Study plan | Catalogo dei Corsi di studio

Percorso formativo

Data management and analysis, i.e. Statistics, is pervasive in any modern professional activity.

SMA- Statistical Methods and Applications - is the acronym of the brand-new two-year Master of Science (in Italian Laurea Magistrale ) delivered by the Department of Statistical Science (DSS). DSS is the largest Department of Statistics in Italy and its faculty members enjoy international reputation in teaching and research. DSS hosts one of the most powerful computing resources at Sapienza University of Rome.

The Master programme is entirely held in English. It provides students with specific statistical skills through a suitable mix of advanced data modelling methodologies and hand-on professional training to address complex scientific and socio-economic problems. Students are prepared to handle the overall data management process: collection, analysis, interpretation, decision making. Specific attention is devoted to methods for Big Data Analysis and their applications to relevant domains with a specific emphasis on economic phenomena.

Starting from a common base of Statistics, Probability and Computing, the Master programme aims at delivering a solid and highly marketable statistical and quantitative training in the interpretation of real-world phenomena and support of decision-making.

Students can choose one of the following study paths:

• [DA] Data Analyst (with optional path for Double Degree with Université Paris Dauphine) <DA.pdf>

• [OS] Official Statistics (EMOS - European Master in Official Statistics label) <OS.pdf>

• [QE] Quantitative Economics <QE.pdf>

All these three study paths (curricula) prepare professionals for careers in consulting companies, industry and State agencies as well as candidates for PhD programmes in Statistics, Quantitative Economics and Econometrics, Data Science.

Mandatory and elective courses are displayed in the overview tables of the study paths available as pdf files

<DA.pdf>

<OS.pdf>

<QE.pdf>

A more datailed list with credits, semester schedule and other constraints is available in the online tables here below. Note that students can alternatively submit their own individual study plan (Piano di Studi Individuale in Italian) to the Master programme board.

For further info not found in this CorsidiLaurea official portal please go to http://sma.dss.uniroma1.it or email out SMA Team staff at sma-dss@uniroma1.it.

Study Path tables (sourced from the official GOMP repository)

Data analyst (percorso valido anche ai fini del conseguimento del doppio titolo italo-francese)

First year

Orientamento unico
Course		Semester	CFU	SSD
1056015 - Stochastic Processes	Learning goals The course provides a broad introduction to stochastic processes. In particular the aim is - to give a rigorous introduction to the theory of stochastic processes, - to discuss the most important stochastic processes in some depth with examples and applications, - to give the flavour of more advanced work and applications, - to apply these ideas to answer basic questions in several applied situations including biology, finance and search engine algorithms. Knowledge and understanding At the end of the course the students will be familiar with the basic concepts of the theory of stochastic processes in discrete and continuous time and will be able to apply various techniques to study stochastic models that appear in applications. Applying knowledge and understanding At the end of the course the students will have the tools to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space. The students will have the tools to solve simple applied problems in new environments and broader contexts. Making judgements At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. The student will also acquire the necessary language skills to start reading academic books on the topic and research papers. Communication skills The students will acquire the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will also acquire the rationale behind the stochastic model studied (e.g. the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour...) that is necessary to communicate to specialist and non-specialist audiences. Learning skills The students will acquire the methodology and the language to study in a manner that may be largely autonomous and to apply the methodology to the subsequent studies in the area of statistics and finance.	First semester	9	MAT/06
1055949 - 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 semester	9	SECS-S/01
AAF1893 - CHOSEN BY THE STUDENT	This course can be chosen by the student within the Sapienza courses as long as consistent with the curriculum.	First semester	12
10589920 - Sample surveys	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
Curriculum Data Analyst Gruppo OPZIONALE C Altre attività per 12		Go to group
Curriculum Data Analyst Gruppo OPZIONALE B a scelta 24 cfu		Go to group

Second year

Orientamento unico
Course		Semester	CFU	SSD
10589458 - Advanced economic statistics	Learning goals The main goal is acquiring advanced modelling techniques for mutivariate economic data. Students are expected to understand the theoretical foundations of the methods studied and to apply them to real datasets. Knowledge and understanding The focus of the course will on the Vector Autoregressive (VAR) model in stationary and non stationary settings, using both asymptotic and simulation (bootstrap) inference Applying knowledge and understanding After the course students will be able to specify a VAR model, evaluate if is adequate to the dataset of interest , use for estimating causal relationship and formatulate forecasts Making judgements Learning how to judging the adequacy of the models and assessing the uncertainty of the estimated relationships and forecasts will be an essential part of the course Communication skills Learning to communicate the results of the estimation process both in oral and written form will be an essential part of the course Learning skills The models object of the course are essential parts of the most advanced and complex models used in quantitative economic analysis, which the students will then be able to tackle.	First semester	6	SECS-S/03
10589834 - Advances in data analysis and statistical modelling	Learning goals Knowing how to reorganize multidimensional data with a complex structure for their statistical analysis. Starting from the multivariate statistical methodologies, knowing how to realize complex strategies of analysis that have an easy interpretation. To be able to make decisions based on empirical evidence giving appropriate answers to corporate information requests. Knowing how to extract relevant information from large data (big data). Knowledge and understanding Knowledge of advanced multivariate statistical methodologies and advanced formal data analysis strategies with a model-based statistical approach. Applying knowledge and understanding Understanding the most appropriate complex techniques to be able to make decisions based on empirical evidence, respond to corporate information requests and be able to extract relevant information from the observed data. Making judgements Students develop critical skills through the application of complex multivariate statistical methodologies. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which must be used in the world of work. 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 face, the world of work having acquired advanced statistical analysis tools for complex and large data.	First semester	9	SECS-S/01
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.	First semester	9	SECS-S/02
AAF1019 - Final exam	Students are required to write an original thesis that represents the knowledge achieved during the course of her/his study.	Second semester	21
Curriculum Data Analyst Gruppo OPZIONALE B a scelta 24 cfu		Go to group
Curriculum Data Analyst Gruppo OPZIONALE C Altre attività per 12		Go to group

