Study plan

Biostatistico

First year

Orientamento unico
Course		Semester	CFU	SSD
1018629 - Statistical Decision Theory	Learning goals Introduction to problems and methods of Statistical decision theory and to its main approaches (Bayesian and frequentist). Students acquire the ability: - to formalize statistical problems as decision problems, - to compare different solutions - to apply decisional methods to real data Knowledge and understanding knowledge and understanding of: - general decision problems - statistical problems as decision problems - different approaches to statistical decision problems (Bayesian and frequentist) Applying knowledge and understanding At the end of the course students are able to: - formalize statistical problems as decision problems - apply and compare different decisional method to the most important models - apply decision theory to new models - interpret the results of their analysis Making judgements Students develop judgement skills by: - applying and comparing different decisional methods to a wide range of statistical models - interpreting the results from the use of alternative decision methods on real data Communication skills Students develop communication skills by: - solving and presenting problems in written and oral form - group activity Learning skills The comparative-analytical methodology used in the course provides students with a learning capacity that is an important basis to approach following courses in the statistical area of the master program and of more advanced programs (PhD in Statistics)."	First semester	9	SECS-S/01
10589539 - Health Statistics	General goals Principles and methods to study the health conditions of a population Specific goals Knowledge and understanding After attending the course students are able to handle problems related to the evaluation of health conditions of single units and of a population Applying knowledge and understanding After attending the course students are able to handle the measure of health conditions of single units and of a population Making judgements Judgement skills are improved by discussion and use of alternative methods and practical and real examples Communication skills Students will develop capacity of communicating results through oral presentation of practical problems and by the final exam Learning skills Students who pass the exam have acquired skills and methods in Social epidemiology and in statistical methods for public health	First semester	6	SECS-S/05
- A SCELTA DELLO STUDENTE		First semester	12
1018630 - Sample surveys theory and methods	Learning goals The primary goal of the course on “Sample Surveys” is that student should learn the main problems and methods in sampling from finite populations. They should be able to formalize and plan the whole process of data collection and analysis in observational studies. In more detail, students should be able to plan a sample survey, to choose a sampling design, to plan the data collection, as well as to analyze real data and to estimate quantities of interest. Knowledge and understanding After attending the course the students know and understand the main methodologies in planning a sample survey, as well as in dealing with non-sampling sources of error, such as nonresponses and missing values, measurement errors, list imperfections. Furthermore, students should be able to analyze real data and to estimate quantities of interests, such as means and proportions. Applying knowledge and understanding At the end of the course the students are able to formalize and plan the whole process of data collection and analysis in observational studies. They should be able to manage the most important (i) sampling designs and (ii) point and interval estimators, as well as the main methodologies to deal with missing values, measurement errors, list imperfections. Moreover, they should be able to apply the methods to the data and to interpret the results. Making judgements Students develop critical skills through the application of sampling and estimation methodologies to a wide range of contexts. They also develop the critical sense through the comparison of different solutions and the analysis of results. Communication skills Students, through their study, should acquire the technical-scientific language of the discipline, to be used in their activity. Learning skills Students who pass the exam have learned a method of analysis to be used in the data collection and analysis from finite populations."	Second semester	9	SECS-S/01
10589782 - Statistical methods for genetics	Learning goals. The aim of this course is to provide the basic knowledge for a statistical approach to the analysis of genetic data. Students have to be able to formalize problems in the genetic field by using appropriate statistical tools, selecting the most convenient statistical models and interpreting the obtained results. Knowledge and understanding. After attending the course, students understand the problems related to the analysis of genetic data and know the most appropriate tools to deal with them. Applying knowledge and understanding. At the end of the course, students are able to use basic statistical models for the analysis of genetic data, also seen as multivariate data with complex structure. Students are able to interpret the results obtained by applying such models on real data. Making judgements. Students develop critical skills through the application and discussion of statistical tools aiming at the analysis of real genetic data, during the practical exercises in software laboratory. Communication skills Students, through practical exercises, applications on benchmark data close to real experimental situations, group activities, acquire a substantial technical-scientific ability to efficaciously communicate the obtained results. Learning skills. After attending the course, students have a broader knowledge of statistical models for analyzing genetic data, have learned son basic notions on the studies of this context, know how to apply the methods discussed, and critically comment the obtained results, through a massive use of the most known software in this field.	Second semester	6	SECS-S/01
Curriculum Biostatistico Gruppo A OPZIONALE un esame da 9 cfu		Go to group		MAT/06
Curriculum Biostatistico Gruppo OPZIONALE B esami a scelta per 18 cfu		Go to group
Curriculum Biostatistico Gruppo opzionale C altre attività per 6 CFU		Go to group

Second year

Orientamento unico
Course		Semester	CFU	SSD
1044607 - DRAWING AND ANALYSIS OF CLINICAL TESTS	The primary objective is to provide students with the main statistical techniques for the design and the analysis of clinical trials based on frequentist and Bayesian approaches. Knowledge and learning Upon completion of the course, students know the characteristics of the main experimental designs used in clinical studies. They are able to implement the fundamental procedures for the analysis of randomized clinical trials using different data (normal data, binary data, count responses and survival data). They know how to critically interpret the results, comparing the potentialities of the frequentist and the Bayesian approaches. Furthermore, they are able to apply suitable procedures for the determination of the optimal sample size based on different criteria. Applying knowledge and learning Upon completion of the course, students are able to apply the knowledge acquired, also through the use of the software R, in order to design clinical studies and to interpret the bresults of the statistical analyses Judgement skills Through their study and the various practical applications shown during the lessons, students develop the critical thinking that derives from the comparison between methodologies of analysis and design based on different approaches. Moreover, they acquire autonomous judgment skills that allow to identify the most appropriate methods to analyze clinical data, appropriately select the number of patients to enroll and choose the most suitable monitoring strategies. 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 have investigated issues concerning the application of inferential methods for the analysis and design of the clinical studies by exploiting both the frequentist and the Bayesian approach. This allows the development of autonomous skills to compare different procedures. Moreover, the group activities, which involve the use of the R software, develop communication abilities and enhance programming skills that will surely be useful to the students during the preparation of their final dissertation.	First semester	9	SECS-S/01
1022720 - Generalized Linear Models	Learning goals. The main educational objective of the course is the knowledge of the analysis of Generalized Linear Models in their theoretical, methodological and applicative aspects. Knowledge and understanding. After attending the course the students know and can apply the methods of statistical analysis to all those situations that can be represented in the Generalized Linear Models family. Applying knowledge and understanding. At the end of the course students are able to identify which types of situations can be analyzed in the family of generalized linear models, identifying sampling model, link function and linear predictor. They are also able to formulate substantive questions in parametric terms and answering to these questions with the tools of statistical analysis. Making judgements. Students develop critical skills through the selection, estimation and validation of the statistical model in different situations that can be represented in the Generalized Linear Models family. Communication skills. Particular attention is paid to the technical-scientific language of the discipline, which must be used appropriately in the final test. Learning skills. Students who pass the exam have acquired the ability to associate to the different real situation, the Generalized Linear Model that best represent them and to evaluate the quality of this representation. These tools are useful both for insights into the possible application fields and for the study of parametric models in general.	First semester	6	SECS-S/02
1038458 - Longitudinal and survival data analysis	Learning goals. Learning goal of the course is to acquire a basic knowledge of methods for the analysis of survival and longitudinal data. Knowledge and understanding. At the end of the course, the students have a basic knowledge of regression models for survival and longitudinal data Applying knowledge and understanding. Thanks to practical, computer-aided, classes, students learn how to apply regression models to observed survival and longitudinal data. Making judgements. Reviewing different estimators make students able in making judgements on observed data. Communication skills. At the ned of the course, students acquire basic notation and communication skills to be used in the context of the analysis of survival and longitudinal data. Learning skills. Students with a positive mark acquire a basic knoledge of surival and longitudinal data analysis that can be used in different empirical application fields.	Second semester	9	SECS-S/01
AAF1019 - Final exam	To allow the student to the writing of an original text that represents the sum of the specialized knowledge achieved during the course of his study.	Second semester	21
Curriculum Biostatistico Gruppo OPZIONALE B esami a scelta per 18 cfu		Go to group
Curriculum Biostatistico Gruppo opzionale C altre attività per 6 CFU		Go to group

