Objectives and competences
The goal of this course is for the student to acquire the basic knowledge of green research in data science with the help of bibliometrics.
Content (Syllabus outline)
• Basics of research and green approaches to research in data science (green approach to research, principles reduce, reuse, and recycling in research, bibliometrics as a green research tool)
• Bibliometric tools and concepts (tools for bibliometric analysis, bibliometrics in data science, comprehensive overview of research areas)
• Collections of scientific literature (various data sources, metadata in bibliographic scientific collections)
• Research methodologies (quantitative research methods, qualitative research methods)
• Planning bibliometric research (research topics, research questions, selection of a suitable bibliometric collection, data collection, data pre-processing)
• Bibliometric data analysis (use of bibliometric tools, selection of suitable methods according to research questions, processing of visualization elements, interpretation of results, visualization of results)
Learning and teaching methods
• written exam,
• lab work.
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
• understand the use of bibliometrics in the research process,
• choose suitable bibliometric tools and suitable databases.
Transferable/Key skills and other attributes:
• Green research: use of effective tools and the principles reduce, reuse, and recycling,
• Use of software: use of bibliometric tools and their methods,
• Using bibliometric approaches in data science for research purposes: obtaining bibliographic data, analyzing bibliographic data, interpreting bibliographic data, and using suitable visualization methods for presenting results.
Readings
• Ian Foster, Rayid Ghani, Ron. S. Jarmin, Frauke Kreuter, Julia Lane (Eds.): Big Data and Social Science: Practical Guide to Methods and Tools, 1st Edition, Chapman and Hall/CRC, 2016.
• Anol Bhattacherjee, Social Science Research: Principles, Methods, and Practices, 2nd Edition. CreateSpace Independent Publishing Platform, 2012.
• Fred Pyrczak, Randall R. Bruce: Writing Empirical Research Reports, 7th edition, Routledge, 2011.
• Gorraiz, Juan Ignacio, Rafael Repiso, Nicola De Bellis, Gernot Deinzer: Best Practices in Bibliometrics & Bibliometric Services, 2022.
• Karakatič, S., & Fister, I. (2022). Strojno učenje: s Pythonom do prvega klasifikatorja (1. izd., pp. 1 spletni vir (1 datoteka PDF (158, 2 ))). Univerza v Mariboru, Univerzitetna založba; Fakulteta za elektrotehniko, računalništvo in informatiko. doi:10.18690/um.feri.1.2022.
Prerequisits
No prerequisites.
Additional information on implementation and assessment The exam may be replaced by written midterm examinations in the weight of 50%.
