Statistics for management

Educational objectives

Learning goals
Knowledge and comprehension of the basic concepts and techniques of linear algebra and of analytic geometry of the plane and the space and ability to apply them to the study and resolution of simple problems also in the context of other courses.

Knowledge and understanding.
Good theoretical and practical knowledge of matrices, linear systems and other fundamental notions of linear algebra and ability to understand these issues also in the context of other courses.

Applying knowledge and understanding.
Ability to use the acquired skills for solving simple problems on matrices, linear systems and other fundamental notions of linear algebra, also for their use required in other courses.

Making judgements.
Good ability to recognize, frame and set out the resolution of simple problems on matrices, linear systems and other fundamental notions of linear algebra, possibly selecting appropriately among the methods learned.

Communication skills.
Good presentation skills of basic concepts and techniques of linear algebra as well as solution methods to simple problems.

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

Educational objectives

Learning goals.
The primary objective is to provide students with basic concepts and procedures of descriptive statistics.
At the end of the course the student must be able to design a small census survey and to conduct descriptive analyses of the data through the use of a statistical software.

Knowledge and understanding.
Upon completion of the course, students know and understand the main procedures of descriptive statistics.
They are able to organize data in simple and contingency tables and to synthesize them through graphical representations.
They know and are able to calculate the most important statistical indicators that measure
(a) position, variability and form of simple distributions and
(b) important aspects of the joint distribution of two variables.
Furthermore, they have acquired the notion of statistical model and are able to implement a simple regression model.

Applying knowledge and understanding.
Upon completion of the course, students are able to apply the knowledge acquired, in order to interpret and critically evaluate the results of descriptive analysis.

Making judgements.
Through a large number of exercises on all the topics covered, students develop autonomous judgment skills that allow to identify the most appropriate methods to solve problems of descriptive statistics and to critically interpret the results of the elaborations provided by the software.

Communication skills Students, through the study and the performance of practical exercises, acquire the technical-scientific language of the discipline, that must be properly used both in the written and in the oral examinations.

Learning skills Students who pass the exam will have knowledge of the fundamental notions for the descriptive analysis of data.
They will also be able to implement simple codes to organize data in tables and synthesize them through graphics and/or calculation of important indicators.
Therefore, they have acquired the basics to learn what will be proposed in the subsequent statistical courses.

Educational objectives

** General objectives

The main goal of the course is to give basic knowledge of computer
science, to provide the tools for understanding what
a computational problem is and which kind of logical reasoning may
lead to its solution. In the meantime we want to work on
programming skills in Python programming language.

** Specific objectives

a) Knowledge and understanding

At the end of the course the students will know what an algorithm
is, what difference exists between data and its representation, how
to reason using abstraction techniques. They will be able to code
in Python to solve new problems, using as well the theoretical
knowledge of algorithm analysis.

b) Ability to apply knowledge and understanding

Learing about the theory of algorithms and a programming language
allows the students to solve new computational problems in
a flexible way.

c) Judgment autonomy

Algorithmic thinking (also called computational thinking), which is
trained by the students during this course, gives tools and
techniques for rigorous and non-ambiguous analysis of problems,
using only relevant information.

d) Communicative ability

Students learn the technical and scientific language of computer
science, which must be appropriately used in written and viva
exams. Moreover writing code documentation trains the students to
explain their work with clarity.

e) Learning ability

The course show the students new concepts (e.g. programming, theory
of algorithms). The effort required to understand and apply these
concepts allows them to study and evaluate new techniques and
programming languages.

Educational objectives

Knowledge and understanding.
Good theoretical and practical knowledge of differential calculus, integration, power series (real functions of one real variable).
Ability to understand these issues also in the context of other courses.

Applying knowledge and understanding.
Ability to use the acquired skills for solving simple problems and for their use required in other courses.

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

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

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

Educational objectives

Learning goals of the course is to provide students the main tools to measure economic aggregates and to compare economic data across time and space.
Students also acquire knowledge of the most important data sources at national and international level.

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

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

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

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

Learning skills.
Students passing the exam have acquired the ability to read and realise basic empirical economic studies.
They learn practical data-analysis skills.

Educational objectives

Learning goals.
The aim of the course is to present the basic principles and tools of modern economic theory - both micro and macroeconomics - showing at the same time their empirical relevance.
This is achieved by integrating the theoretical exposition with the description of actual features of the Italian economy and of other national economies.

Knowledge and understanding.
The lectures aim at allowing students to gain a general knowledge of the essential concepts used in economic analysis and a historical perspective of the development of the main theoretical approaches to the study of the economic system.

Applying knowledge and understanding.
Students will learn the main lines of development of microeconomic and macroeconomic theories, including monetary theory, and will have a general view of economic policy.
This will allow students to approach both historical economic development and present day economic facts.

Making judgements.
The course aims at fostering students' ability to apply economic theory and methodology to the analysis of economic facts and of economic policy.
These analytical abilities will be such to be also applied to current phenomena.

Communication skills.
Frontal teaching and preparation of oral examination allows students to acquire mastering of elementary techniques and of communication skills properly belonging to the fields of economic analysis.