Data analyst

First year

Orientamento unico
Course		Semester	CFU	SSD
1018629 - Statistical Decision Theory	Learning goals Introduction to problems and methods of Statistical decision theory and to its main approaches (Bayesian and frequentist). Students acquire the ability: - to formalize statistical problems as decision problems, - to compare different solutions - to apply decisional methods to real data Knowledge and understanding knowledge and understanding of: - general decision problems - statistical problems as decision problems - different approaches to statistical decision problems (Bayesian and frequentist) Applying knowledge and understanding At the end of the course students are able to: - formalize statistical problems as decision problems - apply and compare different decisional method to the most important models - apply decision theory to new models - interpret the results of their analysis Making judgements Students develop judgement skills by: - applying and comparing different decisional methods to a wide range of statistical models - interpreting the results from the use of alternative decision methods on real data Communication skills Students develop communication skills by: - solving and presenting problems in written and oral form - group activity Learning skills The comparative-analytical methodology used in the course provides students with a learning capacity that is an important basis to approach following courses in the statistical area of the master program and of more advanced programs (PhD in Statistics)."	First semester	9	SECS-S/01
- A SCELTA DELLO STUDENTE		First semester	12
10589780 - big data management and computing	General goals The goal of the course is the teaching of the models and of the technologies available nowadays (or being developed) for the management of Big Data. Students will also learn how to develop distributed algorithms for processing Big Data according to the MapReduce paradigm. Knowledge and comprehension Students will learn about the main issues that arise when managing and processing Big Data as well as the reasons why traditional approaches may fail in such a context. Then, they will learn about the alternative approaches useful to overcome these issues. Applying knowledge and comprehension At the end of the course, students will be able to model and develop NoSQL databases like, e.g., Document based, Graph based or KeyValue based, for storing and managing Bit Data. Moreover, they will be able to design and develop distributed algorithms for processing Big Data according to the MapReduce paradigm. Judgement skills Students will develop the ability to discern the particular types of data management or data processing problems requiring a non conventional approach because of their complexity and/or because of the size of the involved data. Moreover, they will be able to select the most appropriate paradigm for dealing with that particular problem, among the ones introduced during the course. Communication skills The students will acquire the technical-scientific language commonly used in this discipline, also thanks to the study and to the practice. This will be used, in turn, during the final examination. Communication skills will also be developed during the team activities that will be required for the presentation and the discussion of a project work at the end of the course. Learning skills The students that will pass the exam will know the paradigms to be used for managing and processing Big Data. Moreover, they will be able to put in practice these paradigms with the help of the technologies discussed during the course.	First semester	9	INF/01
1018630 - Sample surveys theory and methods	Learning goals The primary goal of the course on “Sample Surveys” is that student should learn the main problems and methods in sampling from finite populations. They should be able to formalize and plan the whole process of data collection and analysis in observational studies. In more detail, students should be able to plan a sample survey, to choose a sampling design, to plan the data collection, as well as to analyze real data and to estimate quantities of interest. Knowledge and understanding After attending the course the students know and understand the main methodologies in planning a sample survey, as well as in dealing with non-sampling sources of error, such as nonresponses and missing values, measurement errors, list imperfections. Furthermore, students should be able to analyze real data and to estimate quantities of interests, such as means and proportions. Applying knowledge and understanding At the end of the course the students are able to formalize and plan the whole process of data collection and analysis in observational studies. They should be able to manage the most important (i) sampling designs and (ii) point and interval estimators, as well as the main methodologies to deal with missing values, measurement errors, list imperfections. Moreover, they should be able to apply the methods to the data and to interpret the results. Making judgements Students develop critical skills through the application of sampling and estimation methodologies to a wide range of contexts. They also develop the critical sense through the comparison of different solutions and the analysis of results. Communication skills Students, through their study, should acquire the technical-scientific language of the discipline, to be used in their activity. Learning skills Students who pass the exam have learned a method of analysis to be used in the data collection and analysis from finite populations."	Second semester	9	SECS-S/01
1047208 - STATISTICAL LEARNING	Learning goals Devising new machine learning methods and statistical models is a fun and extremely fruitful “art”. But these powerful tools are not useful unless we understand when they work, and when they fail. The main goal of statistical learning theory is thus to study, in a statistical framework, the properties of learning algorithms mainly in the form of so-called error bounds. This course introduces the techniques that are used to obtain such results, combining methodology with theoretical foundations and computational aspects. It treats both the basic principles to design successful learning algorithms and the “science” of analyzing an algorithm’s statistical properties and performance guarantees. Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own data analyses. Methods for a wide variety of applied problems will be explored and implemented on open-source software like R (www.r-project.org), Keras (https://keras.io/) and TensorFlow (https://www.tensorflow.org/). Knowledge and understanding On successful completion of this course, students will: know the main learning methodologies and paradigms with their strengths and weakness; be able to identify a proper learning model for a given problem; assess the empirical and theoretical performance of different learning models; know the main platforms, programming languages and solutions to develop effective implementations. Applying knowledge and understanding Besides the understanding of theoretical aspects, thanks to applied homeworks and a final project possibly linked to hackathons or other data analysis competitions, the students will constantly be challenged to use and evaluate modern learning techniques and algorithms. Making judgements On successful completion of this course, students will develop a positive critical attitude towards the empirical and theoretical evaluation of statistical learning paradigms and techniques. Communication skills In preparing the report and oral presentation for the final project, students will learn how to effectively communicate original ideas, experimental results and the principles behind advanced data analytic techniques in written and oral form. They will also understand how to offer constructive critiques on the presentations of their peers. Learning skills In this course the students will develop the skills necessary for a successful understanding as well as development of new learning methodologies together with their effective implementation. The goal is of course to grow a active attitude towards continued learning throughout a professional career.	Second semester	6	SECS-S/01
1055946 - Mathematical Optimization Methods	Learning goals The course aims to provide a unified view of the main optimization problems and related solution algorithms. At the end of the course the student is able to classify optimization problems in appropriate categories, formulate optimization models for simple real problems and solve them with the appropriate algorithms and software. Knowledge and understanding. After attending the course the student knows and understands the different classes of optimization problems (Linear Programming, Integer Linear, Non-Linear Convex Programming) and the main solution methods (Simplex Method, Branch and Bound, Cutting Plane, Gradient-based descent methods and Interior point methods). Applying knowledge and understanding. At the end of the course the students are able to recognize real problems that can be modeled as optimization problems and to solve them with the appropriate algorithms and software. Making judgements. Students acquire the ability to classify optimization problems in appropriate categories and to evaluate their computational complexity. They also learn to explore the various aspects related to application problems, to evaluate different modeling options and to analyze the results obtained. Communication skills. Attending the lessons and studying the course material the students acquire the basic language of the discipline. Laboratory activities allow students to acquire the ability to prepare brief documents describing modeling choices and results of a simple case study. Learning skills. After the exam the students are able to attend courses with the various classes of optimization problems.	Second semester	9	MAT/09
Curriculum Data Analyst Gruppo A OPZIONALE un esame da 9 cfu		Go to group		MAT/06
Curriculum Data Analyst Gruppo B2 OPZIONALE a scelta un esame da 6 cfu		Go to group
Curriculum Data Analyst Gruppo OPZIONALE C altre attività per 6 cfu		Go to group

Second year

Orientamento unico
Course		Semester	CFU	SSD
10589781 - Statistical models advanced course	Learning goals. The aim of this course is to increase the knowledge of multivariate statistical models for analyzing and understanding complex (often very large) data matrices. Students have to be able to formalize the real problems in terms of the models discussed during the course and to interpret the obtained results. Finally, students have to program and apply these methodologies by using statistical software (in particular R / Matlab). Knowledge and understanding. After attending the course, students know and understand the main multivariate statistical models to deal with the studies of complex phenomena. Applying knowledge and understanding. At the end of the course, students are able to use and select among several multivariate statistical models to face problems related to different fields. Students are able to critically interpret the results obtained on real data sets by using statistical software (in particular R / Matlab). Making judgements. Students develop critical skills through the application of statistical models on real data and the comparison among solutions obtained from different models aimed at facing the same problem. Communication skills. Students, through practical exercises, reading and critical evaluation of scientific papers and group activities, acquire the technical-scientific ability to communicate results obtained on real problems. Learning skills. At the end of the course, students have a broader knowledge of the multivariate statistical models that allow them to carry out complex analysis strategies to extract the relevant information from the observed data, often of large size. This prerogative, combined with the knowledge of programming statistical software, responds to the increasing requests of the market (companies, research institutions, etc.).	First semester	9	SECS-S/01
10596188 - Causal inference	Educational objectives The educational goal of the course is students' learning of the main statistical methods used for causal inference. That is, how to answer research questions about the impact of certain causes on a particular outcome. Knowledge and understanding At the end of the course, students know and understand the main methods for causal inference. Ability to apply knowledge and understanding Students learn how to apply the main methods for causal inference also through the use of a statistical software. Judgment independence The discussion of the various methods, even with team works, provides students with the skills necessary to analyze real situations critically and independently. Communicative skills Students acquire the basic elements for reasoning in quantitative terms about causal inference problems. These skills will be further developed through team works on real data. Learning skills Students who pass the exam are able to apply the methods learned in different application contexts.	First semester	6	SECS-S/05
1022798 - DATA MINING AND CLASSIFICATION	Learning goals Thanks to technological advances, the acquisition of data has become inexpensive and big data sets are easily obtained, for example, via Internet, e-commerce or by electronic banking services. Such data can be stored in data warehouses and data marts specifically intended to support business decisions. Data mining provides the tools to manage and analyse these data, to extract the relevant information and build forecasting models, fundamental tools in areas such as credit evaluation, marketing, customer relationship management. The course will examine the data preprocessing methods and their importance. We'll cover some of non-parametric models for classification and regression: decision trees, neural networks, support vector machines. Ensemble learning methods (Bagging, Boosting, Stacking, Blended) will be illustrated. The course will address also the analysis of textual data and images. Knowledge and understanding. Acquire the basics of data mining techniques. Understanding how and why to choose between alternative statistical methods, or possibly how to combine different methods. Ability to handle large amounts of data with the help of appropriate, commercial and open source, software. Applying knowledge and understanding. Students develop critical skills through the application of a wide range of statistical and machine learning models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Making judgements. Students develop critical skills through the application of a wide range of machine learning and statistical models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills. Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which 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, in subsequent statistical area teachings, the study of the formal properties of data mining procedures in more complex modeling contexts.	Second semester	9	SECS-S/01
AAF1019 - Final exam	To allow the student to the writing of an original text that represents the sum of the specialized knowledge achieved during the course of his study.	Second semester	21
Curriculum Data Analyst Gruppo OPZIONALE C altre attività per 6 cfu		Go to group
Curriculum Data Analyst Gruppo B2 OPZIONALE a scelta un esame da 6 cfu		Go to group

Demografico sociale

First year

Orientamento unico
Course		Semester	CFU	SSD
10589650 - Analysis of population data	Learning goals The main learning goal of this course is to acquire skills in managing and analysing some data structures typologies and in choosing the specific suitable methods. Knowledge and understanding. Course’s knowledge concerns population hierarchical data, logitudinal (survival) data, relational data and related characestisics, issues and potentials. Also it concerns some methdological approaches as multilevel, lifecourse, and network analysis approach at a non depth level. Applying knowledge and understanding. Students attending this course will be able to provide specific measure for demographic phenomena; will have ability to read and interpret the main synthetic demographic parameters in the different study approaches to phenomena; ability to orientate among the main national and international population surveys; sensitivity to specific data and research problems, ability to recognize and use the methodological tools suitable to resolve them in the range of the topics developed by the course. Making judgements. This course gives the student the ability to discern opportunities from different data stuctures, so he is able to choose the most suitable methods to constuct an analysing pattern specific for data and for research hypotheses: event history analysis, contextual an multilevel analysis, network analysis. Communication skills. At the end of the course the student acquires the technical and scientific terms related to studied topics, thaks to the traditional lessons and seminars going in depth of the arguments from several point of view and with unusual tools. So he will use a language adequate to face, after the final exam, also developments in the following studies. Learning skills. When passing the examination, the student will have acquired logic, substance and tools the course provides. These elements give the student ability to formulate a research project for the final dissertation for example, but also for future work.	First semester	9	SECS-S/04
- A SCELTA DELLO STUDENTE		First semester	12
1018630 - Sample surveys theory and methods	Learning goals The primary goal of the course on “Sample Surveys” is that student should learn the main problems and methods in sampling from finite populations. They should be able to formalize and plan the whole process of data collection and analysis in observational studies. In more detail, students should be able to plan a sample survey, to choose a sampling design, to plan the data collection, as well as to analyze real data and to estimate quantities of interest. Knowledge and understanding After attending the course the students know and understand the main methodologies in planning a sample survey, as well as in dealing with non-sampling sources of error, such as nonresponses and missing values, measurement errors, list imperfections. Furthermore, students should be able to analyze real data and to estimate quantities of interests, such as means and proportions. Applying knowledge and understanding At the end of the course the students are able to formalize and plan the whole process of data collection and analysis in observational studies. They should be able to manage the most important (i) sampling designs and (ii) point and interval estimators, as well as the main methodologies to deal with missing values, measurement errors, list imperfections. Moreover, they should be able to apply the methods to the data and to interpret the results. Making judgements Students develop critical skills through the application of sampling and estimation methodologies to a wide range of contexts. They also develop the critical sense through the comparison of different solutions and the analysis of results. Communication skills Students, through their study, should acquire the technical-scientific language of the discipline, to be used in their activity. Learning skills Students who pass the exam have learned a method of analysis to be used in the data collection and analysis from finite populations."	Second semester	9	SECS-S/01
10589631 - demographic models	Learning goals The primary educational goal of the course is the students' learning of the main problems, methods and models of mathematical demography. Knowledge and understanding. After attending the course, the students know and understand the most commonly used methodologies for analysing demographic processes. Applying knowledge and understanding. At the end of the course students are able to apply the specific methods and models of the discipline to concrete cases, even in multidisciplinary contexts (socio-demographic, bio-demographic, actuarial, economic). Making judgements. Students develop critical skills through the application of methods and models to real data of different complexity. They also develop critical sense through the comparison of alternative solutions to the same problem. Communication skills. Students, through the study and development of applications to concrete cases, acquire the technical-scientific language of the discipline, which must be used appropriately in the final oral examination. 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 study the main demographic issues autonomously.	Second semester	6	SECS-S/04
Curriculum Demografico Sociale Gruppo A OPZIONALE un esame da 9 cfu		Go to group		MAT/06
Curriculum Demografico Sociale Gruppo OPZIONALE B esami a scelta per 21 cfu		Go to group
Curriculum Demografico Sociale Gruppo OPZIONALE C Altre attività per 6 cfu		Go to group
Curr. Demografico Sociale gruppo opzionale A1 1 esame a scelta per 9 crediti		Go to group		SECS-S/01

