Intelligent Data Analysis
| Tittle | Intelligent Data Analysis |
| Department | Computer Science |
| Type (compulsory/optional) | Optional |
| Cycle (first/second) | Second Cycle |
| Semester when the component is delivered | 2nd and 3rd semesters |
| Course description | The purpose of discipline is to develop students’ skills of using and application of intelligent data analysis such as data mining, practical methodologies for building the tree of data mining, the implementation of cross-tab for data analysis, search associative rules problem solving, classification problem solving, regression and clustering problem solving, application of artificial intelligence in DSS, use ontological approach to the application of knowledge in the enterprises. |
| Course content |
Module
1. Data Mining - means of data mining in the DSS Topic 1. Development and purpose of Data Mining.Topic 2. Tree of data mining methods. Topic 3. Saved data. Distilled data. Topic 4. Characteristics of processes and activities of data mining. Module
2. Available software for data mining Topic 5. PolyAnalyst. MineSet - visual tool for analyst. Knowledge
STUDIO. Intelligent Text Mining technology.Topic 6. Available software for genetic algorithms. Topic 7. Software agents in DSS. Topic 8. Artificial intelligence in the DSS. Neural networks. |
| Learning outcomes | - ability to use OLAP-technologies with building data hypercubes. - ability to implement cluster analysis by means of data mining; - ability to select the logical methods with building the transactions table; - ability to use statistical and cybernetic methods of Data Mining. |
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Contact hours (lectures/seminars)
|
20/8 |
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Prerequisites
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Project Management Software, Simulation and Analysis Software |
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Number of ECTS credits allocated
|
5 credits |