Educational objectives Learning goals
Knowledge and comprehension of the basic concepts and techniques of linear algebra and of analytic geometry of the plane and the space and ability to apply them to the study and resolution of simple problems also in the context of other courses.
Knowledge and understanding.
Good theoretical and practical knowledge of matrices, linear systems and other fundamental notions of linear algebra and ability to understand these issues also in the context of other courses.
Applying knowledge and understanding.
Ability to use the acquired skills for solving simple problems on matrices, linear systems and other fundamental notions of linear algebra, also for their use required in other courses.
Making judgements.
Good ability to recognize, frame and set out the resolution of simple problems on matrices, linear systems and other fundamental notions of linear algebra, possibly selecting appropriately among the methods learned.
Communication skills.
Good presentation skills of basic concepts and techniques of linear algebra as well as solution methods to simple problems.
Learning skills.
Good learning ability of mathematical issues in other courses, by virtue of the comprehension of the logical-deductive character of the discipline.
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Educational objectives Learning goals
The learning goal of the Laboratory is the knowledge of the main functions and tools of Excel, with
particular attention to functionalities useful in the field of empirical social research. The aim is to
provide students with knowledge that enables them to work independently with Excel, becoming
familiar with the software interface and syntax, and acquiring the skills necessary to perform
research operations such as:
- Data storage and construction of a new matrix;
- Management of databases and differently constructed data matrices;
- Data cleaning and pre-processing of different type of information;
- Recoding of variables;
- Sampling procedures involving simple random extraction of cases;
- Calculation of descriptive statistics of cardinal variables;
- Mono and bivariate data analysis using specific functions and tools;
- Production of graphs and tables.
Students will acquire theoretical and methodological knowledge by implement what was explained
by the teacher, thanks to the alternation of lectures, practical activities (individual work and
teamwork) and moments of discussion in the classroom.
Knowledge and understanding
Students will face in practice the main phases of data cleaning, pre-processing, and statistical-
descriptive analysis. At the end of the laboratory, students will have learned the functions and tools
of Excel that allow them to manage databases and data matrices, perform recoding operations of
variables, pre-process unstructured textual data, perform mono and bivariate data analysis, as well
as develop graphical representations.
Applying knowledge and understanding
Through practical experience, students will learn: how to store and organize information in a matrix
built from scratch; how to handle and manage databases obtained through platforms for building
and compiling online questionnaires or exported from other statistical analysis software; how to
choose the most suitable tools and procedures to carry out specific data cleaning, processing, and
analysis operations; how to present the results of data analysis through the production of graphs and
tables.
Making judgements
Revisiting the various methodological phases of data processing and analysis, students acquire
judgment, decision-making and problem-solving skills thanks to an experience of cooperative
learning, which encourages constant discussion among peers and with the teacher.
Communication skills
Participation in group work and discussion in the classroom of the practical results obtained at the
end of each practical session enhances students' communication skills. In particular, these activities
allow improving communication strategies in peer-to-peer discussions and offer the opportunity to
practice public speaking.
Learning skills
The applied research activity allows students to broaden the theoretical knowledge already acquired
and to strengthen the theoretical and practical learning capacity of advanced approaches, methods
and techniques for analyzing social phenomena.
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Educational objectives The goal is to provide students with the so-called soft skills, useful for future academic and professional activities.
They include: public speaking, ability in producing scintific written reports, use of scientific text editors (e.g. Latex), team working, etc.
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Educational objectives Learning goals.
The main learning goal of the Laboratory concerns the definition and the development of the whole process of a social research from its design and planning up to the field-work, the data analysis and their representation in a statistical report.
The lessons and the practical activities of the teaching programme are well balanced in order to enable students to apply the theoretical concepts learned during the lessons.
Knowledge and understanding.
At the end of the Laboratory students know all the phases of the process design and planning as well as the correspondent specific activities.
Students learn the main techniques
a) to conceptualize research problems;
b) to build up data collection tools (paper and electronic questionnaires);
c) to gather data.
Moreover, students learn how to interpret and comment survey data, reporting the contents in an effective way. Applying knowledge and understanding.
The practical experience of applied research enable students to formulate research questions, objectives and hypothesis; to identify the most appropriate data collection tools; to build up a data collection tool (paper and electronic questionnaire); to monitor the data collection activity; to analyse and interpret data.
Students experience the redaction of a report summarizing the main results of the survey they have conducted.
Making judgements.
The students working in group are constantly encouraged to discuss and share ideas, in order to acquire capacity of decision and problem solving with respect to the choices related to the realization of a social research. Moreover, the students improve their ability to interpret data.
Communication skills.
The working group and the presentation of the results of the tasks assigned by the lecturer contribute to the development of communication skills.
The presentation and discussion of the final report contribute to the acquisition of the specific scientific technical language of the discipline.
Learning skills.
The practical experience of research of the Laboratory enable students to strengthen their theoretical knowledge and to improve the capacity to learn more advanced methods and techniques of survey management.
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Educational objectives Educational Objectives
The course "AI Laboratory" aims to provide students with a practical understanding of the main tools and techniques in artificial intelligence, favoring a statistical-probabilistic approach where appropriate. Through hands-on lab activities and applied projects, the course seeks to develop operational skills in the use of machine learning algorithms, deep neural networks, and other emerging AI technologies such as LLMs and diffusion models. Students will be guided in designing and implementing solutions to real-world, multidisciplinary problems, fostering critical thinking and a deeper awareness of both the potential and the limitations of the studied tools.
Knowledge and Understanding
The course offers students a solid practical understanding of the main tools and techniques in artificial intelligence, with particular attention to statistical-probabilistic approaches. It promotes knowledge of machine learning algorithms, deep neural networks, advanced generative models (such as LLMs and diffusion models), and their applications.
Ability to Apply Knowledge and Understanding
Through laboratory activities and applied projects, students learn to concretely apply the techniques studied to develop solutions for real and multidisciplinary problems, gaining operational skills in the use of advanced AI tools.
Independent Judgment
The course encourages the development of critical thinking and awareness of the strengths and limitations of the tools used, promoting an independent and reflective evaluation of the solutions adopted in various application contexts.
Communication Skills
Participation in multidisciplinary applied projects requires the ability to effectively communicate results, design choices, and challenges, even in collaborative contexts, thus contributing to the development of technical communication skills.
Learning Skills
The practical and lab-based nature of the course fosters active learning, adaptability to new technologies, and the ability to stay up to date in a rapidly evolving field like artificial intelligence.
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