Second year

Orientamento unico
Course		Semester	CFU	SSD
10589830 - population estimation and forecasting	Management information on dynamics of population. Interpretation/analysis models on population components. Ability to use provisional demographic models. Skills to acquire Ability to use population sources, especially data on population balance and retrospective estimation of population. To build provisional scenarios. Use of original software for stochastic population forecast.	First semester	9	SECS-S/04
10589781 - Statistical models advanced course	Learning goals. The aim of this course is to increase the knowledge of multivariate statistical models for analyzing and understanding complex (often very large) data matrices. Students have to be able to formalize the real problems in terms of the models discussed during the course and to interpret the obtained results. Finally, students have to program and apply these methodologies by using statistical software (in particular R / Matlab). Knowledge and understanding. After attending the course, students know and understand the main multivariate statistical models to deal with the studies of complex phenomena. Applying knowledge and understanding. At the end of the course, students are able to use and select among several multivariate statistical models to face problems related to different fields. Students are able to critically interpret the results obtained on real data sets by using statistical software (in particular R / Matlab). Making judgements. Students develop critical skills through the application of statistical models on real data and the comparison among solutions obtained from different models aimed at facing the same problem. Communication skills. Students, through practical exercises, reading and critical evaluation of scientific papers and group activities, acquire the technical-scientific ability to communicate results obtained on real problems. Learning skills. At the end of the course, students have a broader knowledge of the multivariate statistical models that allow them to carry out complex analysis strategies to extract the relevant information from the observed data, often of large size. This prerogative, combined with the knowledge of programming statistical software, responds to the increasing requests of the market (companies, research institutions, etc.).	First semester	9	SECS-S/01
AAF1019 - Final exam	To allow the student to the writing of an original text that represents the sum of the specialized knowledge achieved during the course of his study.	Second semester	21
Curriculum Demografico Sociale Gruppo OPZIONALE B esami a scelta per 21 cfu		Go to group
Curriculum Demografico Sociale Gruppo OPZIONALE C Altre attività per 6 cfu		Go to group

Optional Groups

**Curriculum Biostatistico Gruppo A OPZIONALE un esame da 9 cfu: The student must acquire 9 CFU from the exams below**
Course		Year	Semester	CFU	SSD	Language
1022318 - PROBABILITY	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).	First year	First semester	9	MAT/06
10600045 - Stochastic Processes for applied sciences	Learning goals General goals. The primary educational goal of teaching is to learn the main concepts related to the theory of stochastic processes. Students must also be able to apply the methods and be able to interpret the results. Knowledge and understanding. After attending the course the students know and understand the main concepts on discrete and continuous processes. Applying knowledge and understanding. At the end of the course the students are able to formalize real problems and to solve them by applying the specific methods of the discipline. Making judgements. Students develop critical skills through the application of the theory of stochastic processes. Communication skills. Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which must be used appropriately in both the written and oral tests. Learning skills. Students who pass the exam have learned a method of analysis that allows them to tackle the subsequent teachings of statistical-probabilistic area.	First year	Second semester	9	MAT/06

**Curriculum Biostatistico Gruppo OPZIONALE B esami a scelta per 18 cfu: The student must acquire 18 CFU from the exams below**
Course		Year	Semester	CFU	SSD
1052019 - Bayesian modelling	General goals Knowledge at an intermediate and advanced level of the main issues in Bayesian statistics. Ability to apply Bayesian statistical techniques to applicative context. Knowledge and understanding Knowledge and understanding of the Bayesian approach to statistical inference, of its models and of its methodologies Applying knowledge and understanding Ability to apply Bayesian statistical methods for inferential problems in real-data problems Making judgements Ability of choosing appropriate Bayesian methods and models in different inferential problems Communication skills Ability of communicating results of the analyses in written and oral form Learning skills Students acquire skills useful to approach more advanced topics in Bayesian inference, Advanced data analysis, Statistical computing and Mathematical statistics	First year	First semester	6
Bayesian modelling		First year	First semester	3	SECS-S/01
Bayesian modelling		First year	First semester	3	SECS-S/01
10589780 - big data management and computing	General goals The goal of the course is the teaching of the models and of the technologies available nowadays (or being developed) for the management of Big Data. Students will also learn how to develop distributed algorithms for processing Big Data according to the MapReduce paradigm. Knowledge and comprehension Students will learn about the main issues that arise when managing and processing Big Data as well as the reasons why traditional approaches may fail in such a context. Then, they will learn about the alternative approaches useful to overcome these issues. Applying knowledge and comprehension At the end of the course, students will be able to model and develop NoSQL databases like, e.g., Document based, Graph based or KeyValue based, for storing and managing Bit Data. Moreover, they will be able to design and develop distributed algorithms for processing Big Data according to the MapReduce paradigm. Judgement skills Students will develop the ability to discern the particular types of data management or data processing problems requiring a non conventional approach because of their complexity and/or because of the size of the involved data. Moreover, they will be able to select the most appropriate paradigm for dealing with that particular problem, among the ones introduced during the course. Communication skills The students will acquire the technical-scientific language commonly used in this discipline, also thanks to the study and to the practice. This will be used, in turn, during the final examination. Communication skills will also be developed during the team activities that will be required for the presentation and the discussion of a project work at the end of the course. Learning skills The students that will pass the exam will know the paradigms to be used for managing and processing Big Data. Moreover, they will be able to put in practice these paradigms with the help of the technologies discussed during the course.	First year	First semester	9	INF/01
10589835 - computational statistics	Learning goals The main goal of the course is to learn about common general computational tools and methodologies to perform reliable statistical analyses. Students will be able to understand the theoretical foundations of the most important methods; to appropriately implement and apply computational statistical procedures; to interpret the results deriving from their applications to real data. . (a) Knowledge and understanding After attending the course, students will know and understand the most important computational techniques in statistical analysis. In addition, students will be able to appropriately implement the learned tools with the statistical software R and to develop original ideas often in a research context. (b) Applying knowledge and understanding At the end of the course, students will be able to formalize statistical problems from a computational point of view, to apply the learned methods to solve them, also in contexts not covered in the lessons, and to interpret the results deriving from their applications to real data. c) Making judgements Students will develop critical skills through the application of computational methodologies to a wide range of statistical problems and through the comparison of alternative solutions to the same problem by using different tools. Furthermore, they will learn to interpret critically the results obtained by applying procedures to real datasets. (d) Communication skills. By studying and carrying out practical exercises, students will acquire the technical-scientific language of the discipline, which must be suitably used in the final written test. Communication skills will also be developed through group activities. (e) Learning skills Students who pass the exam have learned computational techniques useful in statistical analysis and to work self-sufficiently to face the complexity of the statistical problems.	First year	First semester	6	SECS-S/01
10589631 - demographic models	Learning goals The primary educational goal of the course is the students' learning of the main problems, methods and models of mathematical demography. Knowledge and understanding. After attending the course, the students know and understand the most commonly used methodologies for analysing demographic processes. Applying knowledge and understanding. At the end of the course students are able to apply the specific methods and models of the discipline to concrete cases, even in multidisciplinary contexts (socio-demographic, bio-demographic, actuarial, economic). Making judgements. Students develop critical skills through the application of methods and models to real data of different complexity. They also develop critical sense through the comparison of alternative solutions to the same problem. Communication skills. Students, through the study and development of applications to concrete cases, acquire the technical-scientific language of the discipline, which must be used appropriately in the final oral examination. 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 study the main demographic issues autonomously.	First year	Second semester	6	SECS-S/04
1047802 - SPATIAL STATISTICS AND STATISTICAL TOOLS FOR ENVIRONMENTAL DATA	Learning goals The student at the end of the course should be able to use with knowledge advanced modeling and exploratory techniques specifically developed for spatially dependent data. This is achieved by assigning several homeworks on real data. Practical sessions with the R software are part of each lecture, so to allow students to implement what is taught in the theoretical part. Among the expected results, ability to elaborate environmental data using R software, ability to interpret the results obtained, ability to choose the most suitable statistical models according to the hypotheses they are founded on and to their compatibility with the data available. Knowledge and understanding The student will be able to understand the main tools for the analysis of spatial and spatio-temporal data. Also an introductory knowledge of extreme value estimation and modeling will be part of his cultural heritage Applying knowledge and understanding Students will be involved in the discussion and analysis of case studies using the open source statistical software R. Students will be asked prepare and discuss a presentation of the results of their homeworks. The presentation will be given on front of the class and discussed. Making judgements Through the homeworks and the final presentations discussions, tudente will develop judgements capacity in terms of theoretical choices in representation of real worls phenomena. Communication skills Students will be asked prepare and discuss a presentation of the results of their homeworks. The presentation will be given on front of the class and discussed. This procedure will help the student to develop his/her ability to communicate the results of its work. Learning skills One of the aims of the course is to build a statistical glossary and a dictionary of specific statistical concepts that will allow the student to read and understand scientific papers using advanced statistical tools in the analysis of environmental data.	Second year	First semester	9	SECS-S/02
10589781 - Statistical models advanced course	Learning goals. The aim of this course is to increase the knowledge of multivariate statistical models for analyzing and understanding complex (often very large) data matrices. Students have to be able to formalize the real problems in terms of the models discussed during the course and to interpret the obtained results. Finally, students have to program and apply these methodologies by using statistical software (in particular R / Matlab). Knowledge and understanding. After attending the course, students know and understand the main multivariate statistical models to deal with the studies of complex phenomena. Applying knowledge and understanding. At the end of the course, students are able to use and select among several multivariate statistical models to face problems related to different fields. Students are able to critically interpret the results obtained on real data sets by using statistical software (in particular R / Matlab). Making judgements. Students develop critical skills through the application of statistical models on real data and the comparison among solutions obtained from different models aimed at facing the same problem. Communication skills. Students, through practical exercises, reading and critical evaluation of scientific papers and group activities, acquire the technical-scientific ability to communicate results obtained on real problems. Learning skills. At the end of the course, students have a broader knowledge of the multivariate statistical models that allow them to carry out complex analysis strategies to extract the relevant information from the observed data, often of large size. This prerogative, combined with the knowledge of programming statistical software, responds to the increasing requests of the market (companies, research institutions, etc.).	Second year	First semester	9	SECS-S/01
1047774 - Forecasting models	Learning goals. Main goal is the ability to conduct a statistically correct analysis and a forecasting exercise in all cases where time a fundamental feature of the analysed data Knowledge and understanding. Demonstrate knowledge and understanding ability that extend those typical of the course of time series of the first degree, and allow also to develop original forecast proposal, on integrating different methodologies. Applying knowledge and understanding. Solving complex problem related to the time dynamics of several phenomena measured on different scales and with different methods. Making judgements. Ability to integrate knowledge and perform analysis of complex phenomena integrating different methodologies, also in presence of limited, incomplete or biased information. Communication skills. Communicate the results of a deep statistical analysis, illustrating the main steps and motivation also to non-specialst audience. Learning skills. To study in a self-managed and autonomous fashion.	Second year	First semester	6	SECS-S/01
10592835 - Bioinformatics and computational medicine	Learning goals The course aims at providing students with a basic training in practical and theoretical bioinformatics using the most common models and tools for analyzing "omics" data in biology and molecular medicine. After completing the course, the student is expected to be able to analyze and interpret large-scale data such as, for example, a patient's transcriptomic data using appropriate methodologies implemented with matlab or other high-level programming languages . Furthermore, the student will be able to link the biological theory and the analysis techniques critically analyzing the results. Knowledge and understanding Students will acquire knowledge and understanding in the field of bioinformatics and computational medicine at a level including some innovative topics. Applying knowledge and understanding Students will be able to apply their knowledge and show familiarity with methods in bioinformatics and computational medicine; they will also acquire adequate skills both to support arguments, and to solve problems typical to the subject. Making judgements Students will be able to collect, analyse and interpret data in the field of bioinformatics and computational medicine, to produce autonomous judgments and critical reflections of the corresponding research themes. Communication skills Students will be able to communicate information, ideas, problems and solutions even to non-specialist research staff (MDs/biologists). Learning skills Students will develop those learning skills necessary to move towards continuous in-depth studies that characterize the rapid evolution of the discipline with a high degree of autonomy.	Second year	First semester	9	ING-INF/06
10589824 - statistical analysis of relational structures	General goals Learning advanced statistical methods for relational data (theory and practice) Knowledge and understanding Knowledge and understanding of theory and methodology related to the study of relational data (for instance, dimensionality reduction, classification). Applying knowledge and understanding Ability to apply appropriate methods and approaches in the analysis of relational data Making judgements Ability of choosing appropriate methods, models and software in different problems; ability of discussing and interpreting results of applications of the methodologies on real data Communication skills Ability of using appropriate scientific language and of communicating results of the analyses in written reports Learning skills Students acquire skills useful to approach more advanced topics in Statistics	Second year	First semester	6	SECS-S/01
10596188 - Causal inference	Educational objectives The educational goal of the course is students' learning of the main statistical methods used for causal inference. That is, how to answer research questions about the impact of certain causes on a particular outcome. Knowledge and understanding At the end of the course, students know and understand the main methods for causal inference. Ability to apply knowledge and understanding Students learn how to apply the main methods for causal inference also through the use of a statistical software. Judgment independence The discussion of the various methods, even with team works, provides students with the skills necessary to analyze real situations critically and independently. Communicative skills Students acquire the basic elements for reasoning in quantitative terms about causal inference problems. These skills will be further developed through team works on real data. Learning skills Students who pass the exam are able to apply the methods learned in different application contexts.	Second year	First semester	6	SECS-S/05
10589563 - DATA DRIVEN DECISION MAKING	General Managers worldwide, beyond their personal experience, rely more and more on the use of quantitative decision models which allow to take advantage of today’s data availability. Morover, new computational tools, including algorithms, cloud computing and distributed processing, make it possible to both develop and compute analytical models in a very short time, meeting the requirement of practical applications and often using real time data. Data Driven Decision Making is the new paradigm for managers to make better, evidence based, more rational, transparent and reliable decisions. In this context, the primary educational objective of the course is students' learning of the main decision problems that arise in real world and the quantitative methods to model them and to feed them with adequate data. Students must also be able to correctly use, for decision-making and management purposes, computer tools to analyze data generated by real problems in different contexts (e.g. service management, marketing, transportation, operations management and production, and finance) through the analysis of several case studies. Specific objectives a) Knowledge and ability to understand After attending the course the students know and classify the main decision problems arising in real world organization and the main analytical methods (decision and optimization models and algorithms) to be used to support a Manager during his/her decision process. b) Ability to apply knowledge and understanding At the end of the course the students are able to formalize real problems in terms of decision problems and to apply the specific methods taught in the course to solve them. They are also able to classify the type of problem to it the most appropriate quantitative method, experimenting the effectiveness for decisional purposes also on real problems. c) Autonomy of judgment Students develop critical skills through the application of modeling, decision analysis and multi objective optimization methodologies to a broad set of practical problems. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using methods of analysis and realistic scenarios different from each other. They learn to critically interpret the results obtained by applying the procedures to real data sets. d) Communication skills Students, through the study and the carrying out of practical exercises, acquire the technical- scientific language of the course, which must be properly used both in the intermediate and final written tests and in the oral tests. Communication skills are also developed through group activities. e) Learning ability Students who pass the exam have learned methods of decision analysis and multiobjective optimization that allow them to face, decision-making problems and optimization on complex organizations.	Second year	First semester	6	MAT/09
1022798 - DATA MINING AND CLASSIFICATION	Learning goals Thanks to technological advances, the acquisition of data has become inexpensive and big data sets are easily obtained, for example, via Internet, e-commerce or by electronic banking services. Such data can be stored in data warehouses and data marts specifically intended to support business decisions. Data mining provides the tools to manage and analyse these data, to extract the relevant information and build forecasting models, fundamental tools in areas such as credit evaluation, marketing, customer relationship management. The course will examine the data preprocessing methods and their importance. We'll cover some of non-parametric models for classification and regression: decision trees, neural networks, support vector machines. Ensemble learning methods (Bagging, Boosting, Stacking, Blended) will be illustrated. The course will address also the analysis of textual data and images. Knowledge and understanding. Acquire the basics of data mining techniques. Understanding how and why to choose between alternative statistical methods, or possibly how to combine different methods. Ability to handle large amounts of data with the help of appropriate, commercial and open source, software. Applying knowledge and understanding. Students develop critical skills through the application of a wide range of statistical and machine learning models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Making judgements. Students develop critical skills through the application of a wide range of machine learning and statistical models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills. Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which 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, in subsequent statistical area teachings, the study of the formal properties of data mining procedures in more complex modeling contexts.	Second year	Second semester	9	SECS-S/01
1047773 - BIG DATA ANALYTICS	Learning goals. The different techniques existing for Big Data management will be illustrated, with a particular emphasis on NoSQL databases. The course will also deal with the problem of collecting Big Data from various sources such as from the web or from the online social networks. This will require also the introduction of the different formats that are commonly used to encode unstructured, semi-structured and structured data and of the different techniques that can be used to automate their processing. Successively, pre-processing techniques, including denoising and imputation of missing data, will be considered. Then, the course will treat dimensionality reduction techniques, based on feature extraction and feature selection. Finally, some statistical learning models, supervised and unsupervised, for the analysis of Big Data, will be presented. Real-world problems will be addressed during the course using suitable software. Knowledge and understanding. The student will learn as to apply some statistical learning techniques for dimensionality reduction, based on feature extraction and feature selection. Moreover, he will know and understand some powerful statistical learning models, supervised and unsupervised, to analyse Big Data. Applying knowledge and understanding. The student will be able to manage Big Data collected from various sources. He will learn as to apply dimensionality reduction techniques, based on feature extraction and feature selection. Moreover, he will be able to choose and apply some powerful statistical learning models to analyse Big Data. Making judgements. Students will develop critical skills through the application of a wide range of machine learning and statistical models. They also will develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They will learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills. Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which 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 will have learned an analytical approach that allows them to tackle Big Data analysis with statistical models and machine learning methods.	Second year	Second semester	6
BIG DATA ANALYTICS		Second year	Second semester	3	INF/01
BIG DATA ANALYTICS		Second year	Second semester	3	SECS-S/01