Quantitative economics

First year

Orientamento unico
Course		Semester	CFU	SSD
1056015 - Stochastic Processes	Learning goals The course provides a broad introduction to stochastic processes. In particular the aim is - to give a rigorous introduction to the theory of stochastic processes, - to discuss the most important stochastic processes in some depth with examples and applications, - to give the flavour of more advanced work and applications, - to apply these ideas to answer basic questions in several applied situations including biology, finance and search engine algorithms. Knowledge and understanding At the end of the course the students will be familiar with the basic concepts of the theory of stochastic processes in discrete and continuous time and will be able to apply various techniques to study stochastic models that appear in applications. Applying knowledge and understanding At the end of the course the students will have the tools to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space. The students will have the tools to solve simple applied problems in new environments and broader contexts. Making judgements At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. The student will also acquire the necessary language skills to start reading academic books on the topic and research papers. Communication skills The students will acquire the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will also acquire the rationale behind the stochastic model studied (e.g. the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour...) that is necessary to communicate to specialist and non-specialist audiences. Learning skills The students will acquire the methodology and the language to study in a manner that may be largely autonomous and to apply the methodology to the subsequent studies in the area of statistics and finance.	First semester	9	MAT/06
10589834 - Advances in data analysis and statistical modelling	Learning goals Knowing how to reorganize multidimensional data with a complex structure for their statistical analysis. Starting from the multivariate statistical methodologies, knowing how to realize complex strategies of analysis that have an easy interpretation. To be able to make decisions based on empirical evidence giving appropriate answers to corporate information requests. Knowing how to extract relevant information from large data (big data). Knowledge and understanding Knowledge of advanced multivariate statistical methodologies and advanced formal data analysis strategies with a model-based statistical approach. Applying knowledge and understanding Understanding the most appropriate complex techniques to be able to make decisions based on empirical evidence, respond to corporate information requests and be able to extract relevant information from the observed data. Making judgements Students develop critical skills through the application of complex multivariate statistical methodologies. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which must be used in the world of work. 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 face, the world of work having acquired advanced statistical analysis tools for complex and large data.	First semester	9	SECS-S/01
- A SCELTA DELLO STUDENTE		First semester	9
10589921 - Sample surveys	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 beable 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	6	SECS-S/01
10589568 - EFFICIENCY AND PRODUCTIVITY ANALYSIS	This course has the target of providing students with the modern techniques of measuring quantitatively advanced topics in economics. In particular, our focus will be on two main interrelated directions: the analysis of production and efficiency in the private and public sectors, impact analysis and economic dynamics of sectorial and micro founded systems in the modern economy. Knowledge and understanding Students are requested to know the techniques of estimation for output and input efficiency as well as the analysis methods for sectors (both static and dynamic) Applying knowledge and understanding At the end of the course, students should be capable of interpreting and addressing a study of efficiency pertaining decisional making units, both private and public. The statistical methods related are those of inference (for firms) and impact analysis and forecasting (for sectors). Making judgements At the end of the course, students will develop skills for interpreting and assessing productivity and efficiency problems. Communication skills Students will develop capacity of communicating results through the acquisition of both theoretical and applied skills. Learning skills After the exam, students will develop capacity of making autonomously further progress as far as the new advancements of this and related subjects are concerned.	Second semester	9	SECS-S/03
10589488 - Financial econometrics	Learning goals The aim of the course is to introduce students to the main methods of analysis and forecasting of the economic and financial time series. In particular, it covers i) Linear stochastic processes. Stationarity. Invertibility. Causality. ARMA processes. Identification, estimation, interpretation and forecasting. ii) Measurement and analysis of volatility. ARCH and GARCH models. Identification, estimation, interpretation and forecasting. Knowledge of the econometric theory for cross-section analysis, inference and probability theory is a prerequisite. Knowledge and understanding. After attending the course the students know and understand the main problems related to time series (for example: absence of stationarity) and the main methods to be used to solve such problems (for example: unit root tests). 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. They are also able to produce sound empirical analyzes and forecasts.	Second semester	9	SECS-P/05
Curriculum Quantitative economics Gruppo C attività a scelta per 6 cfu		Go to group
Curriculum Quantitative Economics Gruppo opzionale B2 3 esami per 18 CFU		Go to group
Curriculum Quantitative economics Gruppo OPZIONALE B1 due esami a scelta 18 cfu		Go to group

Second year

Orientamento unico
Course		Semester	CFU	SSD
10589458 - Advanced economic statistics	Learning goals The main goal is acquiring advanced modelling techniques for mutivariate economic data. Students are expected to understand the theoretical foundations of the methods studied and to apply them to real datasets. Knowledge and understanding The focus of the course will on the Vector Autoregressive (VAR) model in stationary and non stationary settings, using both asymptotic and simulation (bootstrap) inference Applying knowledge and understanding After the course students will be able to specify a VAR model, evaluate if is adequate to the dataset of interest , use for estimating causal relationship and formatulate forecasts Making judgements Learning how to judging the adequacy of the models and assessing the uncertainty of the estimated relationships and forecasts will be an essential part of the course Communication skills Learning to communicate the results of the estimation process both in oral and written form will be an essential part of the course Learning skills The models object of the course are essential parts of the most advanced and complex models used in quantitative economic analysis, which the students will then be able to tackle.	First semester	6	SECS-S/03
AAF1019 - Final exam	Students are required to write an original thesis that represents the knowledge achieved during the course of her/his study.	Second semester	21
Curriculum Quantitative economics Gruppo OPZIONALE B1 due esami a scelta 18 cfu		Go to group
Curriculum Quantitative economics Gruppo C attività a scelta per 6 cfu		Go to group
Curriculum Quantitative Economics Gruppo opzionale B2 3 esami per 18 CFU		Go to group

