Statistical Learning and Prediction from Data

Master’s degree in General Management and Consulting

Teaching
Courses
Master
Syllabus Project info Course page

Department of Economics “Marco Biagi” (UNIMORE)
Course type: Master Degree Course
6 ECTS – Statistics (SSD: STAT/01-A)
Semester: Second

Note: Starting from the 2025/2026 academic year, this course has been replaced by Data Analytics for Management.


Course objectives

The primary goal of this course is to equip students with the analytical framework and technical expertise required to extract meaningful predictions from complex datasets. As modern economics and finance increasingly rely on large-scale data characterized by uncertainty, this course bridges the gap between statistical theory and predictive practice.

Course content

For each credit (ECTS) there are 7 hours of lectures and 18 hours of self-study by students. Thus, 1 CFU is equivalent to a standard study commitment of 25 hours.

  1. Preliminary concepts
  2. Measurement and scales
  3. Primary data collection and survey design
  4. Sample survey
  5. Sampling techniques and sample weights
  6. Data preparation, z-scores and outliers
  7. Two mean comparison test (t-test) and one-way ANOVA
  8. Measures of association (Chi-square)
  9. Covariance, correlation and rank correlation
  10. Bivariate and multiple linear regression
  11. Factor Analysis and Principal Component Analysis
  12. Clustering techniques: hierarchical and non-hierarchical

Teaching methods

Teaching is in-person and delivered in Italian. The teaching method is based on (a) face-to-face lectures with both theoretical and applied content, supported by teaching materials (slides, exercises, etc.) and lecture recordings; (b) exercises and demonstrations of the use of SPSS statistical software for dataset analysis, aimed at developing the ability to apply the knowledge acquired. Supplementary teaching materials (slides, exercises, sample exam papers, etc.) can be found on the course’s MS Teams/Moodle pages. In line with Course of Study decisions, lecture materials and video recordings will be made available mid-course. Streaming, however, is not permitted, in accordance with UNIMORE’s teaching delivery regulations.

Expected results

  1. Knowledge and understanding: through classroom lectures and self-study Identification of the steps in the research process, measuring instruments and data collection. Knowledge of sampling techniques. Knowledge of statistical methods useful for measuring the relationship, if any, between two or more variables. Knowledge of the main multivariate statistical methods for dimensionality reduction of a data matrix (Principal Component Analysis and clustering techniques).
  2. Ability to apply knowledge and understanding: By carrying out homeworks and analysing empirical data, development of skills: Ability to carry out a statistical investigation in all its phases and use this knowledge in real problems in the economic, financial and social fields. Ability to use statistical language to study and formalise such problems. Ability to choose the appropriate methodology according to the problem, identifying the variables and the information to be obtained. Ability to interpret processing results critically and draw conclusions. Ability to communicate results, in the form of reports or graphs.
  3. Autonomy of judgement: Aptitude for a methodological approach that leads to verify through rigorous statistical and economic theory arguments the statements and methods presented. Ability to self-assess one’s own competences and skills.
  4. Communication skills: Ability to address statistical theory in a timely and coherent manner and to argue empirical analysis accurately. At the end of the course the student will present in written form the results and interpretations of empirical analyses obtained also with the aid of digital support.
  5. Learning ability: Acquisition of statistical and economic knowledge as one’s own assets, which can be used for individual analyses of empirical data.

Final examination

Students have the flexibility to choose between two assessment methods: a final written exam or a group project to be presented on the exam date.

Materials and books

• [ENG] Mario Mazzocchi (2008). Statistics for Marketing and Consumer Research. London: SAGE. Chapters: 1, 3, 4, 5, 7, 8, 9, 10, 12. • [ITA] Zani S., Cerioli A. (2007). Analisi dei dati e data mining per le decisioni aziendali. Giuffrè Editore (ISBN: 8814136955). Chapters: 1, 2, 3, 4, 5, 6, 8, 9. • Slides and exercises from MS Teams/Moodle.

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