**Curriculum Data Analyst Gruppo OPZIONALE C altre attività per 6 cfu: The student must acquire 6 CFU from the exams below**
Course		Year	Semester	CFU
AAF1149 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.	First year	First semester	3
AAF1152 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.	First year	First semester	6
AAF1966 - LABORATORY OF STATISTICAL DECISIONS	Learning goals Develop analytical and computational skills to solve statistical decision problems. Knowledge and understanding At the end of the course the students have the ability to understand and solve simple and more advanced exercises of Statistical decision theory. Applying knowledge and understanding Students are required to apply theoretical and computational skills (using the software R) to solve inferential problems formalized as decision problems. Making judgements One of the main goals of practical activities is to develop the ability of comparing and choosing alternative methods, i.e. to refine judgement skills. Communication skills Students acquire the ability of presenting written reports of their practical laboratories. Learning skills The students acquire a series of skills useful for future academic and professional activities.	First year	First semester	3
AAF1883 - Laboratory of Machine learning	Learning goals. The lab consists of the application of machine learning techniques to the analysis of images and/or textual documents. The language used is Python 3.x with the Tensorflow package for the application of Convolutional and Recurrent Neural Networks (deep learning). Knowledge and understanding. Acquire the basics of machine learning techniques. Understanding how and why to choose between alternative methods, or possibly how to combine different methods. Ability to handle large amounts of images or text with the help of appropriate open source software. Applying knowledge and understanding. Students develop critical skills through the application of a wide range of statistical and machine learning models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Making judgements. Students develop critical skills through the application of a wide range of machine learning and statistical models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills. Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which 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 analysis of the images or text documents by machine learning techniques.	First year	Second semester	3
AAF2061 - Laboratory of Applied Statistics	The primary educational objective of the course is the study of applied statistics. Given the vastness of the application domain, it will focus in particular on the quantitative analysis applied to the finance sector and market automation methodologies and algorithms. Students must know how to solve the analytical problems necessary for apply the aforementioned methods and be able to interpret the results that derive from their application to real data. Specific objectives a) Knowledge and ability to understand After attending the course the students must acquire a complete profile of quantitative analyst ("quant"), both regarding the knowledge of the methodologies, as well as regards the ability to implement them in modern programming languages, such as c #, c ++, vb.net, j # and in general, the integrated Visual Studio development environment. b) Ability to apply knowledge and understanding At the end of the course, students are able to model all phases of quantitative analysis, including simulation and modeling processes strategies, calculation of performance indices and construction of empirical probabilities of the main performance indices. c) Autonomy of judgment Students develop critical skills through the creation of new strategies and the corresponding simulation study, both with backtesting and forward testing techniques. Both with simulated data, through mixtures of random processes, and through the historical series observed in the past. d) Communication skills Students, through the study and performance of practical exercises, 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 come also developed through laboratory programming activities and also group research activities. e) Learning ability Students who pass the exam have learned the methods of analysis that allow them to face concrete problems of "quantitative analysis" and if necessary to intervene on any phase of the complex process that goes from the acquisition of the financial data stream in real time, to its statistical analysis for the creation of automation strategies, to the quantitative evaluation of the methodologies. They also have the ability to implement any methodology required in a modern development environment, OOP programming, and advanced graphical interfaces. Statistica Applicata Obiettivi generali L'obiettivo formativo primario dell’insegnamento è lo studio di Temi di Statistica Applicata. Data la vastità del dominio applicativo si focalizzerà in particolare sull'analisi quantitativa applicata al settore della finanza e delle metodologie e algoritmi di automazione dei mercati. Gli studenti dovranno saper risolvere i problemi analitici necessari per applicare i suddetti metodi e saper interpretare i risultati che discendono dalla loro applicazione a dati reali. Obiettivi specifici a) Conoscenza e capacità di comprensione Dopo aver frequentato il corso gli studenti devono acquisire un profilo completo di analista quantitativo ("quant"), sia per quanto concerne la conoscenza delle metodologie, sia per quanto riguarda la capacità di implementarle nell'ambito di linguaggi moderni di programmazione, quali ad esempio c#, c++, vb.net, j# in generale l'ambiente integrato di sviluppo Visual Studio. b) Capacità di applicare conoscenza e comprensione Al termine del corso gli studenti sono in grado modellizzare tutte le fasi dell'analisi quantitativa, inclusi i processi di simulazione, modellizzazione delle strategie, calcolo di indici di performance e costruzione delle distribuzioni empiriche di probabilità dei principali indici di performance. c) Autonomia di giudizio Gli studenti sviluppano capacità critiche attraverso la creazione di nuove strategie e il corrispondente studio simulativo, sia con tecniche di backtesting che forward testing. Sia con dati simulati, mediante misture di processi aleatori, sia mediante le serie storiche osservate nel passato. d) Abilità comunicativa Gli studenti, attraverso lo studio e lo svolgimento di esercizi pratici, acquisiscono il linguaggio tecnico-scientifico della disciplina, che deve essere opportunamente utilizzato sia nelle prove scritte intermedie e finali che nelle prove orali. Le abilità comunicative vengono sviluppate anche attraverso attività programmazione in laboratorio e anche attività di ricerca in gruppi. e) Capacità di apprendimento Gli studenti che superano l’esame hanno appreso i metodi di analisi che consentono loro di affrontare problemi concreti di "quantitative analysis" e se necessario di poter intervenire su qualunque fase del complesso processo che va dall'acquisizione dello stream di dati finanziari in tempo reale, alla sua analisi statistica ai fini della creazione di strategie di automazione, alla valutazione quantitativa delle metodologie. Hanno inoltre la capacità di implementare qualunque metodologia richiesta in un ambiente di sviluppo moderno, di programmazione OOP, e interfacce grafiche avanzate. The primary educational objective of the course is the study of applied statistics. Given the vastness of the application domain, it will focus in particular on the quantitative analysis applied to the finance sector and market automation methodologies and algorithms. Students must know how to solve the analytical problems necessary for apply the aforementioned methods and be able to interpret the results that derive from their application to real data. Specific objectives a) Knowledge and ability to understand After attending the course the students must acquire a complete profile of quantitative analyst ("quant"), both regarding the knowledge of the methodologies, as well as regards the ability to implement them in modern programming languages, such as c #, c ++, vb.net, j # and in general, the integrated Visual Studio development environment. b) Ability to apply knowledge and understanding At the end of the course, students are able to model all phases of quantitative analysis, including simulation and modeling processes strategies, calculation of performance indices and construction of empirical probabilities of the main performance indices. c) Autonomy of judgment Students develop critical skills through the creation of new strategies and the corresponding simulation study, both with backtesting and forward testing techniques. Both with simulated data, through mixtures of random processes, and through the historical series observed in the past. d) Communication skills Students, through the study and performance of practical exercises, 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 come also developed through laboratory programming activities and also group research activities. e) Learning ability Students who pass the exam have learned the methods of analysis that allow them to face concrete problems of "quantitative analysis" and if necessary to intervene on any phase of the complex process that goes from the acquisition of the financial data stream in real time, to its statistical analysis for the creation of automation strategies, to the quantitative evaluation of the methodologies. They also have the ability to implement any methodology required in a modern development environment, OOP programming, and advanced graphical interfaces.	First year	Second semester	3
AAF1884 - Laboratory of data driven decision making	The primary educational objective of the laboratory is students' learning and practice of the main tools for Data Driven Decision Making, that is the use of computer tools to analyze data and formalize optimization or decision models and produce decisions that create value. Knowledge and ability to understand After attending the laboratory, students will be able to use decision support methods (like, the Analytical Hierchical Process), optimization solvers (like CPLEX or Gurobi) and computer algorithms for modelling multicriteria decision and optimization problems. Ability to apply knowledge and understanding The models are formalized in the realm of problems. The most appropriate quantitative method, experimenting with the effectiveness of the problem. Autonomy of judgment Students develop critical skills through the application of modeling, analysis and optimization to a broad set of decision problems. They also develop the critical sense through the comparison between alternative solutions to the same problem using methods of analysis and realistic scenarios different from each other. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills Students, through the study and the carrying out of the practical exercises, acquire the technical-scientific language of the course, which should be used in the tests. Communication skills are also developed through group activities. Learning ability Students who pass the exam have acquired the main methods of analysis and optimization of decision problems that allow them to face decision-making and quantitative management in competitive nowadays enterprises.	Second year	First semester	3