Official Statistics

First year

Orientamento unico
Course		Semester	CFU	SSD
1056015 - Stochastic Processes	Learning goals The course provides a broad introduction to stochastic processes. In particular the aim is - to give a rigorous introduction to the theory of stochastic processes, - to discuss the most important stochastic processes in some depth with examples and applications, - to give the flavour of more advanced work and applications, - to apply these ideas to answer basic questions in several applied situations including biology, finance and search engine algorithms. Knowledge and understanding At the end of the course the students will be familiar with the basic concepts of the theory of stochastic processes in discrete and continuous time and will be able to apply various techniques to study stochastic models that appear in applications. Applying knowledge and understanding At the end of the course the students will have the tools to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space. The students will have the tools to solve simple applied problems in new environments and broader contexts. Making judgements At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. The student will also acquire the necessary language skills to start reading academic books on the topic and research papers. Communication skills The students will acquire the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will also acquire the rationale behind the stochastic model studied (e.g. the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour...) that is necessary to communicate to specialist and non-specialist audiences. Learning skills The students will acquire the methodology and the language to study in a manner that may be largely autonomous and to apply the methodology to the subsequent studies in the area of statistics and finance.	First semester	9	MAT/06
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 semester	6
Bayesian modelling		First semester	3	SECS-S/01
Bayesian modelling		First semester	3	SECS-S/01
- A SCELTA DELLO STUDENTE		First semester	9
10589920 - Sample surveys	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
1055990 - Data Management in Official Statistics	Learning goals The general goal of this course are essentially to allow students to learn aspects of Official Statistics and related to data quality, editing, and data integration. A part of the course is also devoted to the use of specific software is also devoted to deal with missing data at item level. Knowledge and understanding Knowledge and understanding of the main techniques use in Official Statistics to improve the quality of raw data, as well as to improve dissemination via integration of data coming from different sources. This involves the analysis of several different problems. - Analysis of errors in data, and methodologies for detecting and correcting errors. - Models for missing values - Statistical analyses with missing data - Data integration: the problem of record linkage - Data integration: the problem of statistical matching Applying knowledge and understanding Students should be able to use the main techniques to deal with data editing, missing data, data integration Making judgements Students should develop their skills in analyzing data quality, as well as in using statostical methods in the presence of errors in data and data incompleteness. Communication skills Students should learn the appropriate language of Official Statistics, with special reference to the improvement data quality and data integration. Learning skills Students should be able to attack the main problems of data quality improvement and data integration.	Second semester	6	SECS-S/01
1055952 - DATA QUALITY AND OTHER TOPICS OF OFFICIAL STATISTICS	Learning goals Knowledge at an intermediate and advanced level of the main issues in official statistics with special attention to data quality Knowledge and understanding Knowledge and understanding of statistical methods within the topics of official statistics in an changing environment Applying knowledge and understanding Ability to apply statistical methods for official statistics problems with emphasis on the data quality process Making judgements Ability of choosing appropriate methods in different problems in official statistics with emphasis on the data quality process Communication skills Ability of communicating results of the analyses in official statistics with emphasis on the data quality process Learning skills Students acquire skills useful to approach more advanced topics in official statistics and data quality management	Second semester	6	SECS-S/01
AAF1179 - FOR STAGES AND INTERNSHIPS AT COMPANIES, PUBLIC OR PRIVATE BODIES, PROFESSIONAL ORDERS	Goals. With this activity (based on internships) students merge their academic knowledge with practical skills. They also develop independent judgement and communication skills.	Second semester	9
Curriculum Official Statistics Gruppo OPZIONALE B un esame 6 cfu		Go to group
Curriculum Official statistics Gruppo OPZIONALE C altre attività per 6 cfu		Go to group

Second year

Orientamento unico
Course		Semester	CFU	SSD
10589458 - Advanced economic statistics	Learning goals The main goal is acquiring advanced modelling techniques for mutivariate economic data. Students are expected to understand the theoretical foundations of the methods studied and to apply them to real datasets. Knowledge and understanding The focus of the course will on the Vector Autoregressive (VAR) model in stationary and non stationary settings, using both asymptotic and simulation (bootstrap) inference Applying knowledge and understanding After the course students will be able to specify a VAR model, evaluate if is adequate to the dataset of interest , use for estimating causal relationship and formatulate forecasts Making judgements Learning how to judging the adequacy of the models and assessing the uncertainty of the estimated relationships and forecasts will be an essential part of the course Communication skills Learning to communicate the results of the estimation process both in oral and written form will be an essential part of the course Learning skills The models object of the course are essential parts of the most advanced and complex models used in quantitative economic analysis, which the students will then be able to tackle.	First semester	6	SECS-S/03
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.	First semester	9	SECS-S/02
AAF1028 - Final exam	Students are required to write an original thesis that represents the knowledge achieved during the course of her/his study.	Second semester	30
Curriculum Official Statistics Gruppo OPZIONALE B un esame 6 cfu		Go to group
Curriculum Official statistics Gruppo OPZIONALE C altre attività per 6 cfu		Go to group
Curriculum Official statistics Gruppo OPZIONALE D un esame per 9 cfu		Go to group

Optional Groups

**Curriculum Data Analyst Gruppo OPZIONALE B a scelta 24 cfu : The student must acquire 24 CFU from the exams below**
Course		Year	Semester	CFU	SSD
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.	First year	Second semester	6	SECS-S/01
10589423 - Algorithms and data structures	General objectives The primary objective is to study algorithms and data structures that efficiently solve problems whose solution, using trivial approaches, would require very high resources. The algorithms studied will be implemented in Java language. Specific objectives Knowledge and ability to understand Basic data structures and their use in solving sorting, searching, and graph problems will be shown. It will be shown how the data structures described are made available in Java language. The student will be able to determine the computational complexity of algorithms, and associate them with the appropriate complexity class. Ability to apply knowledge and understanding At the end of the course the students will be able to determine algorithms to efficiently solve complex problems, in particular problems on graphs, and choose the most suitable data structures to obtain an efficient implementation of the algorithm. They will also be able to use the Java classes that implement the data structures studied. Autonomy of judgment Students will be able to distinguish the computational complexity of problems and algorithms, and to identify the computationally more costly steps in solving a problem. Communication skills Students will be able to describe, in appropriate terms, the characteristics of the main data structures, and identify the primitives needed to efficiently implement an algorithm. Learning ability Students who pass the exam will be able to take advanced courses, of an applicative nature, that require the use of sophisticated algorithms. They will also be able to appreciate software engineering and computational complexity theory teachings.	First year	Second semester	6	INF/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
1047222 - EFFICIENCY AND PRODUCTIVITY ANALYSIS	This course has the target of providing students with the modern techniques of measuring quantitatively advanced topics in economics. In particular, our focus will be on two main interrelated directions: the analysis of production and efficiency in the private and public sectors, impact analysis and economic dynamics of sectorial and micro founded systems in the modern economy. Knowledge and understanding Students are requested to know the techniques of estimation for output and input efficiency as well as the analysis methods for sectors (both static and dynamic) Applying knowledge and understanding At the end of the course, students should be capable of interpreting and addressing a study of efficiency pertaining decisional making units, both private and public. The statistical methods related are those of inference (for firms) and impact analysis and forecasting (for sectors). Making judgements At the end of the course, students will develop skills for interpreting and assessing productivity and efficiency problems. Communication skills Students will develop capacity of communicating results through the acquisition of both theoretical and applied skills. Learning skills After the exam, students will develop capacity of making autonomously further progress as far as the new advancements of this and related subjects are concerned.	Second year	Second semester	6	SECS-S/03
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
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. 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. 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. 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. 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 be also developed through group activities. Learning skills Students who pass the exam have learned computational techniques useful in the statistical analysis and to work self-sufficiently to face with the complexity of the statistical problems.	Second year	Second semester	6	SECS-S/01

