DATA PRODUCTION AND ANALYSIS | Università degli studi di Bergamo

DATA PRODUCTION AND ANALYSIS

Attività formativa monodisciplinare
Codice dell'attività formativa: 
110010-ENG

Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.: 
2020/2021
Insegnamento (nome in italiano): 
DATA PRODUCTION AND ANALYSIS
Insegnamento (nome in inglese): 
Data Production and Analysis
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: 
2020/2021
Crediti: 
6
Responsabile della didattica: 
Altri docenti: 

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
Prerequisites

None

Educational goals

The course “Data production and analysis” offers a solid background for professional activities in the field of data production, collection, treatment and analysis. It aims at supporting students in research developed in different fields (such as economy and social sciences). The course focuses mostly on the fast-changing official data production system, within the framework of the modernization and coordination phases of official statistics producers. On the one hand, the content of this course is useful for professional activities characterized by a strong need of awareness about the production cycle and about the usage of official statistics (even integrating available sources). On the other hand, it suggests the most common data collection methods that can be used in various field (economic and social data, survey data in general). Ability to apply adequate methods at different steps of the data production and collection process (quality check, missing data, imputation, etc.) is achieved through both course lectures and laboratories. Ability to process, analyze and model the data is learned using one of the most widely spread statistical software: SAS.

The course has the following specific objectives:
- Help students becoming able to design and manage data production processes in specific fields.
- Teach students how to evaluate and enhance data quality (taking into account the definition of the main dimensions of quality, learning how to evaluate them and applying suitable methods).
- Introduce students to the analysis of big data and of data that have a hierarchical structure (multilevel modelling).
- Guide students in using different types of data sources (censuses, cross section or longitudinal sample surveys, administrative sources, big data) and in critically evaluating pros and cons of such kind of sources (link with different production models, implications on the results quality).
- Introduce students to the business concepts applied to official statistics (e.g. Generic Statistical Business Process Model, data archiving, metadata management, statistical standard classification, imputation).

The course is fully coherent with the education aims of the EMOS (European Master in Official Statistics) label, of which it is a milestone, as well as for the Master course in Economic and Data Analysis.

Course content

The course is organized in two modules.

Module 1 (teacher: Daniele Toninelli):

• GSBPM (Generic Statistical Business Process Model) step by step: how an official statistics business model is organized and works for data production processes.
• Role of metadata: enhancing the quality standards of the information produced and best practices in communicating statistical outputs.
• Data editing and imputation methods in practice: fixing the most common issues with collected raw data enhancing the quality standards of the output produced.
• Introduction to the SAS statistical software: how to use it and program with it.
• Multilevel modelling: when it is useful, how to estimate them with SAS, how to interpret and use the main output for decision making.

Module 2 (teacher: Annamaria Bianchi):

• The steps in the data production process, the decisions due at each step. How to be aware of interactions between the different steps, pros and cons for statistical purposes.
• Probability-based surveys: basic concepts, sampling methods, mode of data collection, errors and total survey error paradigm, quality framework, European Statistics Code of Practice, questionnaire design, non-response correction methods, estimation.
• Sample selection, estimation and non-response analysis with SAS.
• Non-probability samples: convenience samples, quota samples, volunteer web panels.
• Coverage and self-selection problems in non-probability samples.

Teaching methods

Lectures and lab sessions (with active discussions and participation solicited).

Individual cases or personal projects developed by students according to the themes proposed by the teachers.

Assessment and Evaluation

The course exam will be organized in two different parts, corresponding to the topics of Module 1 and 2. The final evaluation is the sample average of the two scores.

Each module evaluation can be based on:
• A theoretical oral final exam.
• Oral discussion about case studies, research results and/or based on a deeper discussion of the course topics.
• Assessments provided by the professors, including case studies, reports and ppt presentations can be proposed and will be considered as part of the final evaluation.

Further information

Since Eurostat is providing every year new innovative teaching material for EMOS labeled masters, in order to obtain high quality and innovative educational standards (recognized at international levels), the teaching activity will be constantly updated.

Important note: if the course will take place (partially or fully) online, some changes can be introduced in the course syllabus. This in order to adapt both the course and the exam to an online attendance.