**Curriculum Data Analyst Gruppo B2 OPZIONALE a scelta un esame da 6 cfu: The student must acquire 6 CFU from the exams below**
Course		Year	Semester	CFU	SSD
1052019 - Bayesian modelling	General goals Knowledge at an intermediate and advanced level of the main issues in Bayesian statistics. Ability to apply Bayesian statistical techniques to applicative context. Knowledge and understanding Knowledge and understanding of the Bayesian approach to statistical inference, of its models and of its methodologies Applying knowledge and understanding Ability to apply Bayesian statistical methods for inferential problems in real-data problems Making judgements Ability of choosing appropriate Bayesian methods and models in different inferential problems Communication skills Ability of communicating results of the analyses in written and oral form Learning skills Students acquire skills useful to approach more advanced topics in Bayesian inference, Advanced data analysis, Statistical computing and Mathematical statistics	First year	First semester	6
Bayesian modelling		First year	First semester	3	SECS-S/01
Bayesian modelling		First year	First semester	3	SECS-S/01
1022720 - Generalized Linear Models	Learning goals. The main educational objective of the course is the knowledge of the analysis of Generalized Linear Models in their theoretical, methodological and applicative aspects. Knowledge and understanding. After attending the course the students know and can apply the methods of statistical analysis to all those situations that can be represented in the Generalized Linear Models family. Applying knowledge and understanding. At the end of the course students are able to identify which types of situations can be analyzed in the family of generalized linear models, identifying sampling model, link function and linear predictor. They are also able to formulate substantive questions in parametric terms and answering to these questions with the tools of statistical analysis. Making judgements. Students develop critical skills through the selection, estimation and validation of the statistical model in different situations that can be represented in the Generalized Linear Models family. Communication skills. Particular attention is paid to the technical-scientific language of the discipline, which must be used appropriately in the final test. Learning skills. Students who pass the exam have acquired the ability to associate to the different real situation, the Generalized Linear Model that best represent them and to evaluate the quality of this representation. These tools are useful both for insights into the possible application fields and for the study of parametric models in general.	First year	First semester	6	SECS-S/02
10589835 - computational statistics	Learning goals The main goal of the course is to learn about common general computational tools and methodologies to perform reliable statistical analyses. Students will be able to understand the theoretical foundations of the most important methods; to appropriately implement and apply computational statistical procedures; to interpret the results deriving from their applications to real data. . (a) Knowledge and understanding After attending the course, students will know and understand the most important computational techniques in statistical analysis. In addition, students will be able to appropriately implement the learned tools with the statistical software R and to develop original ideas often in a research context. (b) Applying knowledge and understanding At the end of the course, students will be able to formalize statistical problems from a computational point of view, to apply the learned methods to solve them, also in contexts not covered in the lessons, and to interpret the results deriving from their applications to real data. c) Making judgements Students will develop critical skills through the application of computational methodologies to a wide range of statistical problems and through the comparison of alternative solutions to the same problem by using different tools. Furthermore, they will learn to interpret critically the results obtained by applying procedures to real datasets. (d) Communication skills. By studying and carrying out practical exercises, students will acquire the technical-scientific language of the discipline, which must be suitably used in the final written test. Communication skills will also be developed through group activities. (e) Learning skills Students who pass the exam have learned computational techniques useful in statistical analysis and to work self-sufficiently to face the complexity of the statistical problems.	First year	First semester	6	SECS-S/01
10596221 - Panel Data Models for Finance	General Targets: Prior educational teaching concern is the students understanding of the main (Economic Statistics Modeling) problems and methods for studying Panel Data Models for Finance. Furthermore, students should know both how to solve analytical problems, in order to apply the appropriate methodology, and to interpret results obtained from empirical applications to actual data. Specific Targets: a) Knowledge and capability in understanding. After attending the course, students know and understand main problems of Panel Data modeling concerning the Banking System in the international and more specific European context. In particular, the course will account for the logic for building empirical models, related to the underlying economic theory (and the consequent subdivisions in endogenous and exogenous variables), with one or more equations in order to evaluate the management of the Banking System (both from the employment of the inputs and the financial products). An important part will deal with the analysis of the default risk due to Non-Performing Loans and the consequent Systemic Risk. We will study the main estimation methods for solving problems pertaining the reference sectors (quadratic methods with different types of metrics and the comparison with maximum likelihood) and the formulas implementable using the estimated functions, necessary for solving financial problems. b) Capability of applying knowledge and comprehension At the end of the course students are able to formalize and solve problems by means of specific methods as well as treating fundamental models of Economic Statistics with Panel Data and extend them to other specific contexts (such as that of Insurance Companies). Finally, students will be able to apply the methods studied to real data and interpret results correctly also from a theoretical point of view. c) Autonomy in assessment. Students develop analytical skills and capacity of facing different alternative approaches for solving actual empirical problems. d) Communication ability. Students learn technical language which is appropriate for the subject studied and that will be used at the oral and written exam, by means of practical exercises. e) Learning capacity. Students passing the exam are capable to extend the methodology studied also to other fields and derive conclusions.	First year	Second semester	6	SECS-S/03
10596191 - Applied Statistics	The primary educational objective of the course is the study of applied statistics. Given the vastness of the application domain, it will focus in particular on the quantitative analysis applied to the finance sector and market automation methodologies and algorithms. Students must know how to solve the analytical problems necessary for apply the aforementioned methods and be able to interpret the results that derive from their application to real data. Specific objectives a) Knowledge and ability to understand After attending the course the students must acquire a complete profile of quantitative analyst ("quant"), both regarding the knowledge of the methodologies, as well as regards the ability to implement them in modern programming languages, such as c #, c ++, vb.net, j # and in general, the integrated Visual Studio development environment. b) Ability to apply knowledge and understanding At the end of the course, students are able to model all phases of quantitative analysis, including simulation and modeling processes strategies, calculation of performance indices and construction of empirical probabilities of the main performance indices. c) Autonomy of judgment Students develop critical skills through the creation of new strategies and the corresponding simulation study, both with backtesting and forward testing techniques. Both with simulated data, through mixtures of random processes, and through the historical series observed in the past. d) Communication skills Students, through the study and performance of practical exercises, 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 come also developed through laboratory programming activities and also group research activities. e) Learning ability Students who pass the exam have learned the methods of analysis that allow them to face concrete problems of "quantitative analysis" and if necessary to intervene on any phase of the complex process that goes from the acquisition of the financial data stream in real time, to its statistical analysis for the creation of automation strategies, to the quantitative evaluation of the methodologies. They also have the ability to implement any methodology required in a modern development environment, OOP programming, and advanced graphical interfaces. Statistica Applicata Obiettivi generali L'obiettivo formativo primario dell’insegnamento è lo studio di Temi di Statistica Applicata. Data la vastità del dominio applicativo si focalizzerà in particolare sull'analisi quantitativa applicata al settore della finanza e delle metodologie e algoritmi di automazione dei mercati. Gli studenti dovranno saper risolvere i problemi analitici necessari per applicare i suddetti metodi e saper interpretare i risultati che discendono dalla loro applicazione a dati reali. Obiettivi specifici a) Conoscenza e capacità di comprensione Dopo aver frequentato il corso gli studenti devono acquisire un profilo completo di analista quantitativo ("quant"), sia per quanto concerne la conoscenza delle metodologie, sia per quanto riguarda la capacità di implementarle nell'ambito di linguaggi moderni di programmazione, quali ad esempio c#, c++, vb.net, j# in generale l'ambiente integrato di sviluppo Visual Studio. b) Capacità di applicare conoscenza e comprensione Al termine del corso gli studenti sono in grado modellizzare tutte le fasi dell'analisi quantitativa, inclusi i processi di simulazione, modellizzazione delle strategie, calcolo di indici di performance e costruzione delle distribuzioni empiriche di probabilità dei principali indici di performance. c) Autonomia di giudizio Gli studenti sviluppano capacità critiche attraverso la creazione di nuove strategie e il corrispondente studio simulativo, sia con tecniche di backtesting che forward testing. Sia con dati simulati, mediante misture di processi aleatori, sia mediante le serie storiche osservate nel passato. d) Abilità comunicativa Gli studenti, attraverso lo studio e lo svolgimento di esercizi pratici, acquisiscono il linguaggio tecnico-scientifico della disciplina, che deve essere opportunamente utilizzato sia nelle prove scritte intermedie e finali che nelle prove orali. Le abilità comunicative vengono sviluppate anche attraverso attività programmazione in laboratorio e anche attività di ricerca in gruppi. e) Capacità di apprendimento Gli studenti che superano l’esame hanno appreso i metodi di analisi che consentono loro di affrontare problemi concreti di "quantitative analysis" e se necessario di poter intervenire su qualunque fase del complesso processo che va dall'acquisizione dello stream di dati finanziari in tempo reale, alla sua analisi statistica ai fini della creazione di strategie di automazione, alla valutazione quantitativa delle metodologie. Hanno inoltre la capacità di implementare qualunque metodologia richiesta in un ambiente di sviluppo moderno, di programmazione OOP, e interfacce grafiche avanzate. The primary educational objective of the course is the study of applied statistics. Given the vastness of the application domain, it will focus in particular on the quantitative analysis applied to the finance sector and market automation methodologies and algorithms. Students must know how to solve the analytical problems necessary for apply the aforementioned methods and be able to interpret the results that derive from their application to real data. Specific objectives a) Knowledge and ability to understand After attending the course the students must acquire a complete profile of quantitative analyst ("quant"), both regarding the knowledge of the methodologies, as well as regards the ability to implement them in modern programming languages, such as c #, c ++, vb.net, j # and in general, the integrated Visual Studio development environment. b) Ability to apply knowledge and understanding At the end of the course, students are able to model all phases of quantitative analysis, including simulation and modeling processes strategies, calculation of performance indices and construction of empirical probabilities of the main performance indices. c) Autonomy of judgment Students develop critical skills through the creation of new strategies and the corresponding simulation study, both with backtesting and forward testing techniques. Both with simulated data, through mixtures of random processes, and through the historical series observed in the past. d) Communication skills Students, through the study and performance of practical exercises, 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 come also developed through laboratory programming activities and also group research activities. e) Learning ability Students who pass the exam have learned the methods of analysis that allow them to face concrete problems of "quantitative analysis" and if necessary to intervene on any phase of the complex process that goes from the acquisition of the financial data stream in real time, to its statistical analysis for the creation of automation strategies, to the quantitative evaluation of the methodologies. They also have the ability to implement any methodology required in a modern development environment, OOP programming, and advanced graphical interfaces.	First year	Second semester	6	SECS-S/01
1047774 - Forecasting models	Learning goals. Main goal is the ability to conduct a statistically correct analysis and a forecasting exercise in all cases where time a fundamental feature of the analysed data Knowledge and understanding. Demonstrate knowledge and understanding ability that extend those typical of the course of time series of the first degree, and allow also to develop original forecast proposal, on integrating different methodologies. Applying knowledge and understanding. Solving complex problem related to the time dynamics of several phenomena measured on different scales and with different methods. Making judgements. Ability to integrate knowledge and perform analysis of complex phenomena integrating different methodologies, also in presence of limited, incomplete or biased information. Communication skills. Communicate the results of a deep statistical analysis, illustrating the main steps and motivation also to non-specialst audience. Learning skills. To study in a self-managed and autonomous fashion.	Second year	First semester	6	SECS-S/01
10589563 - DATA DRIVEN DECISION MAKING	General Managers worldwide, beyond their personal experience, rely more and more on the use of quantitative decision models which allow to take advantage of today’s data availability. Morover, new computational tools, including algorithms, cloud computing and distributed processing, make it possible to both develop and compute analytical models in a very short time, meeting the requirement of practical applications and often using real time data. Data Driven Decision Making is the new paradigm for managers to make better, evidence based, more rational, transparent and reliable decisions. In this context, the primary educational objective of the course is students' learning of the main decision problems that arise in real world and the quantitative methods to model them and to feed them with adequate data. Students must also be able to correctly use, for decision-making and management purposes, computer tools to analyze data generated by real problems in different contexts (e.g. service management, marketing, transportation, operations management and production, and finance) through the analysis of several case studies. Specific objectives a) Knowledge and ability to understand After attending the course the students know and classify the main decision problems arising in real world organization and the main analytical methods (decision and optimization models and algorithms) to be used to support a Manager during his/her decision process. b) Ability to apply knowledge and understanding At the end of the course the students are able to formalize real problems in terms of decision problems and to apply the specific methods taught in the course to solve them. They are also able to classify the type of problem to it the most appropriate quantitative method, experimenting the effectiveness for decisional purposes also on real problems. c) Autonomy of judgment Students develop critical skills through the application of modeling, decision analysis and multi objective optimization methodologies to a broad set of practical problems. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using methods of analysis and realistic scenarios different from each other. They learn to critically interpret the results obtained by applying the procedures to real data sets. d) Communication skills Students, through the study and the carrying out of practical exercises, acquire the technical- scientific language of the course, which must be properly used both in the intermediate and final written tests and in the oral tests. Communication skills are also developed through group activities. e) Learning ability Students who pass the exam have learned methods of decision analysis and multiobjective optimization that allow them to face, decision-making problems and optimization on complex organizations.	Second year	First semester	6	MAT/09
10589824 - statistical analysis of relational structures	General goals Learning advanced statistical methods for relational data (theory and practice) Knowledge and understanding Knowledge and understanding of theory and methodology related to the study of relational data (for instance, dimensionality reduction, classification). Applying knowledge and understanding Ability to apply appropriate methods and approaches in the analysis of relational data Making judgements Ability of choosing appropriate methods, models and software in different problems; ability of discussing and interpreting results of applications of the methodologies on real data Communication skills Ability of using appropriate scientific language and of communicating results of the analyses in written reports Learning skills Students acquire skills useful to approach more advanced topics in Statistics	Second year	First semester	6	SECS-S/01
10589567 - SPATIAL STATISTICS AND STATISTICAL TOOLS FOR ENVIRONMENTAL DATA	Learning goals The student at the end of the course should be able to use with knowledge advanced modeling and exploratory techniques specifically developed for spatially dependent data. This is achieved by assigning several homeworks on real data. Practical sessions with the R software are part of each lecture, so to allow students to implement what is taught in the theoretical part. Among the expected results, ability to elaborate spatial environmental data using R software, ability to interpret the results obtained, ability to choose the most suitable statistical models according to the hypotheses they are founded on and to their compatibility with the available data. Knowledge and understanding The student will be able to understand the main tools for the analysis of spatial and spatio-temporal data. Applying knowledge and understanding Students will be involved in the discussion and analysis of case studies using the open source statistical software R. Students will be asked to prepare and discuss a presentation of the results of their homeworks. The presentation will be given in front of the class and discussed. Making judgements Through the homeworks and the final presentations discussions, students will develop judgements capacity in terms of theoretical choices in representation of real world phenomena. Communication skills Students will be asked to prepare and discuss a presentation of the results of their homeworks. The presentation will be given in front of the class and discussed. This procedure will help the student to develop his/her ability to communicate the results of its work. Learning skills One of the aims of the course is to build a statistical glossary and a dictionary of specific statistical concepts that will allow the student to read and understand scientific papers using advanced statistical tools in the analysis of environmental data.	Second year	First semester	6	SECS-S/02
10596189 - Social Network Analysis	1. Knowledge and understanding: what students should know on the course topics after having passed the exam After passing the exam, students have got a basic knowledge on both the history and the development of network analysis as an autonomous methodology to study relational data; on the main intellectual traditions, and the most relevant scholars who have contributed to its growth in the studies on the structure and the dynamics within and between groups (Moreno, Freeman, Mit, Harvard School). Moreover, students know the main properties of a relational data matrix; some basic concepts about the nodes and the relationships (lines, directions); they are able to calculate some specific metrics (density, centrality and centralization, betweenness, closeness, clustering); to use some statistical techniques (components, core and clique), and create adequate graphs representation of social networks. 2. Applying knowledge and understanding: what students should be able to do after having passed the exam After passing the exam, students are able a) to apply theoretical schemes to complex social phenomena, traducing them in concrete research questions, smart objectives, and working hypothesis; b) students learn to gather relational data and treat them employing appropriate social network techniques, c) they show a good confidence with using Sas Viya and Ucinet software. 3. Making judgements: activities through which critical faculties should be developed. Critical capabilities are expected to be developed through the involvement of the students in active class-work sessions. Indeed, 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 working group and the presentation/discussion of the results of the class activities (data analysis presentations). 5. Learning skills: ability to continue studying the topics. The competences acquired should contribute to both strengthen students’ knowledge on social networks, and improve their capabilities to learn more advanced methods and techniques of network analysis about complex social phenomena at theoretical and applied level.	Second year	First semester	6	SPS/07
1047773 - BIG DATA ANALYTICS	Learning goals. The different techniques existing for Big Data management will be illustrated, with a particular emphasis on NoSQL databases. The course will also deal with the problem of collecting Big Data from various sources such as from the web or from the online social networks. This will require also the introduction of the different formats that are commonly used to encode unstructured, semi-structured and structured data and of the different techniques that can be used to automate their processing. Successively, pre-processing techniques, including denoising and imputation of missing data, will be considered. Then, the course will treat dimensionality reduction techniques, based on feature extraction and feature selection. Finally, some statistical learning models, supervised and unsupervised, for the analysis of Big Data, will be presented. Real-world problems will be addressed during the course using suitable software. Knowledge and understanding. The student will learn as to apply some statistical learning techniques for dimensionality reduction, based on feature extraction and feature selection. Moreover, he will know and understand some powerful statistical learning models, supervised and unsupervised, to analyse Big Data. Applying knowledge and understanding. The student will be able to manage Big Data collected from various sources. He will learn as to apply dimensionality reduction techniques, based on feature extraction and feature selection. Moreover, he will be able to choose and apply some powerful statistical learning models to analyse Big Data. Making judgements. Students will develop critical skills through the application of a wide range of machine learning and statistical models. They also will develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They will learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills. Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which 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 will have learned an analytical approach that allows them to tackle Big Data analysis with statistical models and machine learning methods.	Second year	Second semester	6
BIG DATA ANALYTICS		Second year	Second semester	3	INF/01
BIG DATA ANALYTICS		Second year	Second semester	3	SECS-S/01
1017084 - ECONOMETRICS	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.	Second year	Second semester	6	SECS-P/05