**Curriculum Data Analyst Gruppo OPZIONALE C Altre attività per 12 : The student must acquire 12 CFU from the exams below**
Course		Year	Semester	CFU
AAF1149 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills. By facing practical and real problems, students develop judgement and communication skills.	First year	First semester	3
AAF1152 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills. By facing practical and real problems, students develop judgement and communication skills.	First year	First semester	6
AAF1578 - 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
AAF1544 - Laboratory of Stochastic Processes	Learning goals. General learning targets: The course is organised as a series of classes where the students will have the possibility to solve and discuss the solutions of a series of advanced exercises on the theory of stochastic processes. Knowledge and understanding. At the end of the course the students will acquire the ability to solve autonomously simple and more advanced exercises on the theory of stochastic processes. Applying knowledge and understanding. During the course the students will exercise the ability to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space and to solve simple applied problems in new environments and broader contexts. Making judgements At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. Communication skills. The students will exercise the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will acquire familiarity with the main ideas that are behind the stochastic model, e.g., the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour. Learning skills. The students will acquire autonomy in studying more advanced theoretical aspects of stochastic processes and in applying the main ideas of stochastic processes to the subsequent studies in the area of statistics and finance.	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
AAF1877 - Laboratory of financial and monetary statistics	Learning goals Students will be introduced to the following topics 1. The Monetary, banking and financial statistics. Why Bank of Italy collects statistics and what collects. Application: recent developments of the banks. 2. The financial accountsThe financial accounts structure.Household wealth after Piketty: an international comparison.The financial structure of the companies. 3. The balance of payments and international investment positionThe Italian balance of payments: the structure and recent developments.The procedure on excessive macroeconomic imbalances in Europe.Funds held abroad by the families. 4. The sample surveys of the Bank of ItalyThe survey on Household Income: recent results and a long-term look.The survey on inflation and growth expectations.	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
AAF1885 - Case studies and statistical consulting	Learning goals Prepare students to proposing solutions to real statistical problems in many research areas. Knowledge and understanding At the end of the course the students have the ability to understand and solve real practical statistical problems and to propose adequate solutions. Applying knowledge and understanding Students are required to apply theoretical and computational skills to real problems and case-studies in a wide range of domains. Making judgements One of the main goals of practical activities is to develop the ability to understand problems and to propose and compare alternative statistical approaches to solve them. Communication skills Students acquire the ability of discussing problems and of presenting oral and written reports of their practical analyses. Learning skills The students acquire a series of skills useful for future academic and professional activities.	Second year	First semester	3

**Curriculum Quantitative Economics Gruppo opzionale B2 3 esami per 18 CFU : The student must acquire 18 CFU from the exams below**
Course		Year	Semester	CFU	SSD
10589423 - Algorithms and data structures	General objectives The primary objective is to study algorithms and data structures that efficiently solve problems whose solution, using trivial approaches, would require very high resources. The algorithms studied will be implemented in Java language. Specific objectives Knowledge and ability to understand Basic data structures and their use in solving sorting, searching, and graph problems will be shown. It will be shown how the data structures described are made available in Java language. The student will be able to determine the computational complexity of algorithms, and associate them with the appropriate complexity class. Ability to apply knowledge and understanding At the end of the course the students will be able to determine algorithms to efficiently solve complex problems, in particular problems on graphs, and choose the most suitable data structures to obtain an efficient implementation of the algorithm. They will also be able to use the Java classes that implement the data structures studied. Autonomy of judgment Students will be able to distinguish the computational complexity of problems and algorithms, and to identify the computationally more costly steps in solving a problem. Communication skills Students will be able to describe, in appropriate terms, the characteristics of the main data structures, and identify the primitives needed to efficiently implement an algorithm. Learning ability Students who pass the exam will be able to take advanced courses, of an applicative nature, that require the use of sophisticated algorithms. They will also be able to appreciate software engineering and computational complexity theory teachings.	First year	Second semester	6	INF/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.	First year	Second semester	6	SECS-S/01
10589579 - Gender economics	The main objective of the course is to provide a gender analysis of economic theory answering to one main question: why and how a gender approach to inequality can explain men and women occupational patterns, wages and poverty? Knowledge and understanding After attending the course, the students will increase their ability in dealing, both theoretically and empirically, with gender diversity in the economic context. Applying knowledge and understanding At the end of the course, students will be able to formalize real economic problems and to apply the specific methods of the discipline to solve them. Students will acquire a theoretical preparation and, thanks to the analysis of numerous practical cases, the ability to critically study policies and economic models. Making judgements Students will increase not only their theoretical skills but also their critical curiosity in reading real recent economic phenomena and economic models in a gender perspective. Communication skills Students, through discussions in the classroom and exercises, will acquire tools for critical analysis of empirical evidence and communication skills. They will also learn how to structure and present a research report. Learning skills Students who pass the exam learned methods of analysis that will allow them to tackle other economic courses.	Second year	First semester	6	SECS-P/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
10589452 - Development finance	Learning goals Aim of the course is to explore the role of financial systems in the economic development process. Lectures will deal with topics related to the deepening, outreach, efficiency and stability of financial systems. The focus will be on applied and policy-oriented research, which can serve as basis for public policy discussions on the financial system issues, especially in developing and emerging markets. Knowledge and understanding Knowledge of the basic concepts and of the main theories elaborated in the field. Historical perspective and awareness of the existence of different interpretative positions. Applying knowledge and understanding At the end of the course students are able to formalize problems and to apply the specific methods of the discipline to solve them. They are also able to link methods to short-term data. Making judgements Students develop critical skills through the application of the same methodology to a wide range of economic models, which are affected by different theoretical approaches. Communication skills Students, through the study, acquire the technical-scientific language of the discipline, which must be appropriately used both in written and oral exams. Learning skills Students who pass the exam have learned a method of analysis that allows them to tackle the study of more complex models in economics.	Second year	Second semester	6	SECS-P/01

