal lectures as well as practical exercises with the use of the software
R

Evaluation methods

Verification of learning:

The exam is divided into a 60-minute written test (exercises in R and open questions with predefined space, to verify the knowledge of the theoretical part of the
topics covered in class) and in an oral presentation (optional) of a report prepared for the analysis of two different data sets using the R software.

In the examination, the optional test gives the opportunity of adding from 1 to 3 grades to the final grade in the cas the student reached a sufficient grade in the written exam. The final grade will be given by the sum of the written exam (in thirtieths) and the grade concerning the report, in the case the latter has been carried out.

Contacts/More Information

E-mail: andrea.bucci@unich.it
Students will be received on Tuesday between 16:00 and 17:00, and on Thursday between 12.00 and 13:00.
Appointments can be fixed by e-mail.

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

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