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Curriculum(s) for 2025 - Statistics, Economics, and Social Sciences (33505)

Single curriculum
Lesson [SSD] [Language] YearSemesterCFU
97786 | LINEAR ALGEBRA [MAT/03] [ITA]1st1st9

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.

1010575 | Statistics [SECS-S/01] [ITA]1st1st9

Educational objectives

Learning goals.
The primary objective is to provide students with basic concepts and procedures of descriptive statistics.
At the end of the course the student must be able to design a small census survey and to conduct descriptive analyses of the data through the use of a statistical software.
Knowledge and understanding.
Upon completion of the course, students know and understand the main procedures of descriptive statistics.
They are able to organize data in simple and contingency tables and to synthesize them through graphical representations.
They know and are able to calculate the most important statistical indicators that measure
(a) position, variability and form of simple distributions and
(b) important aspects of the joint distribution of two variables.
Furthermore, they have acquired the notion of statistical model and are able to implement a simple regression model.
Applying knowledge and understanding.
Upon completion of the course, students are able to apply the knowledge acquired, in order to interpret and critically evaluate the results of descriptive analysis.
Making judgements.
Through a large number of exercises on all the topics covered, students develop autonomous judgment skills that allow to identify the most appropriate methods to solve problems of descriptive statistics and to critically interpret the results of the elaborations provided by the software.
Communication skills Students, through the study and the performance of practical exercises, acquire the technical-scientific language of the discipline, that must be properly used both in the written and in the oral examinations.
Learning skills Students who pass the exam will have knowledge of the fundamental notions for the descriptive analysis of data.
They will also be able to implement simple codes to organize data in tables and synthesize them through graphics and/or calculation of important indicators.
Therefore, they have acquired the basics to learn what will be proposed in the subsequent statistical courses.

1017587 | INFORMATICS [INF/01] [ITA]1st1st9

Educational objectives

Learning goals.
The primary objective is to learn how to describe simple processes in a formal way, through the definition of algorithms, and to acquire a methodology to evaluate the complexity of an algorithm.
Students must be able to:
- unambiguously define a problem,
- identify precisely which data should be processed,
- how to represent such data,
- how to decompose a procedure in steps that solves the problem.
These skills are expressed through the use of the Java programming language.
Knowledge and understanding.
After attending the course the students know and understand the concept of algorithm and how an algorithm can be expressed using a programming language.
They use the basic constructs of the Java language and are aware of the possibility of solving the same problem with different computational complexity algorithms.
They also know various algorithms for solving basic problems, such as searching and sorting, and some numerical algorithms.
Applying knowledge and understanding.
At the end of the course students are able to formalize algorithms for simple problems, implement them in Java language, passing through all phases: design, writing the source code, compilation, debugging and execution. They know the notations that allow to express asymptotically the complexity of an algorithm.
They know how textual, numerical, and other information can be encoded.
Making judgements.
Students are able to appreciate the difference between solving a problem and formally describing a resolutive process.
They manage to evaluate how different implementation choices can lead to solutions with different efficiency characteristics, applying paradigms studied in the context of basic problems.
Through intense laboratory activities they acquire a greater awareness of the processes underlying the use of a computer.
Communication skills.
Students acquire the formal rigor necessary to use a programming language.
They are able to appreciate and foresee the repercussions, in terms of complexity, of the application of different resolution techniques.
They know how to apply decomposition techniques, in order to reduce the solution of complex problems to the solution of simpler problems.
Learning skills.
Students who pass the exam can analyze the structure of a program, even complex, can easily be productive using any other imperative or object-oriented programming language, can distinguish for which problems an automated solution may exist.

10612162 | MATHEMATICAL ANALYSIS I COURSE [MAT/05] [ITA]1st2nd9

Educational objectives

Knowledge and understanding.
Good theoretical and practical knowledge of differential calculus, integration, power series (real functions of one real variable).
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 and for their use required in other courses.