**Curriculum Quantitative economics Gruppo C attività a scelta per 6 cfu: The student must acquire 6 CFU from the exams below**
Course		Year	Semester	CFU
AAF1544 - Laboratory of Stochastic Processes	Learning goals. General learning targets: The course is organised as a series of classes where the students will have the possibility to solve and discuss the solutions of a series of advanced exercises on the theory of stochastic processes. Knowledge and understanding. At the end of the course the students will acquire the ability to solve autonomously simple and more advanced exercises on the theory of stochastic processes. Applying knowledge and understanding. During the course the students will exercise the ability to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space and to solve simple applied problems in new environments and broader contexts. Making judgements At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. Communication skills. The students will exercise the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will acquire familiarity with the main ideas that are behind the stochastic model, e.g., the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour. Learning skills. The students will acquire autonomy in studying more advanced theoretical aspects of stochastic processes and in applying the main ideas of stochastic processes to the subsequent studies in the area of statistics and finance.	First year	First semester	3
AAF1149 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills. By facing practical and real problems, students develop judgement and communication skills.	First year	First semester	3
AAF1877 - Laboratory of financial and monetary statistics	Learning goals Students will be introduced to the following topics 1. The Monetary, banking and financial statistics. Why Bank of Italy collects statistics and what collects. Application: recent developments of the banks. 2. The financial accountsThe financial accounts structure.Household wealth after Piketty: an international comparison.The financial structure of the companies. 3. The balance of payments and international investment positionThe Italian balance of payments: the structure and recent developments.The procedure on excessive macroeconomic imbalances in Europe.Funds held abroad by the families. 4. The sample surveys of the Bank of ItalyThe survey on Household Income: recent results and a long-term look.The survey on inflation and growth expectations.	First year	Second 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
AAF1152 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills. By facing practical and real problems, students develop judgement and communication skills.	Second year	First semester	6
AAF1885 - Case studies and statistical consulting	Learning goals Prepare students to proposing solutions to real statistical problems in many research areas. Knowledge and understanding At the end of the course the students have the ability to understand and solve real practical statistical problems and to propose adequate solutions. Applying knowledge and understanding Students are required to apply theoretical and computational skills to real problems and case-studies in a wide range of domains. Making judgements One of the main goals of practical activities is to develop the ability to understand problems and to propose and compare alternative statistical approaches to solve them. Communication skills Students acquire the ability of discussing problems and of presenting oral and written reports of their practical analyses. Learning skills The students acquire a series of skills useful for future academic and professional activities.	Second year	First semester	3
AAF1888 - Reading seminars	Learning goals. Aim of the course is to allow students to broaden their knowledge of economics, sociology, and other social sciences in an interdisciplinary way. Knowledge and understanding. Historical perspective and awareness of the existence of different interpretative positions in the context of social sciences. Applying knowledge and understanding. At the end of the course students will be able to deal with different models in a critical way. Making judgements. Students will develop critical skills through different theoretical approaches. Communication skills. Students, through the study, acquire the language of different disciplines, which must be appropriately used both in written and oral exams. Learning skills. Students who pass the exam have learned a method of analysis that allows them to tackle the study of more complex models.	Second year	Second semester	3

**Curriculum Quantitative economics Gruppo OPZIONALE B1 due esami a scelta 18 cfu: The student must acquire 18 CFU from the exams below**
Course		Year	Semester	CFU	SSD
10589482 - International monetary economics	Learning goals Working knowledge of the main models of international economics and international finance. Knowledge and understanding. Upon successful completion of the course, students will be able to analyse actual economic problems in terms of competing theories and models. Applying knowledge and understanding. Upon successful completion of the course, students will be able to understand the explicit and implicit hypotheses informing the main economic policy proposals in the current debate. Making judgements. The course is explicitly based on the principle of methodological and theoretical pluralism. Students will be introduced to at least two competing models for each economic problem considered, and will understand the criteria with which to personally choose their favorite interpretation. Communication skills. Through study and hands-on sessions, students will become proficient in the jargon and technical language of the discipline, which they must use in both written and oral examinations. Learning skills. Students that successfully complete the course will have learnt a method of analysis that will allow them to tackle and understand the main economic issues of today, both in subsequent economic courses and in the fruition and participation to the public debate.	First year	Second semester	9	SECS-P/01
1055949 - 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	Second year	First semester	9	SECS-S/01
10589582 - ECONOMIC HISTORY	Learning goals Main learning goal of this course is provide students with ability of connecting historical perspectives, elementary notions of economic theory and statistical analysis Knowledge and understanding Students will learn the main lines of economic development in Europe and worldwide between XV and XX centuries Applying knowledge and understanding This course will allow students to understand the main lines of economic development in Europe and worldwide between XV and XX centuries and to connect them to cultural, social, political and economic dimensions both in historical perspective and in its application to present time. Making judgements The course aims at fostering students' ability to apply historical methodologies and to connect cultural, social, political and economic dimensions both in historical perspective and in their application to present time. Communication skills Frontal teaching, preparation of written essays and preparation of oral examination allows students to acquire communication skills properly belonging to the fields of economic history and to combine them with those of economics and statistics. Learning skills Students who pass the exam will have acquired analytical methodologies which ail allow them to tackle themes proper of other courses and to discuss current economic facts connecting cultural, social, political and economic dimensions both in historical perspective.	Second year	Second semester	9	SECS-P/01
10589565 - applied economics	Learning goals The primary learning goal of this course is that of exposing students to the body of econometric techniques that are customised to economics applications. The aim of the course is to review this body of techniques, to demonstrate their use in hands-on style, drawing on as wide a range of example as possible, and to interpret each set of results in ways that are most useful to read and represent economic phenomena. Knowledge and understanding. The course is supposed to broaden students' knowledge of the various econometric techniques that appear in the economics literature, their properties and the way these are applied to data in order to verify economic theory. Applying knowledge and understanding. Upon successful completion of the course, students will be able to carry out a wide range of tasks in empirical economics, such as recognising the most suitable approaches to analyse the data at hand in order to capture and model its regularities, and intelligibly convey its messages to both economists and broader audiences. Making judgements. The course develops in a way to spurs students on researching empirical evidence of competing economic theories by respecting the nature of convenient data. Communication skills. Through study and hands-on sessions, students will acquire the terminology characterising the discipline, which they are required to use in both written and oral dissemination. Learning skills. Students who complete the course successfully will be acquainted with a method of analysis enabling them to endeavour the main economic issues from an empirical point of view.	Second year	Second semester	9	SECS-P/01
1038218 - 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. 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. 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. 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. 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 be also developed through group activities. Learning skills Students who pass the exam have learned computational techniques useful in the statistical analysis and to work self-sufficiently to face with the complexity of the statistical problems.	Second year	Second semester	9	SECS-S/01

