Foundations of data analytics

Osnove podatkovne analitike

Course credits:

6.00 ECTS / 45 (30 hours of lectures, 0 hours of exercises, 15 hours of seminars, 0 hours other forms of work)

Course holder:

izr. prof. dr. Katja Lozar Manfreda

Type:

Required

Language:

English

Semester:

First semester

Study degree

2. level

Course execution:

• Postgraduate Programme of Journalism
• Postgraduate Programme of Social Informatics

Prerequisits:

Students are allowed to participate in the course provided that they have completed the enrolment procedure.

Objectives and competences

The objectives of the course are (1) to introduce students to the field of data analytics and to present the data analytics processes (data types, data management, statistical analysis, visualisation, archiving, dissemination), and (2) to offer knowledge to use tools of descriptive and inferential statistic. This will allow them to critically evaluate statistical data and analyses, as well as discovering knowledge in data and perform actual data analysis in the field of social sciences. Students will obtain the following competences: - knowledge of data analytics processes in social sciences, - ability to ask questions which can be answered using data analysis, - awareness of importance and relevance of statistical data, - use of basic principles of data management and visualisation, - analysis, interpretation and reading statistical data and analyses in order to discover meaning in data, - ability to use at least one software tool for statistical data analysis, - knowledge of guidelines to present statistical data and analyses.

Content (Syllabus outline)

The course covers basic statistical concepts and tools for data analytics in social sciences, from asking right questions which can be answered using data analysis, through data management, data visualisation, analysis and inference, to dissemination of results. The course will cover the following topics: Introduction to data analytics (definitions, importance of statistics, statistical techniques for data analytics). Types of statistical data (survey data, official statistics data, open (big) data etc.). Basic statistical concepts (population, sample, measures of central tendency and variation, sampling & representativness, inference, confidence, statistical significance). Basic principles of data management. Basic exploratory analysis and simple data visualization. Basic methods of statistical analysis: measures of central tendency and variability, cross-tabulations, correlations, difference in means, regression. Guidelines for dissemination of statistical data and results of statistical analysis. Students will learn to use software application(s) and tool(s) for statistical data analysis.

Intended learning outcomes:

List, define and recognize data analytics processes; statistical data and their visualisations; measures of central tendency and variability, distributions, methods to analyse relationships among variables, inferential statistics. Use approaches to for data management, exploratory data analysis, analysis of relationships among variables, to inter to population. Prepare data, analyse and interpret them, infer from sample to population, disseminate.

Learning and teaching methods:

Lectures, seminars, individual and group work. E-learning.

Assessment

- mid-term exams or final written exam 50 % - group and individual assignment 50 %)

Obligatory literature

Additional literature