Making judgements.
Good ability to recognize, frame and set out the resolution of simple problems, selecting appropriately among the methods learned.

Communication skills.
Good presentation skills of basic concepts and techniques of Calculus.

Learning skills.
Good learning ability of mathematical issues in other courses, by virtue of the comprehension of the logical-deductive character of the discipline.

1017588 | Political Economics I [SECS-P/01] [ITA]1st2nd9

Educational objectives

Learning goals
The aim of the course is to present the basic principles and tools of modern economic theory - both micro and macroeconomics - showing at the same time their empirical relevance. This is achieved by integrating the theoretical exposition with the description of actual features of the Italian economy and of other national economies.
Knowledge and understanding
The lectures aim at allowing students to gain a general knowledge of the essential concepts used in economic analysis and a historical perspective of the development of the main theoretical approaches to the study of the economic system.
Applying knowledge and understanding
Students will learn the main lines of development of microeconomic and macroeconomic theories, including monetary theory, and will have a general view of economic policy.
Making judgements
The course aims at fostering students' ability to apply economic theory and methodology to the analysis of economic facts and of economic policy. These analytical abilities will be such to be also applied to current phenomena.
Communication skills
Frontal teaching and preparation of oral examination allows students to acquire mastering of elementary techniques and of communication skills properly belonging to the fields of economic analysis.
Learning skills
Students who pass the exam will have acquired analytical methodologies which ail allow them to tackle themes proper of other courses and to discuss current economic facts.

1017529 | SOCIOLOGY [SPS/07] [ITA]1st2nd9

Educational objectives

Learning goals
The Course aims to introduce students to the analysis of the main social phenomena characterizing the contemporary society, highlighting the change processes as well as the aspects of innovation they are concerned with. A special focus will be devoted to the issues of globalization and inequality; migration and integration processes; education an employment; technological innovation and communication, according to a gender and generational perspective. Finally, both the analysis and the interpretation of these phenomena will be accompanied by some key methodological issues rooted in different theoretical approaches and paradigms.
Knowledge and understanding
At the end of the Course, students will know the main theories and methodological approach to analyse social phenomena. They will learn the main schools of the classical (the positivism of A. Comte and E. Durkheim, the historical materialism of K. Marx, the comprehensive sociology of M. Weber) as well as the contemporary (the culturalism, the School of Chicago, the functionalism of T. Parsons, the structuralism, the sociology of A. Touraine, the School of Francoforte, the methodological individualism, the habitus of P. Bourdieu) sociological thought. Hence, they will learn the different interpretive perspectives of society, of the socialization processes, of the social change, of employment and education, of family structures, and group dynamics (social, political). This knowledge will enable students to both understand some complex social phenomena and formulate appropriate research questions.
Applying knowledge and understanding
The knowledge acquired enables students to apply theoretical schemes to complex social phenomena, traducing them in concrete research questions, defining objectives and working hypothesis. Moreover, the sociological fundamentals will provide students with the knowledge needed for an in depth interpretation of statistical data related to complex social phenomena. Making judgements Students are constantly involved in active class-work sessions. Indeed, the teaching method aims at encouraging all students, individually or in group, to analyse and critically comment/interpret socio-demographic data of official statistics, in order to develop capacity of synthesis and evaluation with respect to the issues proposed by the lecturer.

Communication skills
The working group and the presentation/discussion of the results of the class activities (comment and interpretation of statistical data and reports) contribute to both the development of communication skills and the acquisition of the specific scientific technical language of the discipline.
Learning skills
The sociological fundamentals acquired during the course will enable students to easily identify further references for an in depth study of those topics of personal interest.