**Curriculum Official Statistics Gruppo OPZIONALE B un esame 6 cfu: The student must acquire 6 CFU from the exams below**
Course		Year	Semester	CFU	SSD
1056085 - Big Data for Official Statistics	General learning goals - Define what subset of Big Data can be used in Official Statistics and what domains of Official Statistics can be enriched through the availability of new data sources - Establish how new data sources can be used in Official Statistics, by taking into account challenges, needs and risks in this exercise - Definition of the role of Big Data in the context of Official Statistics - Establish how to frame the measurement of social, demographic and economic phenomena through Big Data by considering challenges, needs and risks Knowledge and understanding Knowledge and understanding of statistical methods to handle Big Data in official statistics Applying knowledge and understanding Ability to apply statistical methods for official statistics problems with emphasis on Big Data Making judgements Ability of choosing appropriate methods in different problems in official statistics with emphasis on Big Data Communication skills Ability of communicating results of the analyses in official statistics with emphasis on Big Data Learning skills Students acquire skills useful to approach more advanced topics in official statistics and Big Data management	First year	First semester	6	SECS-S/05
10589423 - Algorithms and data structures	General objectives The primary objective is to study algorithms and data structures that efficiently solve problems whose solution, using trivial approaches, would require very high resources. The algorithms studied will be implemented in Java language. Specific objectives Knowledge and ability to understand Basic data structures and their use in solving sorting, searching, and graph problems will be shown. It will be shown how the data structures described are made available in Java language. The student will be able to determine the computational complexity of algorithms, and associate them with the appropriate complexity class. Ability to apply knowledge and understanding At the end of the course the students will be able to determine algorithms to efficiently solve complex problems, in particular problems on graphs, and choose the most suitable data structures to obtain an efficient implementation of the algorithm. They will also be able to use the Java classes that implement the data structures studied. Autonomy of judgment Students will be able to distinguish the computational complexity of problems and algorithms, and to identify the computationally more costly steps in solving a problem. Communication skills Students will be able to describe, in appropriate terms, the characteristics of the main data structures, and identify the primitives needed to efficiently implement an algorithm. Learning ability Students who pass the exam will be able to take advanced courses, of an applicative nature, that require the use of sophisticated algorithms. They will also be able to appreciate software engineering and computational complexity theory teachings.	First year	Second semester	6	INF/01
10589580 - International demography	General Aim The primary educational goal of the course is students' learning of the main concepts and basic methods of Demography. Knowledge and understanding After attending the course, the students know and understand the main international demographic sources and the measures to describe the population processes. Applying knowledge and understanding At the end of the course, the students are able to apply the learned methods to the real data, and to understand the results of these applications. Making judgements Students develop critical skills through the application of different indicators and measures to a wide range of case studies from different countries, and learn to critically interpret the results. Communication skills Students, through the study and the carrying out of practical exercises, acquire the technical-scientific language of the discipline, which must be opportunely used in the final oral examination. Communication skills are also developed through group activities. Learning skills Students who pass the exam have learned the skills necessary to address the study of more complex methods and models in subsequent teachings of demographic area.	Second year	First semester	6	SECS-S/04
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
1047222 - EFFICIENCY AND PRODUCTIVITY ANALYSIS	This course has the target of providing students with the modern techniques of measuring quantitatively advanced topics in economics. In particular, our focus will be on two main interrelated directions: the analysis of production and efficiency in the private and public sectors, impact analysis and economic dynamics of sectorial and micro founded systems in the modern economy. Knowledge and understanding Students are requested to know the techniques of estimation for output and input efficiency as well as the analysis methods for sectors (both static and dynamic) Applying knowledge and understanding At the end of the course, students should be capable of interpreting and addressing a study of efficiency pertaining decisional making units, both private and public. The statistical methods related are those of inference (for firms) and impact analysis and forecasting (for sectors). Making judgements At the end of the course, students will develop skills for interpreting and assessing productivity and efficiency problems. Communication skills Students will develop capacity of communicating results through the acquisition of both theoretical and applied skills. Learning skills After the exam, students will develop capacity of making autonomously further progress as far as the new advancements of this and related subjects are concerned.	Second year	Second semester	6	SECS-S/03
10589562 - Survey methodology	General goals Learning problems and principles of survey methodology Knowledge and understanding Knowledge and understanding of principles and of operational phases of statistical surveys (with specific emphasis in official statistics) Applying knowledge and understanding Ability to plan a statistical survey Making judgements Ability of understanding existing statistical surveys and of proposing improvements Communication skills Ability of using appropriate scientific and technical language of survey methodology; students are also trained in team-work Learning skills Students acquire skills useful to approach more advanced topics in official statistics	Second year	Second semester	6	SECS-S/05
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. 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. 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. 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. 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 be also developed through group activities. Learning skills Students who pass the exam have learned computational techniques useful in the statistical analysis and to work self-sufficiently to face with the complexity of the statistical problems.	Second year	Second semester	6	SECS-S/01

