Attività formativa monodisciplinare
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Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.: 
Insegnamento (nome in italiano): 
Insegnamento (nome in inglese): 
Data Production and Analysis
Tipo di attività formativa: 
Attività formativa Caratterizzante
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Altre informazioni sull'insegnamento

Modalità di erogazione: 
Didattica Convenzionale
Secondo Semestre
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Ore di attività frontale: 
Ore di studio individuale: 
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Educational goals

The course aims are:
-to let students be able to design and manage data production processes, including the definition of the main dimensions of quality and how to monitor and evaluate them,
- to be able to apply methods suitable
- toproduce and analyze data in their specific field.
Starting from their ability on the use of different types of data sources (censuses, sample surveys – cross section, longitudinal –, administrative sources, big data) and to critically evaluate pros and cons, also in terms of implications of the results and of the statistical standards, in this course students will learn and become aware of different production models. The course includes also attention to the business and enterprise architecture concepts applied to official statistics (e.g. metadata management, Generic Statistical Business Process Model, data archiving, mixed mode surveys, statistical standard classification).
The course “Data production and analyses” offers a foundation for professional activities in the field of data collection on social and economic facts in the fast-changing official data production system and in modernization of official statistics. The content of this course is useful also for other professional activities characterized by a strong need of awareness on the production and usage of official statistics and/or by the need to collect their own data (economic and social data or sales, markets, customers and/or employees satisfaction).
Ability to apply adequate methods at different steps of the data collection process (quality check, missing data, imputation, etc.) is achieved through the course lectures and laboratories. Ability to process, analyze and model the data is learned.
The course is fully coherent with the education aims of the EMOS (European Master in Official Statistics) label, of which is a milestone, as well as for the Master in Economic and Data Analysis.
Students will learn how to collect socioeconomic data and identify problems and quality of the data, even big data. They also will be able to apply some modeling on the collected data both to understand behaviors and enlarge the information by using different data sources in an integrated way.

Course content

The course is organized in two parts, since some topics are covered from one teacher and some others are covered from another teacher.
Topics rely on data production topics and on the data analysis.

The following topics are the core content of the course.
• The steps in the data production process, the decisions due at each step. How to be aware of interactions between the different steps, possible adaptive design approaches, usage of paradata and problems in integration of different sources. Understand how to manage big data; pros and cons for statistical purposes.
• Theoretical knowledge and ability to select appropriate sampling methods, select samples, apply weighting and nonresponse adjustment and provide data imputation. Sampling methods, non-response adjustment, weighting, imputation.
• Errors and risks of errors and quality indicators; TSE (total survey error) perspective.
• The conceptual framework as important part in updating, streamlining and aligning the standards and production associated with official statistics at both national and international level.
• Being aware of pros and cons and of methodology of mixing models in surveys.
• Understanding how a business model operates in the official statistics data production.
• The GSBPM (Generic Statistical Business Process Model)
• Understanding the role of metadata in general and of statistical standard classification system.
• Data editing and imputation in practice.
• Problems connected to cross country data.
• Microdata: how to use and analyze them.
•Big data and sentiment analysis.
• Modeling data: multilevel model estimation

Textbooks and reading lists

- "Handbook on Methodology of Modern Business Statistics", CBS-Eurostat
- Big data and social sciences ed. Foster et., selected sections according to the on demand approach and on the specific application undertaken in the course.
- E-learning environment includes:
Lectures (a selection), seminars material, the assessments and news for the students.

Teaching methods

Lectures from the professor; active discussion will be solicited.
Experts of national and international bodies invited during the course for discussion and presentation of specific topics.
Students will undertake individual case studies or personal research projects (time in the classroom is in part devoted to this activity), to be discussed and evaluated.

Assessment and Evaluation

The course is organized in two parts.
The final evaluation is the mean of the two parts scores.
Each part evaluation is based on:
-the assessment provided by the professor (published in the e-learning environment)
- individual case study reports and ppt presentations will be prepared and presented by the student
- oral discussion of the case studies and about the course contents.

Further information

Since Eurostat is providing every year new teaching material for EMOS labeled master in order to obtain high quality and innovative educational standards, recognized at international level, the teaching activity will take into account the innovative material. Students are informed of the new Eurostat material inside the e-learning environment.