INFORMATION VISUALIZATION, DATA SCIENCE AND SOCIAL MEDIA ANALYTICS MOD.1

The course provides an overview of methods and techniques for exploring, analyzing, and visualizing data, with a particular focus on data from digital and social media platforms. Through a combination of theoretical and practical approaches, it introduces tools for digital data analysis and offers an initial introduction to network analysis, highlighting its potential for studying online social phenomena.

Knowledge and understanding:
- Understand the key concepts related to the collection, organization, visualization, and interpretation of big data.
- Develop both theoretical and practical knowledge in the field of data science, with particular focus on the analysis of data from digital platforms.
- Become familiar with the main techniques of network analysis, exploring their conceptual foundations and applications in online contexts.

Applying knowledge and understanding:
- Use tools and methodologies to analyze heterogeneous digital data, both structured and unstructured.
- Carry out analyses of social networks and digital content using programming languages such as R and Python.
- Critically evaluate analytical outputs and communicate results effectively and accurately.

Learning skills:
- Be able to access, understand, and use technical materials and documentation related to data analysis tools.
- Develop an autonomous and critical approach to learning new analytical techniques.

A basic knowledge of the following topics is required:
- Programming: fundamental concepts of structured programming and experience with at least one programming language (e.g., Python).
- Statistics: elements of descriptive statistics, basic probability, and essential notions of statistical inference.

- Introduction to data science and social media analysis
- Working with API
- Data and information visualisation
- Text analysis and topic modeling
- Sentiment analysis
- Network analysis

(This outline may be subject to change)

Easley, David, and Jon Kleinberg. Networks, crowds, and markets. Cambridge University Press, 2010.
Barabási, Albert-László. Network Science. Cambridge University Press, 2016

The final exam consists of an oral examination and the development of a project agreed upon with the instructor. The oral exam (approximately 20 minutes) includes the presentation and discussion of the project. Students are expected to carry out an original analysis by applying methods, tools, and techniques covered during the course. The project may be completed individually or in groups. The final grade will be based on the quality of the analysis in relation to the provided guidelines, the clarity of the presentation, and the technical complexity of the work.

oral

Grades 18–21: Sufficient
Partial understanding of theoretical topics, uncertain presentation, and imprecise language. The project meets only the minimum requirements and presents a basic analysis, possibly with methodological errors. Technical complexity is limited but sufficient to demonstrate a minimal application of course concepts.

Grades 22–24: Fair
Correct but not always in-depth answers, generally adequate language. The project is consistent with the guidelines and methodologically sound, though lacking in originality. Technical complexity is moderate and reflects a good understanding of the course content.

Grades 25–27: Good
Clear and well-structured argumentation, good use of technical language, and confident presentation. The project is accurate and robust in the application of methods and tools, with some original elements. Technical complexity is well developed and aligned with the learning objectives.

Grades 28–30: Very Good / Excellent
Comprehensive and well-articulated answers, excellent command of the topics, precise use of specialized language, and critical thinking skills. The project is in-depth and original, with a rigorous application of methods and, where applicable, thoughtful extensions or improvements. High technical complexity and strong problem-solving abilities.

30 with Distinction (30 e Lode)
Outstanding performance in both understanding and presentation. Flawless communication and a particularly innovative project. Demonstrates advanced critical thinking, autonomy, and analytical skills.

The course is based on interactive lectures, where theoretical concepts are introduced and explored in depth through practical examples and guided discussions.

The lectures combine:
- Theoretical presentation of the fundamental principles of network analysis and its methodologies.
- Practical examples, used to concretely illustrate the application of the methods and tools covered.
- Student interaction, encouraging active participation through questions, critical reflections, and case study analysis.

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development