Objectives and competences
Students gain and enhance their knowledge of contemporary methodological approaches in data science. They develop analytical skills in modeling and identifying patterns and trends in data. Using the open-source software tool R, they are able to analyze empirical cases, select the appropriate methodological approach, critically evaluate, and appropriately interpret the results.
Content (Syllabus outline)
1. R fundamentals for data science
2. Data visualization with R
3. Ethics of data science and reproducible research with R Markdown
4. Tree-based models in R
5. Generalized linear models (GLM)
6. Generalized additive models (GAM)
7. Automated modelling with h2o
Learning and teaching methods
Lectures,
technical demonstrations,
active individual and group work
Intended learning outcomes - knowledge and understanding
In the course Data science with R, students:
1. Systematically gain and enhance knowledge of contemporary methodological approaches in data science.
2. Develop the ability to apply theoretical knowledge from the field of data science to empirical cases. They develop competencies for independent research work in this area. (PILO 2a, PILO 2c)
3. Learn to compare and critically assess different methodological approaches.
4. Formulate or develop an appropriate model and evaluate it in the open-source software tool R. They become skilled in using the R programming tool for practical examples. (PILO 1a, PILO 3a)
5. Appropriately present and interpret the results obtained through quantitative methods. (PILO 3b)
6. Acquire the ability to search for and synthesize information from the field of data science in contemporary literature and the ability to think critically. (PILO 3a)
7. Develop skills for independent and group empirical work and enhance their collaboration and communication abilities. (PILO 3c)
8. Develop the capacity for ethical decision-making in data science and for conducting reproducible research. (PILO 4a)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Intended learning outcomes - transferable/key skills and other attributes
In the course Data science with R, students:
1. Systematically gain and enhance knowledge of contemporary methodological approaches in data science.
2. Develop the ability to apply theoretical knowledge from the field of data science to empirical cases. They develop competencies for independent research work in this area. (PILO 2a, PILO 2c)
3. Learn to compare and critically assess different methodological approaches.
4. Formulate or develop an appropriate model and evaluate it in the open-source software tool R. They become skilled in using the R programming tool for practical examples. (PILO 1a, PILO 3a)
5. Appropriately present and interpret the results obtained through quantitative methods. (PILO 3b)
6. Acquire the ability to search for and synthesize information from the field of data science in contemporary literature and the ability to think critically. (PILO 3a)
7. Develop skills for independent and group empirical work and enhance their collaboration and communication abilities. (PILO 3c)
8. Develop the capacity for ethical decision-making in data science and for conducting reproducible research. (PILO 4a)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Readings
Osnovna študijska literatura (Compulsory textbooks):
Izbrana poglavja iz naslednjih knjig/ Selected chapters from the following textbooks:
Baumer, B. S., Kaplan, D. T., & Horton, N. J. (2021). Modern Data Science with R (2nd ed.). Chapman and Hall/CRC.
Pridobljeno 9. 3. 2026 iz https://mdsr-book.github.io/mdsr2e/
Boehmke, B., & Greenwell, B. M. (2019). Hands-On Machine Learning with R (1st ed.). Chapman and Hall/CRC.
Pridobljeno 9. 3. 2026 iz https://bradleyboehmke.github.io/HOML/
Wickham, H., & Grolemund, G. (2023). R for Data Science (2nd ed.). O'Reilly Media.
Pridobljeno 9. 3. 2026 iz https://r4ds.hadley.nz/
Dodatna študijska literatura (Additonal textbooks):
- KAVKLER, Alenka. Linear and nonlinear panel regression models for fiscal policy evaluation. V: Proceedings. Vol. 1, "High technologies". VII International Scientific Conference High Technologies. Business. Society 2022, 07-09.03.2022, Borovets, Bulgaria. Sofia (Bulgaria): Scientific Technical Union of Mechanical Engineering Industry-4.0, 2022. Str. 85-87. International Scientific Conference High Technologies. Business. Society (Online), Year 6, iss. 1, 2022. ISSN 2535-0013. [COBISS.SI-ID 103973379]
- Izbrani članki za prikaz uporabe drevesnih modelov. (Selected papers on the application of tree-based models.)
Additional information on implementation and assessment Written exam 40 %
Seminar paper 30 %
In-class participation 30 %
Notes:
The student may replace the written exam with two midterm exams.
Seminar paper - the student writes a seminar paper on a selected topic and presents it in class
In-class participation - problems from each chapter