AAF1101 | English language [N/D] [ENG]1st2nd3

Educational objectives

ObjectivesThis course aims to give students a solid grounding in statistical terminology and to acquaint them with the typical linguistic features and characteristics of standard statistical presentations and publications.Skills studiedProceeding from the elementary skills of interpreting and describing tables and graphs, the course will focus on expository texts ,so as to enable the students gain facility in describing the statistical methods underlying reported data. It is hoped that by the end of the course, the students, with reference to the publications of the Istitutonazionale di statistica , will be able to make competent statistical presentations in English on the economic and social realities of Italy and to respond to questions and requests for clarification thereon.

10612163 | MATHEMATICAL ANALYSIS II COURSE [MAT/05] [ITA]2nd1st6

Educational objectives

Knowledge and understanding.
Good theoretical and practical knowledge of differential calculus and integration for functions of several real variables. 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 and for their use required in other courses.

Making judgements.
Good ability to recognize, frame and set out the resolution of simple problems, selecting appropriately among the methods learned.

Communication skills.
Good presentation skills of basic concepts and techniques of Mathematical analysis.

Learning skills.
Good learning ability of mathematical issues in other courses, by virtue of the comprehension of the logical-deductive character of the discipline.

1022318 | PROBABILITY [MAT/06] [ITA]2nd1st9

Educational objectives

Learning goals
The primary educational objective of the course is students' learning of the main theoretical aspects related to probability.
Students must also be able to solve the analytical problems necessary to apply the aforementioned theoretical concepts.

Knowledge and understanding.
At the end of the course the students know and understand the main aspects related to the theory of probability and the main methods useful to solve the problems linked to the uncertainty.

Applying knowledge and understanding.
At the end of the course students are able to formalize problems related to uncertainty in terms of probabilistic problems and to apply the specific methods of the probability to solve them.
They are also able to model real phenomena through remarkable probabilistic structures.

Making judgements.
Students develop critical skills through the application of theory to a wide range of probabilistic models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different methodological aspects.

Communication skills.
Students, through the study and the practical exercises, acquire the technical-scientific language of the probability, which must be properly used both in the intermediate and final written tests and in the oral tests.

Learning skills.
Students who pass the exam have learned the basic concepts of probability that allow them to deal with subsequent statistical area teaching (in particular the teaching of Statistical Inference).

1017493 | DEMOGRAPHY [SECS-S/04] [ITA]2nd1st9

Educational objectives

Learning goals
The primary educational goal of the course is students' learning of the main concepts and basic methods of Demography.
Knowledge and understanding.
After attending the course, the students know and understand the statistical-demographic sources and the elementary measures to describe the main demographic phenomena.
Applying knowledge and understanding.
At the end of the course, the students are able to apply the learned methods to the real data, and to understand the results of these applications.
Making judgements.
Students develop critical skills through the application of different indicators and measures to a wide range of case studies, and learn to critically interpret the results.
Communication skills.
The students, through the study and the carrying out of practical exercises, acquire the technical-scientific language of the discipline, which must be opportunely used in the final oral examination.
Communication skills are also developed through group activities.
Learning skills.
Students who pass the exam have learned the skills necessary to address the study of more complex methods and models in subsequent teachings of demographic area.

1026126 | STATISTICAL INFERENCE AND LABORATORY [SECS-S/01] [ITA]2nd2nd12

Educational objectives

Learning goals.
The primary educational objective of the course is students' learning of the main problems and methods of statistical Inference and its different alternative theoretical approaches.
Students must also be able to solve the analytical problems necessary to apply the above methods and be able to interpret the results that derive from their application to real data.
Knowledge and understanding.
After attending the course the students know and understand the main inferential problems (point and interval parametric estimation and hypothesis testing of the most important univariate statistical models) and the main methods to be used to solve these problems (for example: maximum likelihood estimation, confidence intervals, parametric tests).
Applying knowledge and understanding.
At the end of the course the students are able to formalize real problems in terms of inferential problems and to apply the specific methods of the discipline to solve them.
They are also able to process the most important statistical models (with one or two unknown parameters) and to apply the methods to models not covered in the lessons.
Finally, they are able to apply the methods to the data and to interpret the results.
Making judgements.
Students develop critical skills through the application of inferential methodologies to a wide range of statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different inferential logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.
Communication skills.
Students acquire by means of theoretical study and by solving practical exercises, the technical-scientific language of the discipline, which must be properly used both in the intermediate and final written tests and in the oral axam.
Communication skills are also developed through group activities stimulated during labs and participation to a public discussion forum Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle, in future more advanced courses, the study of the formal properties of inferential procedures in more complex modeling contexts.

