| Course title | Data Mining Fundamentals |
|---|---|
| Course code | KI/0183 |
| Organizational form of instruction | Seminary |
| Level of course | Bachelor |
| Year of study | not specified |
| Semester | Winter |
| 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 |
| Lecturer(s) |
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| Course content |
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1) Stages of data preprocessing and data presentation. Structure and types of data. Entering and importing of data into R Software. The use of 2D diagram to visualize of the data. 2) Data visualization methods. Basic diagrams, histogram and spinogram. Diagram formatting and presentation methods. 3) Packages of R software environment to graphical visualize of data: lattice and ggplot2. 4) Regression analysis. Simple, multiple, linear and nonlinear regression models. Implementation of the regression analysis using R software. 5) Preprocessing data methods. Filtration, standardization and normalization of the data. Missing value processing based on the regression analysis. Practical implementation of the methods using R software. 6) Principal component and factor analysis and their practical implementation using R software. 7) Basic principles of the objective clustering inductive technology. Criteria to estimate the clustering quality. Calculation of these criteria using R software. 8) Models of the objective clustering based on agglomerative hierarchical, SOTA and DBSCAN clustering algorithms. 9) Data classification algorithms and their practical implementation using R and VEKA software. 10) Forecasting methods and models. Fuzzy logic and neural networks. Implementation of models using R and MATLAB software.
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| Learning activities and teaching methods |
| unspecified |
| Learning outcomes |
| Prerequisites |
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unspecified
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| Assessment methods and criteria |
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unspecified
The course is intended for students of 2 and 3 years of study. |
| 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): Information Sciences (double subject) (A14) | Category: Informatics courses | - | Recommended year of study:-, Recommended semester: - |