| Course title | Python and R for Data Science |
|---|---|
| Course code | KI/EPYR |
| Organizational form of instruction | Lecture + Lesson |
| Level of course | unspecified |
| Year of study | not specified |
| Semester | Winter |
| Number of ECTS credits | 6 |
| Language of instruction | English |
| Status of course | unspecified |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Lecturer(s) |
|---|
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| Course content |
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1. Deepening the basics of syntax and basic constructions of Python and R languages 2. Basics of data manipulation and visualization 3. Intermediate data and data file manipulation (import, cleaning, etc.) 4. Intermediate data visualization 5. - 6. Exploratory analysis, selected advanced statistical methods (correlation, regression, factor, cluster analysis, etc.), inference statistics 7. - 8. Introduction to machine learning (selected classificators, regression and clusterring algorithms) 9. Introduction to natural language processing, sentiment analysis 10. Network analysis 11. - 12. Reports, dashboards and interactive data visualization 13. Summary, discussion on the assignment of seminar works
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| Learning activities and teaching methods |
| unspecified |
| Learning outcomes |
| Prerequisites |
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Basics of programming in Python and R, basic knowledge of soft computing
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| Assessment methods and criteria |
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unspecified
preparation and oral defense of a seminar work aimed at data processing, exploratory analysis and machine learning, verification of general factual knowledge |
| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester |
|---|