**Curriculum Demografico Sociale Gruppo OPZIONALE B esami a scelta per 21 cfu: The student must acquire 21 CFU from the exams below**
Course		Year	Semester	CFU	SSD
1022720 - Generalized Linear Models	Learning goals. The main educational objective of the course is the knowledge of the analysis of Generalized Linear Models in their theoretical, methodological and applicative aspects. Knowledge and understanding. After attending the course the students know and can apply the methods of statistical analysis to all those situations that can be represented in the Generalized Linear Models family. Applying knowledge and understanding. At the end of the course students are able to identify which types of situations can be analyzed in the family of generalized linear models, identifying sampling model, link function and linear predictor. They are also able to formulate substantive questions in parametric terms and answering to these questions with the tools of statistical analysis. Making judgements. Students develop critical skills through the selection, estimation and validation of the statistical model in different situations that can be represented in the Generalized Linear Models family. Communication skills. Particular attention is paid to the technical-scientific language of the discipline, which must be used appropriately in the final test. Learning skills. Students who pass the exam have acquired the ability to associate to the different real situation, the Generalized Linear Model that best represent them and to evaluate the quality of this representation. These tools are useful both for insights into the possible application fields and for the study of parametric models in general.	First year	First semester	6	SECS-S/02
10589539 - Health Statistics	General goals Principles and methods to study the health conditions of a population Specific goals Knowledge and understanding After attending the course students are able to handle problems related to the evaluation of health conditions of single units and of a population Applying knowledge and understanding After attending the course students are able to handle the measure of health conditions of single units and of a population Making judgements Judgement skills are improved by discussion and use of alternative methods and practical and real examples Communication skills Students will develop capacity of communicating results through oral presentation of practical problems and by the final exam Learning skills Students who pass the exam have acquired skills and methods in Social epidemiology and in statistical methods for public health	First year	First semester	6	SECS-S/05
10589614 - Socio-environmental demography	Expected learning outcomes Ability to approach the literature on Population and Economy, Population and Environment, Population and Welfare System Handling of main demo-economic models Skills to be acquired Orientation in the use of the main models that link demographic and economic variables Orientation in sources that inform about the relationship between demographic variables and environmental impact indexes	First year	First semester	6	SECS-S/04
1018629 - Statistical Decision Theory	Learning goals Introduction to problems and methods of Statistical decision theory and to its main approaches (Bayesian and frequentist). Students acquire the ability: - to formalize statistical problems as decision problems, - to compare different solutions - to apply decisional methods to real data Knowledge and understanding knowledge and understanding of: - general decision problems - statistical problems as decision problems - different approaches to statistical decision problems (Bayesian and frequentist) Applying knowledge and understanding At the end of the course students are able to: - formalize statistical problems as decision problems - apply and compare different decisional method to the most important models - apply decision theory to new models - interpret the results of their analysis Making judgements Students develop judgement skills by: - applying and comparing different decisional methods to a wide range of statistical models - interpreting the results from the use of alternative decision methods on real data Communication skills Students develop communication skills by: - solving and presenting problems in written and oral form - group activity Learning skills The comparative-analytical methodology used in the course provides students with a learning capacity that is an important basis to approach following courses in the statistical area of the master program and of more advanced programs (PhD in Statistics)."	First year	First semester	9	SECS-S/01
10596189 - Social Network Analysis	1. Knowledge and understanding: what students should know on the course topics after having passed the exam After passing the exam, students have got a basic knowledge on both the history and the development of network analysis as an autonomous methodology to study relational data; on the main intellectual traditions, and the most relevant scholars who have contributed to its growth in the studies on the structure and the dynamics within and between groups (Moreno, Freeman, Mit, Harvard School). Moreover, students know the main properties of a relational data matrix; some basic concepts about the nodes and the relationships (lines, directions); they are able to calculate some specific metrics (density, centrality and centralization, betweenness, closeness, clustering); to use some statistical techniques (components, core and clique), and create adequate graphs representation of social networks. 2. Applying knowledge and understanding: what students should be able to do after having passed the exam After passing the exam, students are able a) to apply theoretical schemes to complex social phenomena, traducing them in concrete research questions, smart objectives, and working hypothesis; b) students learn to gather relational data and treat them employing appropriate social network techniques, c) they show a good confidence with using Sas Viya and Ucinet software. 3. Making judgements: activities through which critical faculties should be developed. Critical capabilities are expected to be developed through the involvement of the students in active class-work sessions. Indeed, 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 working group and the presentation/discussion of the results of the class activities (data analysis presentations). 5. Learning skills: ability to continue studying the topics. The competences acquired should contribute to both strengthen students’ knowledge on social networks, and improve their capabilities to learn more advanced methods and techniques of network analysis about complex social phenomena at theoretical and applied level.	Second year	First semester	6	SPS/07
10589573 - Evaluation tools and methods	Learning goals The course introduces to the methods for the quantitative evaluation of public policies, focusing on the various assessment concepts, on the theoretical framework, on the statistical techniques used and on the sources of data needed. Knowledge and understanding. Applying knowledge and understanding. Making judgements. Communication skills. Learning skills.	Second year	First semester	6	SECS-S/05

