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Curriculum(s) for 2024 - Statistics, Economics and Society (29925)

Single curriculum

1st year

LessonSemesterCFULanguage
97786 | LINEAR ALGEBRA1st9ITA

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.
1017589 | BASIC STATISTICS1st9ITA

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 | INFORMATICS1st9ITA

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 COURSE2nd9ITA

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 I2nd9ITA

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 | SOCIOLOGY2nd9ITA

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 language2nd3ENG

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.
Optional group F for 9 CFU/ ECTS

2nd year

LessonSemesterCFULanguage
10612163 | MATHEMATICAL ANALYSIS II COURSE1st6ITA

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 | PROBABILITY1st9ITA

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 | DEMOGRAPHY1st9ITA

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 LABORATORY2nd12ITA

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 MODELING2nd3ITA

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 MODELING2nd9ITA

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 DEMOGRAPHY2nd6ITA

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 II2nd6ITA

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
Optional group F for 9 CFU/ ECTS
Optional group C for 27 CFU/ ECTS

3rd year

LessonSemesterCFULanguage
1022894 | MULTI-VARIED STATISTICS 1st9ITA

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 RESEARCH1st6ITA

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 course1st12ITA

Educational objectives

This course can be chosen by the student within the Sapienza courses as long as consistent with the curriculum.
1017262 | SAMPLING TECHNIQUES2nd6ITA

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 exam2nd6ITA

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.
Optional group C for 27 CFU/ ECTS
Optional group F for 9 CFU/ ECTS

Optional groups

The student must acquire 9 CFU from the following exams
LessonYearSemesterCFULanguage
AAF2347 | Preparatory Mathematics1st1st3ITA

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.
AAF2455 | Excel Lab. Basic and Advanced Course1st2nd3ITA

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.
AAF1456 | Laboratory of probability2nd1st3ITA

Educational objectives

Learning goals
The primary educational objective of the course is students' learning of the main applied aspects related to probability.
Knowledge and understanding
At the end of the course the students know and understand 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 in the oral test.

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).
AAF2041 | Soft skills2nd2nd6ITA

Educational objectives

1. Knowledge and understanding: what students should know on the course topics after having passed the exam
After passing the assessment, students have got useful knowledge for an easier entry in to the labour market. They are introduced to team building, problem solving, decision making, public speaking and communication techniques, as well as to the use of professional tools to realize valuable presentations and visualization of statistical data and reports (LaTeX and Beamer), to have a colloquium, to draft a CV, etc… Moreover, students gain some insights on topics of specific interest for the labour market.

2. Applying knowledge and understanding: what students should be able to do after having passed the exam
After passing the assessment, students are able to effectively interact with future colleagues, to solve possible conflicts, to manage his/her own work and that of other collaborators, to address problems and take decisions, to present results in a captivating style.

3. Making judgements: activities through which critical faculties should be developed.
Critical capabilities are expected to be developed engaging students in traineeships attended in public research institutions, or private companies linked to Sapienza with official agreements as well as offering them opportunities to attend specific seminar cycles. Actually, the teaching method aims at encouraging all students, individually or in group, to observe, to analyse, to critically comment, to interpret, and share ideas, in order to get through decision making, and problem solving about specific data analysis issues posed by the lecturer.

4. Communications skills and activities through which the ability to communicate what was learned is developed.
The ability to communicate is developed through individual and team presentations, according to the different type of activity chosen by the student/s.

5. Learning skills: ability to continue studying the topics.
The competences acquired should contribute to both strengthen the opportunities for students to entry the labour market, and improve their cross-sectional competencies and skills that are increasingly demanded by public research institutions and private companies.
AAF1377 | Social Research Lab3rd2nd3ITA

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.
AAF1454 | Statistical Software Lab3rd2nd3ITA