THREE-DIMENSIONAL MODELING [SECS-S/01] [ITA]2nd2nd3

Educational objectives

Learning goals.
The primary educational objective of the course is students' learning of the main problems and methods of statistical Inference and its different alternative theoretical approaches.
Students must also be able to solve the analytical problems necessary to apply the above methods and be able to interpret the results that derive from their application to real data.
Knowledge and understanding.
After attending the course the students know and understand the main inferential problems (point and interval parametric estimation and hypothesis testing of the most important univariate statistical models) and the main methods to be used to solve these problems (for example: maximum likelihood estimation, confidence intervals, parametric tests).
Applying knowledge and understanding.
At the end of the course the students are able to formalize real problems in terms of inferential problems and to apply the specific methods of the discipline to solve them.
They are also able to process the most important statistical models (with one or two unknown parameters) and to apply the methods to models not covered in the lessons.
Finally, they are able to apply the methods to the data and to interpret the results.
Making judgements.
Students develop critical skills through the application of inferential methodologies to a wide range of statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different inferential logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.
Communication skills.
Students acquire by means of theoretical study and by solving practical exercises, the technical-scientific language of the discipline, which must be properly used both in the intermediate and final written tests and in the oral axam.
Communication skills are also developed through group activities stimulated during labs and participation to a public discussion forum Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle, in future more advanced courses, the study of the formal properties of inferential procedures in more complex modeling contexts.

THREE-DIMENSIONAL MODELING [SECS-S/01] [ITA]2nd2nd9

Educational objectives

Learning goals.
The primary educational objective of the course is students' learning of the main problems and methods of statistical Inference and its different alternative theoretical approaches.
Students must also be able to solve the analytical problems necessary to apply the above methods and be able to interpret the results that derive from their application to real data.
Knowledge and understanding.
After attending the course the students know and understand the main inferential problems (point and interval parametric estimation and hypothesis testing of the most important univariate statistical models) and the main methods to be used to solve these problems (for example: maximum likelihood estimation, confidence intervals, parametric tests).
Applying knowledge and understanding.
At the end of the course the students are able to formalize real problems in terms of inferential problems and to apply the specific methods of the discipline to solve them.
They are also able to process the most important statistical models (with one or two unknown parameters) and to apply the methods to models not covered in the lessons.
Finally, they are able to apply the methods to the data and to interpret the results.
Making judgements.
Students develop critical skills through the application of inferential methodologies to a wide range of statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different inferential logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.
Communication skills.
Students acquire by means of theoretical study and by solving practical exercises, the technical-scientific language of the discipline, which must be properly used both in the intermediate and final written tests and in the oral axam.
Communication skills are also developed through group activities stimulated during labs and participation to a public discussion forum Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle, in future more advanced courses, the study of the formal properties of inferential procedures in more complex modeling contexts.

1023023 | TERRITORIAL DEMOGRAPHY [SECS-S/04] [ITA]2nd2nd6

Educational objectives

Goals Management of information about mobility and migration. Construction of interpretation/analysis models based on the use of origin/destination matrices. Ability to understand the problems about the construction of functional areas. Skills to acquire Ability to use population sources, especially data about population equilibrium and reconstruction. Management of migration information. Management of data coming from origin/destination matrices. Knowledge of recent immigration literature and the presence of foreigners.

