Objectives and competences
- Ability to use R software, the free programming environment for statistical computing and graphics,
- In-depth understanding of data visualization and analytics,
- Ability to solve complex statistical dilemmas,
- Discovering patterns and trends through data analysis for the purpose of creating business strategies,
- Analytical modeling skills using complex datasets.
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
Knowledge and understanding:
Students:
- Acquire comprehensive and in-depth knowledge in the field of data science with R.
- Are able to apply theoretical knowledge of data science and research in this field in their work; understand scientific articles and know how to use them.
- Are aware of ethical dilemmas and have a proactive approach to finding solutions to these dilemmas.
- Know and understand a wide range of models and techniques in the field of data science.
Intended learning outcomes - transferable/key skills and other attributes
Cognitive/Intellectual skills:
- Knows how to analyze complex areas of knowledge and explain the results of the analysis in an understandable way. Capable of critically comparing scientific articles in the field of data science.
- Can critically synthesize information in a way that is innovative and sees the practical value of knowledge or processes from both theoretical and practical perspectives.
- Shows originality in problem-solving. Is independent in planning and carrying out tasks at a professional level and is able to make decisions in complex situations.
Key/Transferable skills
- Knows how to successfully work in diverse teams and lead a group.
- Is independent and self-critical as a student.
- Can communicate clearly and effectively.
- Has the ability for creative thinking.
- Is capable of independently developing his professional skills.
Practical skills:
- Masters operating in complex, unpredictable, and/or special circumstances.
- Knows how to use analytical tools in business practice.
Readings
Baumer, B. S., Kaplan, D. T., & Horton, N. J. (2017). Modern Data Science with R (1st ed.). Chapman and Hall/CRC.
Pridobljeno 4. 4. 2023 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 4. 4. 2023 iz https://bradleyboehmke.github.io/HOML/
Wickham, H., & Grolemund, G. (2017). R for Data Science. O'Reilly Media.
Pridobljeno 4. 4. 2023 iz https://r4ds.hadley.nz/
Additional information on implementation and assessment Type (examination, oral, coursework, project):
Written examination or 2 written tests
Seminary work
Active (group) work