Educational objectives

Learning goals.
The main goal of this lab course is the acquisition of the logic and the fundamentals of the statistical package SAS for the analysis of real data.
Moreover, at the end of the course students should be able to formalize simple questions on real practical problems using standard statistical tools.
The focus is also on the theoretical framework together with the computational details related to real applications, giving special attention to the interpretation of the results from the statistical software output. Knowledge and understanding.
When completing the course, students will have learnt the logic and basics of SAS programming to import and manipulate data, perform standard statistical analyses on real data.
Moreover, they will have learnt the basics of empirical checking of statistical laws and inferential theoretical properties by simulation.
Applying knowledge and understanding.
When completing the course, students will be able to formalize a selected statistical problem, make standard statistical analyses in autonomy, interpret and explain the results.
Moreover, they will be able to carry on simple simulations.
Making judgements.
Students develop the critical thinking by applying standard methodologies learnt in their curricula which they are able to use in autonomy by means of a statistical software.
The skill to process data and produce the output by themselves helps to learn how to interpret the results taking into considerations theoretical criteria.
Communication skills.
By processing data and interpreting the results, students will learn the correct use of the technical language which is required in both coursework and final exam.
Special attention is given to the skill of communicating results to non-specialists by using a rigorous but understandable language.
Learning skills Students passing the exam have learnt how to perform standard statistical analyses in autonomy with SAS and the logic to study and apply different methodologies in other applicative cases which are the premises to continue their studies in a Second Cycle Degree in either Statistics or Applied Statistics.

AAF2238 | Network analysis laboratory with Gephi3rd2nd3ITA

Educational objectives

Learning goals
The learning goal of the Laboratory is the knowledge of the main concepts and main tools for the analysis of social networks. At the end of the Laboratory the students know:
- the main fields of application of the SNA;
- the basic concepts of Social Network Analysis (nodes, links, ego-network and total network);
- the techniques for collecting and processing relational data (construction and computerization of a questionnaire aimed at acquisition of relational information, preparation of a DB for the organization of information, construction of adjacency and incidence matrices);
- the main network analysis measures (density, centrality and centralization, clustering);
- some statistical analysis techniques (components and cliques analysis);
- how to manage and modulate the graphic representation of social networks.
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 face practically all phases of the investigation process aimed at the study of networks, both the ego-network and total network. At the end of the Laboratory, students know how: to conceptualize and to plan the research design according to the relational approach; to collect the relational data; to analyze, read and interpret the data; finally, to present the results in a research report.

Applying knowledge and understanding
Through practical experience, students learn how: to formulate research questions, cognitive objectives and investigation hypotheses; to choose the tools and techniques for collecting relational data most suitable for specific research goals; to archive, organize and process relational information in order to reconstruct and graphically represent a social network; to analyze and interpret the collected data; to summarize and present the research results in a final report. Furthermore, at the end of the Laboratory, students know how to use Gephi and Ego-net software.

Making judgements
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
Participating of teamwork and to making speech in the classroom for telling results and goals achieved at the end of each practical session, favor the development of students' communication skills. The drafting of a final research report also allows students to learn and adopt the technical-scientific language of the specific discipline.

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.
The student must acquire 27 CFU from the following exams
LessonYearSemesterCFULanguage
1010576 | SOCIAL STATISTICS2nd2nd9ITA

Educational objectives

Learning goals
The primary educational objective of teaching is to show students how to apply the notions of descriptive and inferential Statistics to the specific issues of society. Furthermore, students must be able to manage a data base, deriving from a questionnaire survey, or from a secondary analysis on indicators, produced by an official statistical source, at national and international level.
Knowledge and understanding
After attending the course, the students will be able to understand the meaning and the role of the main statistical tests and the main strategies for the synthesis of univariate, bivariate and multivariate data.
Applying knowledge and understanding
At the end of the course the students will be able to apply the above tests and synthesis strategies on real data bases, taken as case studies.
Making judgements
Students will develop critical skills by comparing the results obtained with the application of different methods on the same real data bases. They will learn to contextualise the results obtained in the various social, economic and cultural spheres.
Communication skills
The students, through the study and the carrying out of practical exercises, acquire the technical-scientific language of the discipline, which will be opportunely used both in the exercises during the work, also in groups, and in the presentation of results of autonomous elaborations, both in the final oral exam.
Learning skills
Students who pass the exam learned a method of analysis that will allow them to understand the opportunities and limitations of the statistical approach to social, economic and cultural problems.
1036199 | THEORIES AND TECHNIQUES OF PSYCHOLOGICAL TESTS3rd1st9ITA

Educational objectives

GENERAL AIMS
The course aims to provide the student with a methodological framework for the quantitative assessment of cognitive, general and specific skills, and of the personality traits, both normal and pathological, through psychological tests, as well as deepening the statistical methods underlying the construction of these tests and the verification of their psychometric properties (reliability and validity). The lectures therefore have the purpose of integrating the conceptual aspects of psychometry (reliability, validity) with the psychology theories of intelligence and personality. The goal is to prepare the student to apply the methods of data analysis necessary to evaluate the psychometric properties of a test, and to learn how to choose a test, administer it correctly and interpret the scores obtained.

