- Docente: Luca Trapin
- Credits: 6
- SSD: SECS-S/02
- Language: English
- Teaching Mode: In-person learning (entirely or partially)
- Campus: Bologna
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Corso:
Second cycle degree programme (LM) in
Digital Humanities and Digital Knowledge (cod. 9224)
Also valid for Second cycle degree programme (LM) in Data, Methods and Theoretical Models For Linguistics (cod. 6725)
Second cycle degree programme (LM) in Innovation and Organization of Culture and the Arts (cod. 6795)
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from Nov 10, 2025 to Dec 15, 2025
Learning outcomes
techniques concerning the analysis of data bases. In particular the student is expected to learn: - probability s tools - measures of variance - index numbers
Course contents
Module A: Trapin
- Data collection, management and visualization
- Descriptive statistics
- Foundations of probability
- Statistical inference
--> Sampling
--> Estimation
--> Hypothesis testing
Module B: Montalto (GIOCA students)
This 10-hour module is designed to equip future cultural managers and policy-makers with the foundational tools and understanding to engage effectively with data analysts and navigate the growing field of cultural data. Recognizing the increasing role of evidence-based decision-making in cultural policy, the course introduces students to key concepts, classifications, methods, and data sources used to measure culture — a complex and multidimensional domain.
Rather than training students to become data scientists, this course empowers them to ask the right questions, critically interpret statistical information, and use data to support or challenge cultural policy choices. Emphasis is placed on the practical use of data analytics for policy formulation, including the preparation, analysis, and visualization of data. Students will explore the limitations and potential of data in cultural measurement, helping them to manage uncertainty and make informed decisions in their future careers.
Topics include:
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Introduction: Why Data Matters in Cultural Policy
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Measuring Culture: Is That Even Possible?
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Data for Policy-Making: Purposes and Pitfalls
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Finding Reliable Data Sources
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Preparing, Analyzing, and Visualizing Cultural Data
By the end of the course, students will be better prepared to interpret cultural data, understand its relevance for policy, and engage thoughtfully in data-informed policy discussions in the cultural and creative sectors.
Readings/Bibliography
Textbook
Luca Trapin, Lecture Notes for Basics Analytics.
Additional Material (Optional)
Gary Smith, Essential Statistics, Regression, and Econometrics, Academic Press (Elsevier).
David Spiegelhalter, The Art of Statistics: Learning from Data, Pelican Book.
Teaching methods
Class lessons
Assessment methods
Module A: Trapin
Written exam on EOL in Computer Lab: 20 multiple choice questions
- 13 theoretical
- 7 excercises
The exam lasts one hour.
Grading system:
- <18: fail
- 18-23:sufficient
- 24-27: good
- 28-30: very good
- 30 e lode: excellent
Module B: Montalto (GIOCA students)
Attendace required
Teaching tools
Class notes on Virtuale
Office hours
See the website of Luca Trapin