Data analysis and legal protection of data

Completed course Programming 1.

Elementary data analysis. Selecting rows and columns, data summarization. Advanced SQL.

Data visualization anf graphs. Components for building regular and spatial visualizations.
Advanced data analysis. Clustering. Distances on data units and clusters.
Reports on data analysis. Mark-up languages for basic and interactive reports.
Basic principles and rules related to GDPR. Data protection and security.
Legal sources on statistical big data processing in the EU and Slovenia.
Institutions for data protection and suitability of statistical data analysis.
Statistical confidentiality. Statistical unit, (pseudo)anonymisation, and synthetic data.
Data use in scientific research (EU Regulation on access to confidential data for scientific purposes). Practice of the Statistical Office of the Republic of Slovenia.

• Wes McKinney (2017) Python for Data Analysis. Druga izdaja. O'Reilly Media, Inc. Prosto dostopna na https://wesmckinney.com/book/. Izbrana poglavja.
• José Unpingco (2021) Python Programming for Data Analysis. Springer. Izbrana poglavja.
• N. Pirc Musar (ur.): Komentar Splošne uredbe o varstvu podatkov, Uradni list RS, Ljubljana, 2020, izbrana poglavja.
• M. Prelesnik (ur.): Komentar ZVOP-2, GV, Lexpera, Ljubljana, 2023, izbrana poglavja.
• U. Pagallo: The Legal Challenges of Big Data, European Data Protection Law Review, vol. 3, 1, 2017, 36 - 46, https://doi.org/10.21552/edpl/2017/1/7.
• R. Ducato: Data protection, scientific research, and the role of information, Computer Law & Security review, vol. 37, 2020, https://doi.org/10.1016/j.clsr.2020.105412.
• Temeljni predpisi: uredbe EU, ZVOP-2, ZDSta.
• Izbrane spletne strani Eurostat, EDPB/S, IP, SURS.

Students get familiar with the basic concepts of data analytics, on the one hand, as well as legal concepts and resources in the field of data security and the suitability of the procedures for data analysis or statistical processing, on the other. They acquire practical knowledge of using libraries in the Python programming language to retrieve web data, prepare data tables, and analyze and visualize data. They learn to understand and follow the legal framework for analyzing massive data.

Knowledge and understanding: Students get to know the basic concepts and procedures of data analysis as well as legal concepts, grounds, and resources in the field of data protection.
Application: Students can independently design, implement and implement effective data analytics procedures from data acquisition to writing reports that consider legal restrictions in the field of data protection.
Reflection: Analysis of the importance of modern technology in analyzing mass data and the importance of legal bases and limitations in data analysis.
Transferable skills – not tied to just one subject: Algorithmic thinking, basic understanding and use of legal sources, ability to formulate computational problems and choose appropriate methods.

Lectures, exercises, homeworks, seminar/group work, consultations.

Homework, seminar work
Theoretical exam

BRENCE, Jure, DŽEROSKI, Sašo, TODOROVSKI, Ljupčo. Dimensionally-consistent equation discovery through probabilistic attribute grammars. [Print ed.]. 2022, vol. 632, str. 742-756. ISSN 0020-0255. DOI: 10.1016/j.ins.2023.03.073.
LUKŠIČ, Žiga, TANEVSKI, Jovan, DŽEROSKI, Sašo, TODOROVSKI, Ljupčo. Meta-model framework for surrogate-basedparameter estimation in dynamical systems. IEEE access. 2019, vol. 7, str. 181829 -181841. ISSN 2169-3536. DOI: 10.1109/ACCESS.2019.2959846.
GRAU LEGUIA, Marc, LEVNAJIĆ, Zoran, TODOROVSKI, Ljupčo, ŽENKO, Bernard. Reconstructing dynamical networks via feature ranking. Chaos. 2019, vol. 29, no. 9, str. 09310-1-093107-15. ISSN 1054-1500. DOI: 10.1063/1.5092170.