**Curriculum Demografico Sociale Gruppo OPZIONALE C Altre attività per 6 cfu : The student must acquire 6 CFU from the exams below**
Course		Year	Semester	CFU
AAF1149 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.	First year	First semester	3
AAF1152 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.	First year	First semester	6
AAF1898 - Laboratory of social epidemiology	Learning goals The learning goal of the course is to provide students with the main conceptual and operational tools for assessing the health conditions of the population and its monitoring over time. Attention is paid to health inequalities in the territory, between genders and between social classes. Knowledge and understanding At the end of the course, students will be able to: - correctly use the main concepts and the main tools used in epidemiology; - use the most appropriate methods for calculating intensity, association and impact measurements, checking possible sources of distortion; illustrate and correctly interpret the results obtained; - to deal with the concepts of causality, causal mechanism and causal factor in epidemiology; ¬- elaborate / select the analytical and synthetic indicators necessary for the evaluation and monitoring of the health condition of a population. Applying knowledge and understanding The course will allow students to deal with the most relevant problems, of a conceptual and operational nature, related to the measurement of health and to the evaluation of the relationships between disease onset and risk factors Ability to apply knowledge and understanding The course will provide knowledge and, at the same time, will make the tools necessary for the assessment of the health conditions of a population in real conditions available to students. For this reason, particular importance is given to the available data sources and the evaluation of their quality. Making judgments Students will be stimulated to develop their analytical and judgment skills by dealing with empirical situations that they will have to describe through appropriate indicators. Communication skills Students will prepare and present small research on specific topics. In this way they will be able to acquire the technical-scientific language of the discipline and to experiment themselves in the presentation of the results obtained. Learning skills At the end of the course, the students attending the Laboratory will have to demonstrate that they are able to develop a research which - although of limited breadth and of a descriptive nature - covers the main phases of the preparation of a report on specific aspects of the health status of a population or group	Second year	First semester	3
AAF1883 - Laboratory of Machine learning	Learning goals. The lab consists of the application of machine learning techniques to the analysis of images and/or textual documents. The language used is Python 3.x with the Tensorflow package for the application of Convolutional and Recurrent Neural Networks (deep learning). Knowledge and understanding. Acquire the basics of machine learning techniques. Understanding how and why to choose between alternative methods, or possibly how to combine different methods. Ability to handle large amounts of images or text with the help of appropriate open source software. Applying knowledge and understanding. Students develop critical skills through the application of a wide range of statistical and machine learning models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Making judgements. Students develop critical skills through the application of a wide range of machine learning and statistical models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills. Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which 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 analysis of the images or text documents by machine learning techniques.	Second year	Second semester	3

**Curriculum Biostatistico Gruppo opzionale C altre attività per 6 CFU: The student must acquire 6 CFU from the exams below**
Course		Year	Semester	CFU
AAF1152 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.	First year	First semester	6
AAF1149 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.	First year	First semester	3
AAF1898 - Laboratory of social epidemiology	Learning goals The learning goal of the course is to provide students with the main conceptual and operational tools for assessing the health conditions of the population and its monitoring over time. Attention is paid to health inequalities in the territory, between genders and between social classes. Knowledge and understanding At the end of the course, students will be able to: - correctly use the main concepts and the main tools used in epidemiology; - use the most appropriate methods for calculating intensity, association and impact measurements, checking possible sources of distortion; illustrate and correctly interpret the results obtained; - to deal with the concepts of causality, causal mechanism and causal factor in epidemiology; ¬- elaborate / select the analytical and synthetic indicators necessary for the evaluation and monitoring of the health condition of a population. Applying knowledge and understanding The course will allow students to deal with the most relevant problems, of a conceptual and operational nature, related to the measurement of health and to the evaluation of the relationships between disease onset and risk factors Ability to apply knowledge and understanding The course will provide knowledge and, at the same time, will make the tools necessary for the assessment of the health conditions of a population in real conditions available to students. For this reason, particular importance is given to the available data sources and the evaluation of their quality. Making judgments Students will be stimulated to develop their analytical and judgment skills by dealing with empirical situations that they will have to describe through appropriate indicators. Communication skills Students will prepare and present small research on specific topics. In this way they will be able to acquire the technical-scientific language of the discipline and to experiment themselves in the presentation of the results obtained. Learning skills At the end of the course, the students attending the Laboratory will have to demonstrate that they are able to develop a research which - although of limited breadth and of a descriptive nature - covers the main phases of the preparation of a report on specific aspects of the health status of a population or group	First year	First semester	3
AAF1966 - LABORATORY OF STATISTICAL DECISIONS	Learning goals Develop analytical and computational skills to solve statistical decision problems. Knowledge and understanding At the end of the course the students have the ability to understand and solve simple and more advanced exercises of Statistical decision theory. Applying knowledge and understanding Students are required to apply theoretical and computational skills (using the software R) to solve inferential problems formalized as decision problems. Making judgements One of the main goals of practical activities is to develop the ability of comparing and choosing alternative methods, i.e. to refine judgement skills. Communication skills Students acquire the ability of presenting written reports of their practical laboratories. Learning skills The students acquire a series of skills useful for future academic and professional activities.	First year	First semester	3
AAF1883 - Laboratory of Machine learning	Learning goals. The lab consists of the application of machine learning techniques to the analysis of images and/or textual documents. The language used is Python 3.x with the Tensorflow package for the application of Convolutional and Recurrent Neural Networks (deep learning). Knowledge and understanding. Acquire the basics of machine learning techniques. Understanding how and why to choose between alternative methods, or possibly how to combine different methods. Ability to handle large amounts of images or text with the help of appropriate open source software. Applying knowledge and understanding. Students develop critical skills through the application of a wide range of statistical and machine learning models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Making judgements. Students develop critical skills through the application of a wide range of machine learning and statistical models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills. Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which 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 analysis of the images or text documents by machine learning techniques.	First year	Second semester	3
AAF1884 - Laboratory of data driven decision making	The primary educational objective of the laboratory is students' learning and practice of the main tools for Data Driven Decision Making, that is the use of computer tools to analyze data and formalize optimization or decision models and produce decisions that create value. Knowledge and ability to understand After attending the laboratory, students will be able to use decision support methods (like, the Analytical Hierchical Process), optimization solvers (like CPLEX or Gurobi) and computer algorithms for modelling multicriteria decision and optimization problems. Ability to apply knowledge and understanding The models are formalized in the realm of problems. The most appropriate quantitative method, experimenting with the effectiveness of the problem. Autonomy of judgment Students develop critical skills through the application of modeling, analysis and optimization to a broad set of decision problems. They also develop the critical sense through the comparison between alternative solutions to the same problem using methods of analysis and realistic scenarios different from each other. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills Students, through the study and the carrying out of the practical exercises, acquire the technical-scientific language of the course, which should be used in the tests. Communication skills are also developed through group activities. Learning ability Students who pass the exam have acquired the main methods of analysis and optimization of decision problems that allow them to face decision-making and quantitative management in competitive nowadays enterprises.	Second year	First semester	3

