ECONOMIC STATISTICS AND BIG DATA

Attività formativa monodisciplinare
Codice dell'attività formativa: 
110018-ENG

Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.: 
2018/2019
Insegnamento (nome in italiano): 
ECONOMIC STATISTICS AND BIG DATA
Insegnamento (nome in inglese): 
Economic Statistics and Big Data
Tipo di attività formativa: 
Attività formativa Caratterizzante
Tipo di insegnamento: 
Obbligatoria
Settore disciplinare: 
STATISTICA ECONOMICA (SECS-S/03)
Anno di corso: 
1
Anno accademico di offerta: 
2018/2019
Crediti: 
6
Responsabile della didattica: 
Mutuazioni

Altre informazioni sull'insegnamento

Modalità di erogazione: 
Didattica Convenzionale
Lingua: 
Inglese
Ciclo: 
Primo Semestre
Obbligo di frequenza: 
No
Ore di attività frontale: 
48
Ore di studio individuale: 
102
Ambito: 
Statistico-matematico
Materiali didattici: 
Prerequisites

None.

Educational goals

“Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write”–H.G.Wells. Moreover, "What steam was to the 19th century, and oil has been to the 20th, data is to the 21th" and "if you can’t measure it, you can’t manage it.”(Evidence based decision making). Official statistics needs new ways to create indicators and new indicators, users (decision makers, businesses and citizen) need to know more about the data and how to use them. Official statistics providers and data provider in general understand there is a need of an innovative education to provide culture on how to produce data for socio-economic analysis and businesses and on how to understand and process statistically the indicators.
Objectives: a) to be aware of the relevance of official statistics as information infrastructure for the society and of its principles; b) to be aware of the main institutions operating at national and international level and their data sources (e.g. Eurostat, ECB, IMF, ILO, BIS, UN, OECD, World Bank); c) to understand the principles, limits and advantages of big data as a statistical data source; d) to understand the principles, limitations and advantages of big data as a statistical data source.
This course will acquaint students to the socio-economic data sources, indicators and to basic approaches for generating and analyzing data. It will be discussed how data can be used to make smart decisions in various aspects of working and private environment.
Students will be aware of the data sources of main national and international sources and understand differences in data sources.
Students learn how to collect economic data, characteristics of the data, statistical analyses based on socio-economic data. Students get acquainted on how to build and search economic indicators and use them for decision making.
Students will learn how to draw conclusions from them using critical thinking skills and statistical terms.
Problem solving and examples within many subfields are discussed.
Big data as a source for economic analysis in a context of integrated data sources is introduced.

The flow and relationships between businesses internal data and macroeconomic indicators is discussed, too.
Knowledge of the characteristics of the sources will be considered (classifications, definitions ,and so on)
Most courses rely to official statistics data for their applicative part, thus this learning outcome is supported by different inputs provided in various courses.

Course content

Economic data sources and data collection methods for socio-economic and business decision making: Type of sources: surveys (special attention to on line surveys); other data collection methods (administrative database, social networks, big data); sources integration. Balance sheets as a source of data; from balance sheets to macroeconomic data.

Indicators: how to build them for an integrated economic analysis. Types of indicators: Economic sustainable wellness, Beyond GPD, social/economic indicators, territorial analyses, market indicators.
Short and long term analyses.
Graphical analyses for scenario (dynamic graphics). Techniques for territorial and markets growth (shift share).
Economic statistics concepts for evidence based decision making in the complex socio-economic scenario

Big data: what they are, how to use them.
Data sources integration. Administrative data for statistical purposes.

Being aware of main dimensions of quality and risks of errors.

Producing clear and correct tables, reference to adequate sources, not using misleading information.

Textbooks and reading lists

Giovannini E., Understanding economic statistics, OECD, 2008; teacher minutes (and slides).

Moodle course is the repository of the lecture minutes and material. To access the course material students are invited to write an email; please specify your name, university record number and course you want to access.

Teaching methods

Lectures and open discussions mixed with activities as supplementary learning tools. Problem solving and examples within many subfields are discussed.
Students will work in a case study during the course; activities will be carried out in the laboratory.
Seminars.

Assessment and Evaluation

Case studies reporting and discussion.
Oral examination on the course content comprehension (for issues not included in the case study).

Further information

Students interested in the ongoing macro trends at international level and in the related research for thesis and stages in this discipline is supervised from the lecturer and engaged in the undergoing research.