SPECIFIC AIMS

Knowledge and understanding
The student must demonstrate that he/she has acquired the basic knowledge and skills related to the construction, use and interpretation of tests in the profession and in psychological research.

Applying knowledge and understanding
The student must be able to interpret the data analysis necessary to demonstrate the reliability and validity of the tests. Moreover, under the supervision of a professional psychologist, he / she should be able to: consciously use a wide range of psycho-metric tools for psychological assessment; to know how to choose to administer, to interpret the main psychological tests for the evaluation of personality and intelligence.

Making judgements
The student must be able to assess critically and autonomously how to use different psychological tests, how to deal with ethical and deontological issues related to psychology evaluation, how to interpret the test results in a mindful way.

Communication skills
The student will be able to elaborate written materials and oral presentations able to communicate the knowledge (for example nature and use of psychological tests) to specialists and non-specialist stakeholders.

Learning skills
Through the lectures the student will acquire learning skills that can be spent in the specific context of psychometrics applied in different disciplinary fields, and in the more general autonomy in the reading of advanced scientific texts, which will be addressed during the studies, and in particular in the preparation of the "laurea" degree dissertation, as well as necessary to address the master's degree courses.
10600173 | Design and evaluation for social innovation - Evaluative Research Laboratory3rd1st9ITA

Educational objectives

The main objective of the course is s to offer the knowledge and skills useful for carrying out projects, research and evaluations in the field of public policies with a focus on social innovation, in a pluralist and democratic perspective of evaluation.
The student will acquire the ability to carry out functions of planning, coordination and implementation of evaluation research in the sectors of social innovation, sustainability and gender policies.

The course includes the following specific training objectives:

1) knowledge of the main design and evaluation approaches for social innovation and territorial development in terms of their application in terms of empirical research.

2) ability to design, coordinate and apply design and evaluation methodologies in the social innovation sector.

3) development of soft skills: problem solving, critical analysis and evaluation of public policies will be solicited in the theoretical lessons and in the workshop. A part of the theoretical lessons will be dedicated to illustration and discussion in class with students of projects for social innovation and evaluation surveys in order to stimulate students' meta-evaluation skills. In the laboratory, students will be divided into working groups for carrying out exercises on data design and analysis in class, in order to produce a final project work that will be evaluated with the peer evaluation methodology.

4) ability to correctly communicate the results of an evaluative research for the purpose of scientific dissemination and the usability of evaluation evidence in the field of design for social innovation. In the exercises and in the laboratory, students will be involved in activities of presentation and discussion in class of the results of the projects developed in the group work.

5) during the lectures, the participation of the students will be solicited, with the objective of ongoing assessment of learning, as well as for the accompaniment to the laboratory activities.
Specifically, the workshop part will allow the acquisition of practical skills that orientate to the planning an evaluative research focused on the social innovation.

Expected learning outcomes: at the end of the course, students will be able to carry out social innovation projects and empirical evaluative surveys using the different approaches to evaluation.
The students will acquire skills in the construction and application of evaluative research designs in the following phases: definition of the evaluative object, definition of the evaluation mandate with the client, selection of the evaluative approach to the context of the analysis, construction and selection of evaluative questions, design evaluation techniques and data collection, analysis of evaluative data with the support of the main software for empirical research.
10589727 | ECONOMICAL AND POLITICAL GEOGRAPHY3rd2nd9ITA

Educational objectives

The aim is to provide students with the interpretive spatial means functional to understanding the actions of the entities operating in the political and economic fields. They are necessary to grasp the complexity and the interrelations between the territory’s different geopolitical and geo-economic phenomena. The theoretical framework will be applied to actual instances of geopolitical instability on different levels of analysis.

