Data Science

CINZIA DARAIO

General Objectives of the course

The general objectives of the course are:
- Present a general framework for the development of quantitative models for economic analysis and management;
- Provide the basic concepts and a guide to analyse the specialised literature;
- Propose a unified framework on the main methodologies available to compare the productivity and efficiency of Decision Making Units (DMUs);
- Introduce to the relevant roles played by the data for the development of effective quantitative models of socio-economic systems;
- Make an introduction to the main softwares available to implement the quantitative models presented during the course;
- Provide laboratory sessions to implement the quantitative models presented during the course in practice;
- Present several applications in the field of economics and management, including public sector services as potential group project works, to be developed by the students according to their personal interest and background;
- Interact with students through seminars, assisted laboratory, oral presentations and the realization of a project work on real data.

Specific objectives of the course
• KNOWLEDGE AND UNDERSTANDING: DEMONSTRATE THE KNOWLEDGE OF THE BASIC METHODS FOR THE DEVELOPMENT OF QUANTITATIVE MODELS FOR ECONOMIC ANALYSIS AND MANAGEMENT ;
• ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING: TO BE ABLE TO DEVELOP QUANTITATIVE ECONOMIC MODELS ON THE BASE OF THE KNOWLEDGE AND TECHNIQUES LEARNED DURING THE COURSE;
• JUDGMENT AUTONOMY: TO BE ABLE TO DEVELOP A QUANTITATIVE ECONOMIC MODEL WITH CRITICAL SPIRIT, CHOOSING THE APPROPRIATE METHOD AND CORRECTLY IMPLEMENTING IT.
• COMMUNICATION SKILLS: BEING ABLE TO COMMUNICATE THE RESULTS OF THE ANALYSIS AND ITS INFORMATION TO DIFFERENT TYPES OF INTERLOCUTORS;
• LEARNING SKILLS: TO DEVELOP THE NECESSARY SKILLS TO APPLY AND DEVELOP AUTONOMOUSLY THE METHODS AND MODELS LEARNED DURING THE COURSE.

SHORT DESCRIPTION

The course, based on an interdisciplinary approach, combines classical lectures, with seminars of invited experts, with practical sessions and tutorials to introduce to the main quantitative techniques (including cost benefit analysis, productivity and efficiency analysis, sensitivity analysis and sensitivity auditing, tools from the physics of complex systems and regression methods for continuous and discrete responses) available for the development of the economic analysis and management of organizations and socio-economic systems.

GENERAL OBJECTIVES OF THE COURSE

- Present a general framework for the development of quantitative models for economic analysis and management;
- Provide the basic concepts and a guide to analyse the specialised literature;
- Propose a unified framework on the main methodologies available to compare the productivity and efficiency of Decision Making Units (DMUs);
- Introduce to the relevant roles played by the data for the development of effective quantitative models of socio-economic systems;
- Make an introduction to the main softwares available to implement the quantitative models presented during the course;
- Provide laboratory sessions to implement the quantitative models presented during the course in practice;
- Present several applications in the field of economics and management, including public sector services as potential group project works, to be developed by the students according to their personal interest and background;
- Interact with students through seminars, assisted laboratory, oral presentations and the realization of a project work on real data.

SPECIFIC OBJECTIVES OF THE COURSE

• Knowledge and understanding: demonstrate the knowledge of the basic methods for the development of quantitative models for economic analysis and management;
• Ability to apply knowledge and understanding: to be able to develop quantitative economic models on the base of the knowledge and techniques learned during the course;
• Judgment autonomy: to be able to develop a quantitative economic model with critical spirit, choosing the appropriate method and correctly implementing it.
• Communication skills: being able to communicate the results of the analysis and its information to different types of interlocutors;
• Learning skills: to develop the necessary skills to apply and develop autonomously the methods and models learned during the course.

No prerequisites

Programme

The course is composed by the following main sections, organized in modules.

-Section I. INTRODUCTION
1) A three-dimensional framework for a quantitative approach to the economic analysis and management.
-Section II. DATA
2) Nature, collection, semantic modelling, and analysis of big data and little data within organizations and socio-economic systems.
3) Classification systems and information taxonomies for the management of organizations and socio-economic systems.

-Section III. TOOLS Quantitative tools for economic modelling and management
4) Cost Benefit Analysis
5) Regression Methods for Continuous and Discrete Responses from Parametric to Nonparametric Approaches
6) Productivity and Efficiency Analysis: main techniques in an unified approach from parametric to nonparametric models.
7) Statistical Tools from the Physics of Complex Systems.
8) Sensitivity Analysis and Sensitivity Auditing techniques.

-Section IV. APPLICATIONS
9) Available data sources for empirical analysis in economics and management
10) Applications in economics and management, including public sector services. Part one. Outline of the existing literature.
11) Applications in economics and management, including public sector services. Part two. Developments during the Project work activities (see below).

Group project works
The group project works will be defined according to the interest of students.
The following broad projects areas will be available:
1) Estimation of socio-economic models using ISTAT (http://www.istat.it/it/prodotti/banche-dati) and EUROSTAT (http://ec.europa.eu/eurostat) data;
2) Empirical analysis of education, science and technology systems, with data coming from ongoing European research projects, including the ETER project (http://eter.joanneum.at/imdas-eter/ );
3) Application of statistical tools from the physics of complex systems to compare the scientific performance of countries, and case studies in collaboration with the Italian Institute of Technology (https://www.iit.it/it/home.html, http://lns.iit.it/ );
4) Applications of sensitivity analysis and sensitivity auditing, in collaboration with the Joint Research Center of the European Commission, Ispra (https://ec.europa.eu/jrc/ ).

Detailed guidelines on how to make a presentation, how to make a bibliographic search, and on how to carry out the project work and on the choice of the technique to carry out the analysis will be provided during the practical laboratory sessions.

Materials

A base of the course is the material contained in: Daraio C. (2016) Eds., Challenges of Big Data for Economic Modeling and Management: Tools from Efficiency Analysis, Sensitivity Analysis, Sensitivity Auditing and Physics of Complex Systems. Proceedings of the Workshop of the 10-11 November 2015, DIAG Sapienza University of Rome, Edizioni Efesto, Rome.
During the course the Lecture Notes and additional materials will be distributed.

Materials

A base of the course is the material contained in: Daraio C. (2016) Eds., Challenges of Big Data for Economic Modeling and Management: Tools from Efficiency Analysis, Sensitivity Analysis, Sensitivity Auditing and Physics of Complex Systems. Proceedings of the Workshop of the 10-11 November 2015, DIAG Sapienza University of Rome, Edizioni Efesto, Rome.
During the course the Lecture Notes and additional materials will be distributed.

Teaching includes lectures, practical exercises, assisted laboratory and expert seminars.

Classes will be held mainly in-person. Lectures, tutorials and face-to-face laboratory will be supplemented by online seminars conducted by experts.

Evaluation

All the course, and in particular the group project work activities, will require the active participation of students that will be asked to make small homework assignments, an in-class presentation and to prepare a group project work with real data according to their interest.

The course grade determination, as a consequence, is as follows. Homework assignments and in-class presentation: around 30-40%. Final project work realization, presentation and discussion: around 60-70%.

Not applicable