**Curriculum Data Analyst Gruppo A OPZIONALE un esame da 9 cfu: The student must acquire 9 CFU from the exams below**
Course		Year	Semester	CFU	SSD	Language
1022318 - PROBABILITY	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).	First year	First semester	9	MAT/06
10600045 - Stochastic Processes for applied sciences	Learning goals General goals. The primary educational goal of teaching is to learn the main concepts related to the theory of stochastic processes. Students must also be able to apply the methods and be able to interpret the results. Knowledge and understanding. After attending the course the students know and understand the main concepts on discrete and continuous processes. Applying knowledge and understanding. At the end of the course the students are able to formalize real problems and to solve them by applying the specific methods of the discipline. Making judgements. Students develop critical skills through the application of the theory of stochastic processes. Communication skills. Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which must be used appropriately in both the written and oral tests. Learning skills. Students who pass the exam have learned a method of analysis that allows them to tackle the subsequent teachings of statistical-probabilistic area.	First year	Second semester	9	MAT/06

**Curriculum Demografico Sociale Gruppo A OPZIONALE un esame da 9 cfu: The student must acquire 9 CFU from the exams below**
Course		Year	Semester	CFU	SSD	Language
1022318 - PROBABILITY	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).	First year	First semester	9	MAT/06
10600045 - Stochastic Processes for applied sciences	Learning goals General goals. The primary educational goal of teaching is to learn the main concepts related to the theory of stochastic processes. Students must also be able to apply the methods and be able to interpret the results. Knowledge and understanding. After attending the course the students know and understand the main concepts on discrete and continuous processes. Applying knowledge and understanding. At the end of the course the students are able to formalize real problems and to solve them by applying the specific methods of the discipline. Making judgements. Students develop critical skills through the application of the theory of stochastic processes. Communication skills. Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which must be used appropriately in both the written and oral tests. Learning skills. Students who pass the exam have learned a method of analysis that allows them to tackle the subsequent teachings of statistical-probabilistic area.	First year	Second semester	9	MAT/06

**Curr. Demografico Sociale gruppo opzionale A1 1 esame a scelta per 9 crediti: The student must acquire 9 CFU from the exams below**
Course		Year	Semester	CFU	SSD	Language
1022798 - DATA MINING AND CLASSIFICATION	Learning goals Thanks to technological advances, the acquisition of data has become inexpensive and big data sets are easily obtained, for example, via Internet, e-commerce or by electronic banking services. Such data can be stored in data warehouses and data marts specifically intended to support business decisions. Data mining provides the tools to manage and analyse these data, to extract the relevant information and build forecasting models, fundamental tools in areas such as credit evaluation, marketing, customer relationship management. The course will examine the data preprocessing methods and their importance. We'll cover some of non-parametric models for classification and regression: decision trees, neural networks, support vector machines. Ensemble learning methods (Bagging, Boosting, Stacking, Blended) will be illustrated. The course will address also the analysis of textual data and images. Knowledge and understanding. Acquire the basics of data mining techniques. Understanding how and why to choose between alternative statistical methods, or possibly how to combine different methods. Ability to handle large amounts of data with the help of appropriate, commercial and open source, software. Applying knowledge and understanding. Students develop critical skills through the application of a wide range of statistical and machine learning models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Making judgements. Students develop critical skills through the application of a wide range of machine learning and statistical models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills. Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which 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, in subsequent statistical area teachings, the study of the formal properties of data mining procedures in more complex modeling contexts.	First year	Second semester	9	SECS-S/01
1038458 - Longitudinal and survival data analysis	Learning goals. Learning goal of the course is to acquire a basic knowledge of methods for the analysis of survival and longitudinal data. Knowledge and understanding. At the end of the course, the students have a basic knowledge of regression models for survival and longitudinal data Applying knowledge and understanding. Thanks to practical, computer-aided, classes, students learn how to apply regression models to observed survival and longitudinal data. Making judgements. Reviewing different estimators make students able in making judgements on observed data. Communication skills. At the ned of the course, students acquire basic notation and communication skills to be used in the context of the analysis of survival and longitudinal data. Learning skills. Students with a positive mark acquire a basic knoledge of surival and longitudinal data analysis that can be used in different empirical application fields.	First year	Second semester	9	SECS-S/01

PProgramme regulations

Regulations of the Masters Degree in Statistical Sciences
LM-82 Statistical Sciences Class
Academic year 2021/2022
Activated years: I and II

Specific learning outcomes
The degree programme aims to train professional figures capable of managing the entire process of acquisition, modelling and analysis of statistical data for explicative or decision purposes in relation to complex phenomena in a variety of contexts, albeit mainly at an organisational or experimental level.
Specific learning outcomes include:

acquiring a sound knowledge of the theory of stochastic processes;
knowledge of principles and methods of planning of statistical surveys and experiments;
competence in developing and using specific statistical models for diverse contexts of application;
acquiring competencies to manage, also on the IT level, large databases;
ability to work autonomously and in a team to solve application problems;
written and oral communication skills, particularly in relation to specialized terminology;
acquiring a basic knowledge which allows a constant update;
acquiring adequate knowledge to have access to national and international PhD programmes in core subjects.

The Masters programme offers a wide common education in statistics and probabilistic studies leading to three different specific curricula, with activities aimed to ensure adequate competencies in relation to the expected professional figures.
"Biostatistics" Curriculum – It aims to train a professional figure who can manage planning processes related to medical and pharmaceutical experiments, surveys and studies, as well as modelling and data analysis with particular dependence structures (survival, longitudinal, genetic, and environmental data, and spatial-temporal series).
"Data Analyst" Curriculum
It aims to train a professional figure who, besides possessing traditional statisticians’ skills, is capable of managing, gathering and analyse Big Data which, given their growing complexity, are changing the way decisions are taken in several strategic fields, going from medicine to business management, from marketing to scientific research.
“Demographic and social” Curriculum
It aims to train a professional statistician who can analyse several aspects of complex modern society. It combines the profile of an analyst of demographic dynamics, who can examine and interpret phenomena related to how populations change, with the profile of an analyst of social systems who can study, describe and monitor social phenomena with statistical methods.

Entry requirements

Entry requirements are defined in such a way to meet two types of needs: recognizing that graduates of L41-Statistics class possess the knowledge and skills necessary to continue their academic career in statistics, and as provided by par. 1.4.2 of the Ministry Guidelines (DM 26.07.2007), as well as encouraging an interdisciplinary education by allowing access to graduates of other classes.
Candidates need to possess the following three requirements to be admitted.

1) A first-level degree or university diploma, or other equivalent degree acquired abroad.

2) Possess at least 60 credits in the following academic disciplines and corresponding areas:
- Area 01 (Mathematics and Computer Science): MAT/*, INF/01
- Area 02 (Physics): FIS/01, FIS/02, FIS/07
- Area 06 (Medicine): MED/01
- Area 09 (Industrial and Information Engineering): ING-IND/35, ING-INF/05, ING-INF/06
- Area 11 (Historical, Philosophical, Pedagogical and Psychological Sciences): M-PSI/03
- Area 13 (Economical and Statistical Sciences): SECS-S/*, SECS-P/*
- Area 14 (Political and Social Sciences) SPS/07.

3. Knowledge of basics of Mathematics, Probabilities, and Statistics. In particular:
Mathematics - Basics of differential and integral calculus, functions of one or more variables, linear algebra and analytical geometry.
Probability - Properties of probability; aleatory variables; notions of convergence of sequences of aleatory variables.
Statistics - Elements of descriptive statistics. Inferential Statistics: sample distributions; methods for parameters estimate; tests.
Computer Science – Familiarity with at least one programming language.

For those students who already possess requisites 1 and 2, requisite 3 is assessed by a committee appointed by the relevant didactic structure. The committee automatically approves the admission of students who possess a degree in L-41 class (class of Degree Programmes in Statistics) or equivalent. Other students who possess Requisites 1 and 2 could be interviewed to assess their knowledge, as indicated in Requisite 3. On the basis of the student’s curriculum and the interview results, in relevant cases, the Committee identifies specific learning outcomes, which, according to the courses of the degree programme, are related to exams that have not already been passed and are considered vital for the student’s education.

Job opportunities
The Masters degree prepares students for a career as a “senior statistician”, that is to say, an expert in acquiring and managing information in every context, particularly Public Administration and Local Authorities, ASL, private companies, research institutes (CNR, Istat, Istituto Superiore di Sanità, etc.), national and international study centres, international organisations (UN, FAO, WHO, etc.).
Specific areas of application include statistical analysis, data mining, biostatistics, analysis of populations’ dynamics, marketing, official statistics, social research.
The Masters degree prepares students to have access to PhD programmes in core disciplines.

Description of the degree programme
Masters degree in Statistics and Decision Sciences is a two-year programme for a total of 120 credits, and includes three different curricula:
1) The "Biostatistics" Curriculum includes 51 credits from key courses (Statistics, Applied Statistics, Applied Mathematics), 30 credits from related or supplementary courses, the remaining credits are from elective courses and other learning activities (laboratories and any internship) and 21 credits for the final exam.
2) The "Data Analyst" Curriculum includes 51 credits from key courses (Statistics, Applied Statistics, Applied Mathematics), 30 credits from related or supplementary courses, the remaining credits are from elective courses and other learning activities (laboratories and any internship) and 21 credits for the final exam.
3) The “Social demographics” curriculum includes 54 credits from key courses (Statistics, Applied Statistics, Applied Mathematics), 27 credits from related or supplementary courses, the remaining credits are from elective courses and other learning activities (laboratories and any internship) and 21 credits for the final exam.

Characteristics of the final exam
The final exam consists of preparing and presenting a final thesis. This step represents the conclusion of the student's learning process and must demonstrate their abilities in facing, analysing and solving in an original manner real problems in their complexity while using statistical and decisional strategies and tools, at the national and international level.

Attendance Regulations
Attendance is not compulsory.
Regulations relating to transfer to years following the first one
admission to the second year is regulated by the Students’ Handbook. For students enrolled in previous academic systems, coming from other courses or possessing other degrees, the Educational Area Committee defines criteria for credit recognition and provides guidelines for the submission of an individual study plan which, as provided by the course planning, takes into account the students’ previous career.

General Info
Syllabus and learning materials: courses’ syllabi and learning materials are available on the students’ portal, in the degree programme table:
https://corsidilaurea.uniroma1.it/
All academic staff act as tutors to support students in relation to their disciplines and tutoring timetable is available on the degree programme website
https://www.dss.uniroma1.it/it

Quality Assessment
The degree programme, together with the Faculty of Information Engineering, Computer Science and Statistics, monitors students’ opinions on all courses, regardless of whether students attend lessons or not. The assessment system is integrated with a quality process for which the self-assessment group is responsible (the group is formed by academic staff, students and technical and administrative staff of the degree programme). The feedback and the group’s self-assessment are used to improve the quality of courses and other learning activities.

The Masters degree, starting from a common basis of specialized education in statistical methodology, includes three curricula aimed to train professional figures in biomedical, methodological, socio-demographic studies.
All curricula require the acquisition of 51/54 credits in core learning activities. Other credits are distributed as follows: 30/27 credits in related and supplementary learning activities, 9/12 credits in elective courses, 21 credits for the final exam, 6/9 credits for further learning activities (internships, placements and workshops).