90591 - BIG DATA ANALYTICS

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. Data and descriptions

2. Dimensionality reduction: factor analyses

3. Perceptual maps: correspondence analysis

4. Cluster analysis

5. Classifcation Tree

6. Text mining

Readings/Bibliography

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

The following references are recommended:

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

Cerioli A., Zani M. (2007) Analisi dei dati e data mining per le decisioni aziendali. Giuffrè Editore

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

Teaching methods

Some business case studies and the most appropriate statistical methods to solve them are presented.

Solutions of case studies are then analysed and discussed through the use of statistical software (SPSS, R).

Students are invited to solve case studies following the instructions provided by the teacher, using the statistical software.

Students can carry out  an individual or in group of 2 students work by deepening a topic of a list provided by the teacher.

Assessment methods

Oral examination.

The examination consists into two to three answers in order to evaluate knowledges learned by the student and how they are deepened.

Questions concern:

- theoretical aspects

- understanding and interpretation of empirical aspects.

Students are examined following the registration number. If the number of students to examine is high, about 15 students a day are examined. The timetable list is provided one/two days before the exam.   

Additional grade

Individual/group work is valued from 0 to 3 points. For each student, the highest grade increases the grade of oral exam, until a maximum of 30 points.

 

Teaching tools

Pc; videoprojector; computer laboratory

Teaching material is available at https://iol.unibo.it/course/ [http://campus.unibo.it/]

Office hours

See the website of Marzia Freo