75422 - Data Mining for Corporate Decisions

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: i) to correctly plan a data mining process; ii) to choose the best suited methodology for the problem at hand; iii) to critically interpret the results.

Course contents

1. Introduction: Data mining, big data, machine learning and  statistics

2. Exploratory data analysis. 

3. Cluster analysis

4. Linear regression model and logistic regression.

5. Classifcation Tree

6. Association rules

Readings/Bibliography

Teaching material is available on the website https://iol.unibo.it/course/

The following reference is recommended:

Azzalini A., Scarpa B. (2004).   Analisi dei dati e data mining. Springer-Verlag Italia, Milano

Teaching methods

The course consists of lectures and computer laboratory activities.

Lectures deal with methodological issues about the statistical tools listed in the course content.

Computer laboratory exercises focus on the application of data mining algorithms on specific case studies.

The aim is 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. 

Students attending the class may optionally execute  group works.

Assessment methods

Assessment is based on a single final written exam, which lasts 1 hour and half.  It consists of 8 questions: 4 questions deal with theoretical issues and the remaing 4 ones deal with interpreting and commenting the output of a Data Mining analysis. The mark will be expressed in points out of 30, and will result as the sum of the scores corresponding to the questions answered by the student (the maximum mark equals 32).

During the exam, using lecture notes, books or electronic devices is forbidden.

Students participating to work groups, get additional 3 points.

Teaching tools

Pc; videoprojector; computer laboratory

Teaching material is available at  https://iol.unibo.it/course/

Office hours

See the website of Marzia Freo