Knowledge of basic notions in geography, as well as in the most recent developments in geopolitically relevant international matters, is required. In this regard, it is highly recommended that students prepare for the exam by making use not only of up to date atlases but also of specialized magazines. Visiting authoritative websites which focus on geopolitical themes is also advised.
98431 | INTERNATIONAL ECONOMY3rd2nd9ITA

Educational objectives

The course develops a systematic understanding of the key areas of international economics: trade, migration and international monetary economics, and their impact on each other. Theory will be applied to events, problems and trends in the international economy. Since international economics is dynamic in nature and influenced by real-world developments in the economic, political and financial spheres, the course coverage will be updated periodically to include recent developments/conditions in the real-world environment.

(a) knowledge and understanding;

After taking the course, students will be able to understand and define the main concepts, models and patterns of analysis of the three key areas of international economics: trade, migration and international monetary economics.

(b) Applying knowledge and understanding;

By the end of the course, students will be able to formalize real economic problems and apply discipline-specific methods to analyze them in detail. Students will acquire a theoretical background and, through the analysis of practical cases, the ability to critically study economic policies and models in the context of international trade.

(c) Making judgments

Students will increase not only their theoretical skills but also their critical curiosity in reading economic phenomena and economic models from an open economics perspective.

(d) Communication skills

Students/youth, through class discussions and exercises, will acquire tools for critical analysis and communication skills. They will acquire the technical and scientific language of the discipline; they will also learn how to structure and present a research report.

(e) Learning skills

Students who pass the exam will have learned notions, definitions, patterns of analysis and methods of analysis that will enable them to take other courses in economics and social sciences.
1024055 | Statistics for Experimental Research3rd2nd9ITA

Educational objectives

Learning goals
The main educational objective of the course is the learning of the linear model analysis in its theoretical, methodological and applicative aspects.
Students must master language and the principles of statistical analysis in the experimental field.

Knowledge and understanding.
After having attended the course the students know and know how to apply the methods of analysis of the Linear Model, in the various experimental, observational and quasi-experimental situations.

Applying knowledge and understanding.
At the end of the course the students are able to identify which types of situations can be analyzed with the linear model tools, and to formalize them in terms of parametric statistical models.
They are also able to formulate substantive questions in parametric terms, in different situations, and answer to these questions with the tools of statistical analysis.

Making judgements.
Students develop critical skills through the application of inferential methodologies to a wide range of situations that can be represented in the linear model family.
They also develop the critical sense through the selection, estimation and validation procedure of the statistical model in different situations related to real data.

Communication skills.
Particular attention is paid to the technical-scientific language of the discipline, which must be used correctly in the final test.

Learning skills.
Students who pass the exam have acquired the fundamentals of the parametric models that allow them to face the study of more complex models.
98457 | ECONOMIC STATISTICS3rd2nd9ITA

Educational objectives

Learning goals.
An introduction to the basic skills of empirical economic analysis

Knowledge and understanding.
After taking the course the students know and understand the main problems in measuring economic variables and the methods to be used to solve them.

Applying knowledge and understanding.
After taking the course the students know how to solve the main problems in measuring economic variables.

Making judgements.
Students develop their critical skills through the analysis of real datasets.

Communication skills.
Students acquire the technical language, which must be used both in the written exam and in the individual project.

Learning skills.
Students passing the exam have acquired the ability to read and realise basic empirical economic studies.
1018133 | ECONOMETRICS3rd2nd9ITA

Educational objectives

Learning goals.
The aim of the lectures is to provide an exhaustive discussion of the main topics concerning the linear model (OLS, MLE, IV, asymptotic theory and inference) for cross-section analysis and a brief introduction to the analysis of discrete data.
Students must understand the analytical problems of these methods and be able to apply them to concrete situations.

Knowledge and understanding.
After attending the course the students know and understand the main problems related to the linear regression model (for example: absence of exogeneity) and the main methods to be used to solve such problems (for example: IV estimator).

Applying knowledge and understanding.
At the end of the course the students are able to formalize real problems in terms of linear regression models and to apply the methods specific to the discipline to solve them.
They are also able to apply the methods to concrete situations and to interpret the results.

Making judgements.
Students develop a knowledge of the analytical properties of the presented methodologies and the ability to build programs for their implementation.
They also learn to critically interpret the results obtained by applying the procedures to concrete situations.

Communication skills.
Students acquire the technical-scientific language of the discipline, which it must be used appropriately in both the intermediate and final written tests and in the oral tests.
Communication skills are also developed through group activities.

Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle the study of analytical properties in more complex modeling contexts in subsequent quantitative area teachings.