1023028 | Political Economics II [SECS-P/01] [ITA]2nd2nd6

Educational objectives

Learning goals
Working knowledge of the main models of economic dynamics
Knowledge and understanding
Upon succesfull completion of the course, students will be able to analyse actual economic problems in terms of competing theories and models.
Applying knowledge and understanding
Upon succesfull completion of the course, students will be able to understand the explicit and implicit hypotheses informing the main economic policy proposals in the current debate.
Making judgements
The course is explicitly based on the principle of methodological and theoretical pluralism. Students will be introduced to at least two competing models for each economic problem considered, and will understand the criteria with which to personally choose their favourite interpretation
Communication skills
Through study and hands-on sessions, students will become proficient in the jargon and technical language of the discipline, which they must use in both written and oral examinations.
Learning skills
Students that succesfully complete the course will have learnt a method of analysis that will allow them to tackle and understand the main economic issues of today, both in subsequent economic courses and in the fruition and participation to the public debate

1022894 | MULTI-VARIED STATISTICS [SECS-S/01] [ITA]3rd1st9

Educational objectives

Learning goals.
The primary aim of the course is the students' knowledge of the most relevant multivariate statistical methods and their application to real data.
Knowledge and understanding.
Knowledge of the most applied multivariate techniques in the domains of dependence, classification and latent structures from the exploratory and inferential (multivariate Gaussian case) points of view.
Applying knowledge and understanding.
Ability to formalize real problems in statistical terms and to solve them by applying the corresponding multivariate methods.
Making judgements.
Ability to apply multivariate statistical methods and to evaluate the reliability of the results obtained by analyzing real data.
Communication skills.
By studying and by examples, learning and acquiring technical and scientific language in the statistical domain to be used properly in the final evaluation.
Learning skills
Students passing the exams acquire the knowledge for facing more complex issues, in more advanced statistical courses, by means of multivariate statistical methods.

1023208 | METHODOLOGY AND TECHNIQUE OF SOCIAL RESEARCH [SPS/07] [ITA]3rd1st6

Educational objectives

Learning goals
Capabilities of designing and running a social research.
Knowledge and understanding
Capability of analyzing social phenomena through middle range theories.
Applying knowledge and understanding
Capability of choosing techniques for detection, processing, and analysis more suitable to studied phenomena, at the macro, meso, and micro level.
Making judgements
Capability of research planning, organizing, and self-assessing.
Communication skills
Capability of team working and appropriately interacting in a multidisciplinary statistical-sociological team.
Learning skills
Capability of autonomous search of statistical, web, and bibliographical sources, necessary for doing research.

Elective course [N/D] [ITA]3rd1st12

Educational objectives

This course can be chosen by the student within the Sapienza courses as long as consistent with the curriculum.

1017262 | SAMPLING TECHNIQUES [SECS-S/01] [ITA]3rd2nd6

Educational objectives

Learning goals
The primary goal of the present course is to allow students to learn the main elementary techniques and methodologies for sampling finite populations and estimate population parameters. Students should be able to plan a sample survey and analyze collected data in order to provide point and interval estimates of the population parameters.
Knowledge and understanding
Students are expected to have a good knowledge of the main elementary sampling designs (simple random sampling, stratified sampling, single-stage cluster sampling, two-stage sampling, systematic sampling) as well as a basic knowledge of variable-probability sampling designs.
Applying knowledge and understanding
Students should be able to formalize real problems involving survey sampling and should use acquired knowledge to solve real problems. Furthermore, they should be able to estimate parameters of interest even in the presence of auxiliary variables.
Making judgements
Students should develop their skills by planning sample surveys.
Communication skills
Students should learn the appropriate language of survey sampling.
Learning skills
Students should be able to attack the problem of planning a sample survey by using elementary sampling designs. This is the typical case of surveys on a small/medium scale.

AAF1004 | Final exam [N/D] [ITA]3rd2nd6

Educational objectives

The final exams consists of writing, presenting and discussing a thesis, developed autonomously by the students, which illustrates in a coherent and detailed manner the problem tackled during the practical training and all the activities carried out to develop its solution.