**Curriculum Official statistics 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 goal of these activities is to enable the students to merge their academic knowledge with professional skills. By facing practical and real problems, students develop judgement and communication skills.	First year	First semester	3
AAF1578 - 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
AAF1544 - Laboratory of Stochastic Processes	Learning goals. General learning targets: The course is organised as a series of classes where the students will have the possibility to solve and discuss the solutions of a series of advanced exercises on the theory of stochastic processes. Knowledge and understanding. At the end of the course the students will acquire the ability to solve autonomously simple and more advanced exercises on the theory of stochastic processes. Applying knowledge and understanding. During the course the students will exercise the ability to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space and to solve simple applied problems in new environments and broader contexts. Making judgements At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. Communication skills. The students will exercise the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will acquire familiarity with the main ideas that are behind the stochastic model, e.g., the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour. Learning skills. The students will acquire autonomy in studying more advanced theoretical aspects of stochastic processes and in applying the main ideas of stochastic processes to the subsequent studies in the area of statistics and finance.	First year	First semester	3
AAF1152 - OTHER USEFUL SKILLS FOR INCLUSION IN THE WORLD OF WORK	The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills. By facing practical and real problems, students develop judgement and communication skills.	First year	First semester	6
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
AAF1877 - Laboratory of financial and monetary statistics	Learning goals Students will be introduced to the following topics 1. The Monetary, banking and financial statistics. Why Bank of Italy collects statistics and what collects. Application: recent developments of the banks. 2. The financial accountsThe financial accounts structure.Household wealth after Piketty: an international comparison.The financial structure of the companies. 3. The balance of payments and international investment positionThe Italian balance of payments: the structure and recent developments.The procedure on excessive macroeconomic imbalances in Europe.Funds held abroad by the families. 4. The sample surveys of the Bank of ItalyThe survey on Household Income: recent results and a long-term look.The survey on inflation and growth expectations.	First year	Second semester	3
AAF1885 - Case studies and statistical consulting	Learning goals Prepare students to proposing solutions to real statistical problems in many research areas. Knowledge and understanding At the end of the course the students have the ability to understand and solve real practical statistical problems and to propose adequate solutions. Applying knowledge and understanding Students are required to apply theoretical and computational skills to real problems and case-studies in a wide range of domains. Making judgements One of the main goals of practical activities is to develop the ability to understand problems and to propose and compare alternative statistical approaches to solve them. Communication skills Students acquire the ability of discussing problems and of presenting oral and written reports of their practical analyses. Learning skills The students acquire a series of skills useful for future academic and professional activities.	Second year	First semester	3

**Curriculum Official statistics Gruppo OPZIONALE D un esame per 9 cfu: The student must acquire 9 CFU from the exams below**
Course		Year	Semester	CFU	SSD	Language
10589834 - Advances in data analysis and statistical modelling	Learning goals Knowing how to reorganize multidimensional data with a complex structure for their statistical analysis. Starting from the multivariate statistical methodologies, knowing how to realize complex strategies of analysis that have an easy interpretation. To be able to make decisions based on empirical evidence giving appropriate answers to corporate information requests. Knowing how to extract relevant information from large data (big data). Knowledge and understanding Knowledge of advanced multivariate statistical methodologies and advanced formal data analysis strategies with a model-based statistical approach. Applying knowledge and understanding Understanding the most appropriate complex techniques to be able to make decisions based on empirical evidence, respond to corporate information requests and be able to extract relevant information from the observed data. Making judgements Students develop critical skills through the application of complex multivariate statistical methodologies. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which must be used in the world of work. 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 face, the world of work having acquired advanced statistical analysis tools for complex and large data.	Second year	First semester	9	SECS-S/01
10589488 - Financial econometrics	Learning goals The aim of the course is to introduce students to the main methods of analysis and forecasting of the economic and financial time series. In particular, it covers i) Linear stochastic processes. Stationarity. Invertibility. Causality. ARMA processes. Identification, estimation, interpretation and forecasting. ii) Measurement and analysis of volatility. ARCH and GARCH models. Identification, estimation, interpretation and forecasting. Knowledge of the econometric theory for cross-section analysis, inference and probability theory is a prerequisite. Knowledge and understanding. After attending the course the students know and understand the main problems related to time series (for example: absence of stationarity) and the main methods to be used to solve such problems (for example: unit root tests). 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. They are also able to produce sound empirical analyzes and forecasts.	Second year	Second semester	9	SECS-P/05

Regulations of the Masters Degree in Statistical Methods and Applications

LM-82 Class, Statistical Sciences

Academic year 2018/2019

Active years I

Specific learning outcomes

The degree programme, entirely taught in English and with an international approach, aims to train professional figures who can manage the entire process of data collection, modelling, analysis and interpretation to examine complex phenomena and for decision support in institutions, companies and public and private research institutes. The aim is to equip statisticians with skills adequate for specific professional profiles. These figures include data analyst, statistical officer and quantitative economist. Besides a sound education offered by the different curricula, graduates acquire:

- The ability to work autonomously or in a team to solve application problems

- The ability to communicate professionally in spoken and written English

- A sound knowledge allowing a constant update of professional skills

- A sound knowledge allowing access to national and international PhD programmes in the core disciplines.

A sound basic knowledge of Mathematics, Probability, Statistics, Financial and Actuarial Mathematics are necessary requirements to have access to the Masters Degree.

Required knowledge and credits

Candidates need to meet the following four requirements to be admitted to the Masters Degree:

1. A bachelors degree or university diploma, or other degree acquired abroad and recognized as being equivalent;

2. At least 60 credits in all the academic sectors indicated in the following areas:

- Area 01 (Mathematical and Information Sciences): INF/01, MAT/*;

- Area 02 (Physical Sciences): FIS/01, FIS/02, FIS/07;

- Area 09 (Industrial and Information Engineering): ING-IND/35, ING-INF/05;

- Area 11 (Historical, Philosophical, Pedagogical and Psychological Sciences): M-PSI/03;

- Area 13 (Economical and Statistical Sciences): SECS-P/*, SECS-S/*;

3. An adequate knowledge of Mathematics, Probability, Statistics and Computer Science.

(a) Mathematics: Differential and integral calculus for functions with one or more real variables; foundations of linear algebra and analytical geometry in space and time. (b) Probability: aleatory variables, distributions and expected values; main parameter families of aleatory variables’ distributions; convergence for sequences of aleatory variables. (c) Statistics: foundations of descriptive statistics, simple and multiple distributions and their main synthetic indicators (mode, median and average, indicators of di heterogeneity and variability, indicators of dependency and correlation), foundations of inferential statistics, methods of point estimate and through sets, tests, and models of linear regression. (d) Computer science: basic knowledge.