Learning skills Students who pass the exam will have acquired analytical methodologies which ail allow them to tackle themes proper of other courses and to discuss current economic facts."

Educational objectives

Objectives
This course aims to give students a solid grounding in statistical terminology and to acquaint them with the typical linguistic features and characteristics of standard statistical presentations and publications.

Skills studied
Proceeding from the elementary skills of interpreting and describing tables and graphs, the course will focus on expository texts ,so as to enable the students gain facility in describing the statistical methods underlying reported data. It is hoped that by the end of the course, the students, with reference to the publications of the Istituto Nazionale di Statistica, will be able to make competent statistical presentations in English on the economic and social realities of Italy and to respond to questions and requests for clarification thereon.

Educational objectives

Knowledge and understanding.
Good theoretical and practical knowledge of differential calculus and integration for functions of several real variables. Ability to understand these issues also in the context of other courses.

Applying knowledge and understanding.
Ability to use the acquired skills for solving simple problems and for their use required in other courses.

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

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

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

Educational objectives

General objectives
The main objectives are: acquire the ability to identify the conceptual structure of a database and learn how to interact with a database management system (DBMS).

Specific objectives

a) Knowledge and ability to understand
After attending the course the students know and understand the relational data model, they are able to cover all the phases of the design of a database (in particular the conceptual design) and are able to write and understand SQL commands for defining and querying a relational DB.
They also know the basic DBMS administration tools.

b) Ability to apply knowledge and understanding
At the end of the course the students are able to derive the relational schema of a DB starting from the requirements of a database-based application.
They are able to translate the informal description of the structure of data into an Entity-Relationship diagram that correctly represents the data and define the integrity constraints on the data object of the design. They are able to realize a relational DB using the SQL language, they are able to perform complex queries using the SQL language.

c) Autonomy of judgment
The students are able to apply the formalism of the ER diagrams in order to obtain an accurate description of the structure of the data that make up a database.
They can distinguish the role of data definition language, data manipulation language and query language to interact with a DBMS. They manage to evaluate how different implementation choices can lead to more or less adequate solutions to represent the integrity constraints existing between the data.
Through the articulated laboratory activities they acquire the ability to evaluate the efficiency of commands that define queries of various complexity levels.

d) Communication skills
Students acquire the formal rigor necessary to read and produce a conceptual scheme. They can understand the meaning of a query expressed in a formal language.

e) Learning ability
Students who pass the exam can easily understand formalisms for the definition of DB in non-relational models, they are able to understand how to use query languages ​​embedded in general-purpose programming languages.

Educational objectives

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

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

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

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

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

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

Educational objectives

Learning goals.
The primary educational objective of the course is students' learning of the main problems and methods of statistical Inference and its different alternative theoretical approaches.
Students must also be able to solve the analytical problems necessary to apply the above methods and be able to interpret the results that derive from their application to real data.

Knowledge and understanding.
After attending the course the students know and understand the main inferential problems (point and interval parametric estimation and hypothesis testing of the most important univariate statistical models) and the main methods to be used to solve these problems (for example: maximum likelihood estimation, confidence intervals, parametric tests).

Applying knowledge and understanding.
At the end of the course the students are able to formalize real problems in terms of inferential problems and to apply the specific methods of the discipline to solve them.
They are also able to process the most important statistical models (with one or two unknown parameters) and to apply the methods to models not covered in the lessons.
Finally, they are able to apply the methods to the data and to interpret the results.

Making judgements.
Students develop critical skills through the application of inferential methodologies to a wide range of statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different inferential logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Communication skills.
Students acquire by means of theoretical study and by solving practical exercises, the technical-scientific language of the discipline, which must be properly used both in the intermediate and final written tests and in the oral axam.
Communication skills are also developed through group activities stimulated during labs and participation to a public discussion forum Learning skills.

Students who pass the exam have learned a method of analysis that allows them to tackle, in future more advanced courses, the study of the formal properties of inferential procedures in more complex modeling contexts.

Educational objectives

Learning goals.
The primary educational objective of the course is students' learning of the main problems and methods of statistical Inference and its different alternative theoretical approaches.
Students must also be able to solve the analytical problems necessary to apply the above methods and be able to interpret the results that derive from their application to real data.

Knowledge and understanding.
After attending the course the students know and understand the main inferential problems (point and interval parametric estimation and hypothesis testing of the most important univariate statistical models) and the main methods to be used to solve these problems (for example: maximum likelihood estimation, confidence intervals, parametric tests).

Applying knowledge and understanding.
At the end of the course the students are able to formalize real problems in terms of inferential problems and to apply the specific methods of the discipline to solve them.
They are also able to process the most important statistical models (with one or two unknown parameters) and to apply the methods to models not covered in the lessons.
Finally, they are able to apply the methods to the data and to interpret the results.

Making judgements.
Students develop critical skills through the application of inferential methodologies to a wide range of statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different inferential logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Communication skills.
Students acquire by means of theoretical study and by solving practical exercises, the technical-scientific language of the discipline, which must be properly used both in the intermediate and final written tests and in the oral axam.
Communication skills are also developed through group activities stimulated during labs and participation to a public discussion forum Learning skills.

