Course type: compulsory
Lecturer: Mojca Bavdaž
 

For the timeline see Curriculum.

Prerequisites:

• Enrolment into the first year of the program is required to participate in the course.
• Positively graded assignment is a prerequisite for written exam.

Content (Syllabus outline):

1. General issues about data sources (Basic terminology. Characteristics of data and data sources. Different kinds of access to data.)

2. Big data and ethics (creative use, scope, bias, disclosures, misuses).

3. Data confidentiality and data protection (legal aspects, statistical disclosure control).

4. Data visualization:

• Milestones in data visualization.
• Basics of visual processing (preattentive characteristics, Gestalt principles, bias).
• Problematic charts.
• Approaches to improving visualizations.
• Visualizations by data types.

5. Data in official statistics:

• Functioning of the system of official statistics (institutions, organization, legislation, principles).
• Data sources in official statistics.
• Statistical infrastructure (registers, statistical standards, metadata).
• Quality (principles, code of practice, accuracy and errors).
• Accessibility, tools and approaches for obtaining and using statistical data.
• Visualisation and exploratory data analysis in official statistics.

6. Data search and secondary analysis of data from scientific data archives:

• General data archives (Social Sciences Data Archive etc.) and specialised data archives (e.g. qualitative, networks).
• Access to international data and different disciplinary data (CESSDA, ICPSR, international research projects, organisations).
• Preparation of data for analysis: merging from different sources and formats, ex-post harmonisation of variables, data cleaning and documentation.
• Advanced options for exploiting existing data (multilevel analysis, comparative analysis, longitudinal research etc.).
• Research data management and planning.
• FAIR data assessment.
• Ethical and legal aspects of data management.
• Open science ecosystem.

7. Specific data sources from the fields of social and natural sciences (e.g. commercial databases, data sources in public and private sector, data sources in specific scientific fields such as public health and medicine etc.).

Other relevant topics.

Objectives and competences:
Course objectives are to:

• Introduce students to the most important national and foreign sources of statistical data and possibilities of their use.
• Enable students for an efficient use of statistical data sources in their research field (statistical and data literacy).
• Introduce students to the basic principles of data management and access rules.

Competences:

• Ability to judge the usefulness of statistical data sources for statistical analysis.
• Knowledge of tools and approaches to obtaining and searching for statistical data.       

Intended learning outcomes:
Students will deepen and extend their knowledge of data sources from various scientific disciplines and understanding of their importance and role in statistical analysis. They will get the latest knowledge about their availability, access and possibilities of exploitations, and improve their optimal strategies of obtaining and exploiting these data sources.