- Docente: Marzia Freo
- Credits: 6
- SSD: SECS-S/03
- Language: Italian
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Business Administration (cod. 0897)
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. Classifcation Tree
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 computer lab activities.
Lectures deal with statistical tools listed in the course content and their application 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.
At the end of each class, with a ìweb survey few questions are answerd to students to verify the learning outcome.
Assessment methods
Assessment is based on a 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
During the exam, using lecture notes, books or electronic devices is forbidden.
The grade of the written exam is increased by grades attained by student who answered to the web questionnaire after the classes.
At each lesson, students get 0.1 points if they answer to all the questions, 0.3 points if they CORRECTLY answer to at least half of questions.
ONLY students attaining the maximum mark 32 in the written exam will be registered 30L.
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