Business Statistics
A.Y. 2024/2025
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 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 a source within the firm, such as those related to customers or users, or may derive from appropriate market research. 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 reasoning critically and rigorously, 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 both theoretical and practical formalization of 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; learn tree models and provide a strong time series analysis. Moreover, the students will be able to 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.
Lesson period: Third trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Third trimester
Course syllabus
The course topics are:
- Data organization
- Univariate and multivariate descriptive statistics
- Definition of a model (regressions, clusters, decision trees, etc.)
- Model validation techniques
Emphasis will be placed on the application of these techniques in real-world fields of economics/business through various business cases.
- Data organization
- Univariate and multivariate descriptive statistics
- Definition of a model (regressions, clusters, decision trees, etc.)
- Model validation techniques
Emphasis will be placed on the application of these techniques in real-world fields of economics/business through various business cases.
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 includes 20 theoretical and applied lectures (a series of business cases will be analyzed during the lessons) and 6 practical exercises, during which students will analyze and synthesize a series of economic and business datasets. The analyses will also be conducted using open-source software, such as R.
Teaching Resources
The main reference books for the preparation of the exam are indicated below:
- material available from the lecture (ONLY FOR ATTENDING STUDENTS);
- Applied Data Mining for Business and Industry (2 Ed. - Wiley)
- STATISTICA PER LE DECISIONI AZIENDALI - 2/ED (Pearson)
- material available from the lecture (ONLY FOR ATTENDING STUDENTS);
- Applied Data Mining for Business and Industry (2 Ed. - Wiley)
- STATISTICA PER LE DECISIONI AZIENDALI - 2/ED (Pearson)
Assessment methods and Criteria
FOR ATTENDING STUDENTS the final course evaluation will be based on:
A) Written exam:
- 32 multiple-choice questions (1 point for each correct answer, 0 points for incorrect or unanswered questions)
B) Group Project Work, for which instructions will be provided at the beginning of the course and will be presented by each group during the final lesson.
The final grade will be a weighted average of the exam and the project work.
FOR NON-ATTENDING STUDENTS:
The final assessment will consist solely of the written exam, which includes 24 multiple-choice questions (1 point for a correct answer, 0 for an incorrect or missing answer) and an exercise (max 8 points).
A) Written exam:
- 32 multiple-choice questions (1 point for each correct answer, 0 points for incorrect or unanswered questions)
B) Group Project Work, for which instructions will be provided at the beginning of the course and will be presented by each group during the final lesson.
The final grade will be a weighted average of the exam and the project work.
FOR NON-ATTENDING STUDENTS:
The final assessment will consist solely of the written exam, which includes 24 multiple-choice questions (1 point for a correct answer, 0 for an incorrect or missing answer) and an exercise (max 8 points).
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