| Course title | Hybrid Data Science Models in R and Python |
|---|---|
| Course code | KI/0208 |
| Organizational form of instruction | Seminary |
| Level of course | Bachelor |
| Year of study | not specified |
| Semester | Winter and summer |
| Number of ECTS credits | 2 |
| Language of instruction | Czech |
| Status of course | Compulsory-optional |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Course availability | The course is available to visiting students |
| Lecturer(s) |
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| Course content |
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1. data analysis based on data visualization in R (graphics and ggplot2 packages) 2. data analysis based on data visualisation in Python (matplotlib and plotly modules) 3. processing of missing values in R based on different types of regression, choice of the optimal model 4. methods of data preprocessing in R and Python (normalization, standardization), criteria for evaluating the quality of data processing, comparison of models and choice of the optimal method 5. hybrid signal filtering model based on joint use of wavelet analysis and Huang mode decomposition method 6. practical implementation of hybrid signal filtering models in R and Python 7. clustering model optimization methods based on joint use of internal and external clustering quality evaluation criteria 8. optimization of clustering algorithm parameters using Harrington method and fuzzy logic in R and Python 9. optimization of data sorting model using different data sorting criteria 10. hybrid data dimension reduction models based on joint use of clustering and sorting models 11. practical implementation of hybrid data dimension reduction models in R and Python for big data processing (gene expression) 12. development of a gene expression-based patient prognosis model using data mining and machine learning methods
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| Learning activities and teaching methods |
| unspecified |
| Learning outcomes |
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The aim of the course is to acquire knowledge and skills in information processing. During the course, students will get introduced to the practical implementation of different methods of data analysis and preprocessing (visualization, missing value processing based on different types of regression models, normalization and filtering) and to hybrid models created using data mining and machine learning methods, which are designed for processing different types of data. The course will use R and Python software functions and modules.
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| Prerequisites |
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unspecified
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| Assessment methods and criteria |
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unspecified
accomplishment of the assigned tasks |
| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester | |
|---|---|---|---|---|
| Faculty: Faculty of Science | Study plan (Version): Information Sciences (double subject) (A14) | Category: Informatics courses | - | Recommended year of study:-, Recommended semester: - |
| Faculty: Faculty of Science | Study plan (Version): - (A14) | Category: Informatics courses | - | Recommended year of study:-, Recommended semester: - |
| Faculty: Faculty of Science | Study plan (Version): Information Sciences (double subject) (A14) | Category: Informatics courses | - | Recommended year of study:-, Recommended semester: - |
| Faculty: Faculty of Science | Study plan (Version): Information Systems (A14) | Category: Informatics courses | - | Recommended year of study:-, Recommended semester: - |