Download Computer Systems Laboratory for Decision

Computer Systems Laboratory for Decision-Making Support
I SEM.: I MOD.: PROF. MASSIMO MAMINO; II MOD.: PROF. GIUSEPPE BOARI
II SEM.: I MOD.: PROF. MASSIMO MAMINO; II MOD.: PROF. GABRIELE CANTALUPPI
COURSE AIMS
To teach students the main methods used to analyse the enormous amounts of data
kept in various corporate repositories, which - if duly processed - may be used
profitably to define present and future scenarios and support decision-making
processes.
Particular attention will be paid to methodological and practical aspects, through
illustration of real cases and laboratory exercises with the aid of tools widely used
in the corporate world.
COURSE CONTENT
The course entails 60 hours of lessons split between theory (40) and laboratory
(20).
The theory and laboratory lessons are in turn split into two (20+10)-hour parts:
– the first 20 hours of theory are devoted to the information technology aspects of
data processing (data acquisition techniques and access to databases, data
warehousing) and the application of business intelligence and data mining
techniques (on-line analytical processing, decision trees, clustering algorithms
and association rules);
– the remaining 20 hours of theory are devoted to investigating aspects regarding
the application of statistical data analysis methods, with particular attention to
the procedures available in MS Excel, including multiple regression analysis,
simulation of probability distributions and time series analysis (moving
averages, deseasonalisation, exponential smoothing and scenario forecasting).
READING LIST
For the information technology part
C. VERCELLIS, Business Intelligence: modelli matematici e sistemi per le decisioni, McGraw-
Hill, Milano, 2006. (ch. 1, 3, 5, 6, ch. 10, sect. 10.1, 10.1.1, 10.2, 10.2.4, 10.3, 10.3.2,
10.3.4, 10.6, ch. 11, sect. 11.1, 11.3, ch. 12, sect. 12.1, 12.1.1, 12.2, 12.2.1).
Advised texts
F. RAJOLA, Customer Relationship Management in the Financial Industry: Organizational
Processes and Technology Innovation, Springer, 2013 (Second Edition).
R.J. ROIGER-M.W. GEATZ, Introduzione al Data Mining, McGraw-Hill, 2004.
For the statistics part
M. BINI-G. SCAFFAI, Statistica aziendale. Analisi svolte con Excel, Pearson, Milano, 2009 (ch.
1, ch. 2 no sect. 2.4, ch. 5, ch. 6).
G. BOARI-G. CANTALUPPI, Raccolta di temi ed esercizi per il corso di Laboratorio Informatico
per le Decisioni Aziendali, 2° modulo (statistico) con allegato compact disk “Dati di
base e documentazione lezioni teoriche”, EDUCatt, Milano, 2010.
Advised texts
S. BORRA-A. DI CIACCIO, Statistica. Metodologie per le scienze economiche e sociali, McGraw
Hill, Milano, 2008.
TEACHING METHOD
Theory lessons (equally shared between theorical placement of the course contents and
the development of computer skills preparatory to the laboratories) and laboratories.
ASSESSMENT METHOD
Written exam (exercises and open-ended questions) in order to test the whole program at
the end of the lectures.
NOTES
Students can use their notebook during theory lessons, which take place in traditional rooms.
The following softwares are adopted in the course:
1. Weka, open source, available for download at: http://www.cs.waikato.ac.nz/ml/weka
2. Microsoft Excel and Microsoft Access
3. R, open source, available for download at: http://www.r-project.org
Further information can be found on the professor's webpage
http://www2.unicatt.it/unicattolica/docenti/index.html, or on the Faculty notice board.
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