INFORMATION VISUALIZATION, DATA SCIENCE AND SOCIAL MEDIA ANALYTICS MOD.2

The course introduces methods and techniques for the exploration, analysis, and visualization of data, with particular attention to digital data and online platforms. Through a theoretical and applied approach, the course provides the foundations of data science and introduces tools for data analysis and information representation. The course adopts a blended learning format, combining in-person lectures (for introducing and discussing concepts) with online activities (for practical application, exercises, and further study), in line with the applied nature of the course.

Knowledge and understanding
- Understand the fundamental concepts related to data collection, organization, visualization, and interpretation
- Acquire both theoretical and practical foundations in data science
- Understand the basic principles of data representation

Ability to apply knowledge and understanding
- Use tools and programming languages (e.g., Python, R) to analyze heterogeneous data
- Explore and analyze large-scale datasets
- Critically interpret results and communicate them effectively

Learning skills
- Access and use technical documentation and analytical tools
- Develop autonomy and critical thinking in learning new techniques

- Basic knowledge of programming (e.g., Python or R)
- Basic elements of descriptive statistics and probability

- Introduction to data science
- Data collection and management
- Large-scale data analysis
- Text and sentiment analysis
- Network analysis
- Methodological biases in data and analysis
- Effective communication and visualization of results

(The list of contents may be subject to change)

- Easley, D., Kleinberg, J. Networks, Crowds, and Markets (2010)
- Barabási, A.-L. Network Science (2016)

Additional materials will be provided on Moodle.

The final exam consists of an oral examination aimed at assessing both the understanding of theoretical content and the ability to apply the concepts and tools introduced during the course.

The exam includes:
- questions on the main theoretical topics covered (data science, data visualization, digital data analysis);
- applied questions, which may require interpreting results, discussing analytical procedures, or solving simple problems related to the tools and methods presented.

The evaluation will take into account:
- level of understanding of the concepts;
- ability to connect theory and application;
- clarity of presentation and appropriate use of technical language;
- autonomy of reasoning and critical thinking.

During the course, online activities (e.g., exercises, quizzes, discussions) may be used to support the learning process and self-assessment.

oral

The lecturer has a duty to ensure that the rules regarding the authenticity and originality of exam tests and papers are respected. Therefore, if there is suspicion of irregular conduct, an additional assessment may be conducted, which could differ from the original exam description.

Grades 18–21: Pass (Sufficient)
Partial understanding of theoretical topics, uncertain presentation, and imprecise language. Limited ability to apply concepts to simple examples or to interpret basic results.

Grades 22–24: Fair (Satisfactory)
Adequate understanding of the main contents, generally clear presentation. Ability to apply concepts to simple cases and to correctly interpret basic results and analytical procedures.

Grades 25–27: Good
Good command of the topics, clear presentation, and appropriate use of technical language. Ability to connect theory and application, correctly and coherently interpreting results and methods.

Grades 28–30: Very good / Excellent
Thorough and comprehensive understanding of the topics, confident and precise presentation. Strong critical thinking and ability to apply concepts independently across different contexts, with well-founded and articulated interpretations.

30 cum laude
Outstanding mastery and critical elaboration of the content, particularly effective and rigorous presentation. Ability to integrate theory and application autonomously, with original and well-structured insights.

The course combines in-person lectures and online activities in an integrated and progressive format. Classroom sessions focus on introducing and deepening theoretical concepts through applied examples, case study discussions, and interactive moments with students. Online activities are aimed at practical application and skills consolidation. Students work independently on exercises, multimedia materials, and self-assessment tools.

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