Students who pass the exam have learned a method of analysis that allows them to tackle, in future more advanced courses, the study of the formal properties of inferential procedures in more complex modeling contexts.

Educational objectives

Learning goals.
The primary educational objective of the course is students' learning of the main problems and methods of statistical Inference and its different alternative theoretical approaches.
Students must also be able to solve the analytical problems necessary to apply the above methods and be able to interpret the results that derive from their application to real data.

Knowledge and understanding.
After attending the course the students know and understand the main inferential problems (point and interval parametric estimation and hypothesis testing of the most important univariate statistical models) and the main methods to be used to solve these problems (for example: maximum likelihood estimation, confidence intervals, parametric tests).

Applying knowledge and understanding.
At the end of the course the students are able to formalize real problems in terms of inferential problems and to apply the specific methods of the discipline to solve them.
They are also able to process the most important statistical models (with one or two unknown parameters) and to apply the methods to models not covered in the lessons.
Finally, they are able to apply the methods to the data and to interpret the results.

Making judgements.
Students develop critical skills through the application of inferential methodologies to a wide range of statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different inferential logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Communication skills.
Students acquire by means of theoretical study and by solving practical exercises, the technical-scientific language of the discipline, which must be properly used both in the intermediate and final written tests and in the oral axam.
Communication skills are also developed through group activities stimulated during labs and participation to a public discussion forum Learning skills.

Students who pass the exam have learned a method of analysis that allows them to tackle, in future more advanced courses, the study of the formal properties of inferential procedures in more complex modeling contexts.

Educational objectives

Learning goals
1.Formulate Linear Programming Issues.
2. Know the main problems of optimization on networks.
3. Knowing the basic algebraic and geometric aspects of Linear Programming.
4. Knowing the basic techniques for solutions of Linear Programming and Optimization of network problems.

Knowledge and understanding
Know and understand the main problems of optimization on networks and the main solution methods.

Applying knowledge and understanding
At the end of the course the students are able to recognize and formulate problems of linear optimization and optimization on networks and apply the appropriate solution algorithms.

Making judgements
Students develop critical skills through the application of operational research methodologies to a wide range of decision models Communication skills
Through the study and the carrying out of practical exercises they acquire the technical-scientific language of the discipline that must be opportunely used in written and oral tests.

Learning skills
By passing the exam, students are able to subsequently tackle the study of more complex decision models

Educational objectives

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

Educational objectives

Learning goals
Know how to reorganize multivariate data for their statistical analysis.
Acquire the tools for the analysis of multivariate statistical data.

Knowledge and understanding.
Knowledge of multivariate statistical methodologies and their formalization through matrix algebra.

Applying knowledge and understanding.
Understand which techniques are most appropriate 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. Being able to carry out a statistical survey - using the skills already acquired in IT, Descriptive Statistics, Inferential Statistics and Sampling - and be able to analyze the multivariate data with the most appropriate methods of Multivariate Statistics.

Making judgements.
Students develop critical skills through the application of multivariate statistical methodologies to a wide range of statistical models.
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 properly used both in 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 deal with work experience.

Educational objectives

Learning goals
Main goal is learning the main methods for the statistical analysis of phenomena variable in time, related both to the description of their main properties, and to forecasting of future behavior

Knowledge and understanding
Knowledge of the foundations of time series analysis (stationarity, autocorrelation, representative models) and understanding the main methods for estimating them using real data.

Applying knowledge and understanding.
Students will be able to formalize the analysis of the main properties of a time series through statistical indices, and to actually obtain estimates of such indices and some types of forecasts, basing on real data, using appropriate software.

Making judgements.
Students develop judgement abilities by applying alternative methodologies to the same data sets, and learn to interpret results in a critical way.

Communication skills.
Students learn the specific technical-scientific language of the present discipline, and also learn to communicate correctly through discussion of practical applications.

Learning skills.
Students develop the adequate skill to take into account, in an authonomous and statistically correct fashion, of the influence of time, in all data analyses that they will find in further studies and their professional experience.

Educational objectives

Learning goals
The primary goal of the present course is to allow students to learn the main elementary techniques and methodologies for sampling finite populations and estimate population parameters.
Students should be able to plan a sample survey and analyze collected data in order to provide point and interval estimates of the population parameters.

Knowledge and understanding.
Students are expected to have a good knowledge of the main elementary sampling designs (simple random sampling, stratified sampling, single-stage cluster sampling, two-stage sampling, systematic sampling) as well as a basic knowledge of variable-probability sampling designs.

Applying knowledge and understanding.
Students should be able to formalize real problems involving survey sampling and should use acquired knowledge to solve real problems.
Furthermore, they should be able to estimate parameters of interest even in the presence of auxiliary variables.

Making judgements.
Students should develop their skills by planning sample surveys.

Communication skills.
Students should learn the appropriate language of survey sampling.

Learning skills.
Students should be able to attack the problem of planning a sample survey by using elementary sampling designs.
This is the typical case of surveys on a small/medium scale.

Educational objectives

Students are required to write, present and discuss an original thesis, which illustrates a problem tackled during the practical training and all the activities carried out to develop its solution.