4. B2 level or higher knowledge of English. Assessment of such requisites is compulsory to be admitted. Requirement 4 is assessed through an appropriate certificate or through a degree in English equivalent to or higher than a bachelors degree. For those students who meet Requirements 1 and 2, Requirement 3 is assessed by a committee nominated by the relevant didactic structure. The committee approves admission of students who possess Requirement 4 and a degree of the LM-41 Class (Class of Degree Programmes in Statistics) or equivalent. Other students who possess Requirements 1 and 2 might be interviewed to assess their knowledge, as indicated by Requirement 3 and/or Requirement 4, if an appropriate certificate is not submitted. Depending on the assessment of students’ curricula and on the interview outcome, when this is the case, the committee identifies study plans, which, according to the learning activities of the current degree programme, include courses essential for the students’ career that have not been attended yet.

These figures include data analyst, statistical officer and quantitative economist. Besides a sound education offered by the different curricula, graduates acquire:

- The ability to work autonomously or in a team to solve application problems

- The ability to communicate professionally in spoken and written English

- A sound knowledge allowing a constant update of professional skills

- A sound knowledge allowing access to national and international PhD programmes in the core disciplines.

Description of the degree programme

The degree programme offers a homogenous education in Statistics and Probability. In particular, the degree programme aims to provide a sound knowledge in statistical methods through advanced courses in statistical theory, applied statistics and stochastic processes. Starting from this common basis, students can choose among three curricula allowing them to acquire skills adequate for well-defined professional figures. These curricula are:  Data Analyst: a professional figure who possess traditional statistician’s skills as well as the ability to manage Big Data, thanks to specific Computer Science courses (Management of big data, and Big Data Analytics). Data analysts manage and analyse data collected according to proper planning and transforms them into information through models and techniques of statistical analysis and visualization, to provide support to decision processes in various contexts. Students of this curriculum can earn a double Italian-French degree thanks to an agreement with the University of Paris-Dauphine.  Official Statistics: this curriculum aims to train a professional figure, who works in the statistical departments of big government organizations and NGOs, national and international, related to the production and use of official statistics. Such figure provides methodological support for all phases of production and analysis of official statistics. This curriculum is part of the EMOS (European Master in Official Statistics) network sponsored by Eurostat: please go to http://ec.europa.eu/eurostat/web/european-statistical-system/emos.  Quantitative Economics: this curriculum aims to train a professional figure who contributes to identify business and public strategies and assessment of outcomes. Such figure can act as supervisor, coordinator and consultant to solve problems related to management of data, economic and financial information. Besides common courses and those specific for each curriculum, students are also required to attend an elective course and laboratory activities. A traineeship period is also part of the Official Statistics curriculum. Furthermore, students earn a significant number of credits through the final exam.

Characteristics of the final exam. Students can choose between two alternatives for the final exam: i) preparing and discussing a final thesis; ii) a research project or traineeship activity in a company, public or private research institute or other international academic institutions. The final thesis focuses on a topic relating to the degree programme’s courses and agreed with a supervisor. The thesis is a written and original work, through which students demonstrate that they can adequately apply concepts and tools acquired in their academic career. Students should also demonstrate their ability to refer to the relevant literature. Research projects focus on methodological issues relating to the degree programme’s disciplines and can address specific needs of companies and research institutes. The work should (a) be original; (b) show that students can adequately apply statistical and decision methods and/or their application in a specific field at the level required in the context of application.

Professional opportunities for “Data Analyst” graduates

“Data Analyst” graduates can work in large public and private companies managing complex and big data, such as multinationals operating in ICT (Information and Communication Technology), energy, search engines, market research, consulting firms and research institutes, as well as public administration. The degree programme prepares students to have access to PhD programmes in core disciplines.

Official Statistics –Official Statistics graduates can work in the Public Administration, at official statistics’ institutions, local bodies of the National Statistics System (SISTAN), national and international government organizations, other European and supranational organizations (FAO, World Bank, United Nations, etc.). The degree programme prepares students to have access to PhD programmes in core disciplines.

Quantitative Economics – The Quantitative Economics graduates can work in large companies, banks, funds and financial institutions, consulting firms, public administration, central banks, watchdogs, European and supranational organizations. The degree programme prepares students to have access to PhD programmes in core disciplines.

Attendance Regulations

Attendance is not compulsory.

Regulations relating to transfer to years following the first one

Admissions to the second year are determined by Regulations for Sapienza Students.

Students enrolled in previous academic systems, coming from other courses or possessing other degrees.

The Area Educational Board defines criteria for credit recognition and provides guidelines for the submission of an individual study plan, which, according to the academic system, takes into account students’ previous career.

General Info

Syllabi and learning materials: courses’ syllabi and learning materials are available on the students’ portal, in the degree programme table.

All academic staff act as tutors to support students in relation to their disciplines and tutoring timetable is available on the Department of Statistical Sciences’ website.

http://www.dss.uniroma1.it/dipartimento/persone/docentititle=

Quality Assessment

The degree programme is offered by the Department of Statistical Sciences, which, in collaboration with the Faculty of Information Engineering, Computer Science and Statistics, monitors the opinions of all attending students. The assessment system is integrated with a quality process managed by the self-assessment group (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 degree programme, starting from a sound basis in mathematics, probability and statistics, aims to train professional figures who can manage, through an integrated approach, the entire process of collection, modelling and analysis of statistical data for explanatory and decision purposes in relation to complex phenomena in a variety of contexts. The degree programme trains Data Analysts, who are experts in streamlining and operational research methods, experts of official statistics and in quantitative methods for economical analyses.

Percorso formativo

Overview

Study Path tables (sourced from the official GOMP repository)

Data analyst (percorso valido anche ai fini del conseguimento del doppio titolo italo-francese)

First year

Second year

Quantitative economics

First year

Second year

Official Statistics

First year

Second year

Optional Groups