Business Statistics

A.Y. 2021/2022
Overall hours
Learning objectives
The course of Business Statistics aims to provide the knowledge of the main Data Mining techniques addressed to the analysis of business data. Indeed, the increasing availability of data, typically generated by the information society, has brought out the need to deal with methodologies and tools for the quantitative decision-making processes in economic and business applications. Data may have source within the firm, such as those related to customers or users, or may derive from appropriate market researches. The presence of data of different nature (i.e., both qualitative and quantitative) requires that the student achieves suitable skills which allow him to justify the logical process underlying the adoption of a specific technical analysis, to formulate a reasoning in a critical and rigorous way and to detect the synthetic information to support decisions especially in the risk management process.The skills achieved in the course of Business Statistics will be useful for the courses whose main issues are related to marketing, market research and decision-making processes.
Expected learning outcomes
At the end of the course the student will have achieved the skills for the theoretical and practical formalization of the Data Mining techniques presented along the course. In particular, the student will be able to: recognize the differences between supervised methods, non-supervised methods, descriptive models and predictive models; demonstrate an adequate ability to choose the most suitable model based on the features of the available data and the purpose of the analysis to be led; select, among several models, the model characterized by the greatest predictive accuracy; implement the models using the programming language of the statistical R software; correctly interpret the analysis outputs by extracting information that can support the decision-making processes.
Course syllabus and organization

Single session

Lesson period
Second trimester
Course syllabus
The topics of the course are mainly focused on:
- summary statistics univariate and multivariate
- introduction to cluster analysis (hierarchical and non-hierarchical methods)
- logistic regression
- tree models
- local models and sequential rules
- time series models
Prerequisites for admission
In order to adequately understand the contents of the course, the students must have basic knowledges in Statistics and Mathematics.
Teaching methods
The course will be organized through lectures with support tools consisting in the employment of the overhead projector, through which the mathematical passages underlying models and methodologies will be illustrated.
Teaching Resources
The main reference books for the preparation of the exam are indicated below:
- material available from the lecture;
- Applied Data mining for business and industry of Giudici e Figini (Wiley)
- Data Mining and Business Analytics with R of Ledolter (Wiley)
- Data Mining for Business Analytics of Shmueli, Bruce, Yahav, Patel e Lichtendahl (Wiley)
Assessment methods and Criteria
Final Written Exam divided in two parts:
1) theoretical questions
2) interpretation of output in R
SECS-S/03 - ECONOMIC STATISTICS - University credits: 6
Lessons: 40 hours
Professor: Rossini Luca
Educational website(s)
Each Wednesday 9:15:12:15
DEMM, room 31, 3° floor