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Lecturer(s)
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Juříková Martina, Ing. Ph.D.
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Course content
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1.-2. Data mining, Big data, the meaning of "data mining and extraction," conditions and process of implementing and managing data mining in a company. 3.-5. Specifics, possibilities, and limits of data mining for small businesses. 6.-8. Database marketing and its connection to data mining in an offline environment - important types of information, their acquisition, analysis, and interpretation for marketing decisions. 9.-10. Data mining in an online environment, what and how to "extract" from social networks and online marketing. 11.-13. Web mining - tools for deeper analysis and connection with CRM modules.
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Learning activities and teaching methods
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- Home preparation for classes
- 24 hours per semester
- Participation in classes
- 8 hours per semester
- Term paper
- 18 hours per semester
- Preparation for examination
- 25 hours per semester
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| prerequisite |
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| Knowledge |
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| Prerequisities are not set. |
| Prerequisities are not set. |
| learning outcomes |
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| Knowledge of the concept of data mining |
| Knowledge of the concept of data mining |
| Knowledge of data mining techniques |
| Knowledge of data mining techniques |
| Knowledge of the benefits of data mining |
| Knowledge of the benefits of data mining |
| Knowledge of the most common areas of application of data mining |
| Knowledge of the most common areas of application of data mining |
| Knowledge of data mining efficiency measurement options |
| Knowledge of data mining efficiency measurement options |
| Skills |
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| Explain the principle of data mining |
| Explain the principle of data mining |
| Explain the difference between data mining and other research techniques |
| Explain the difference between data mining and other research techniques |
| Create a data mining usage plan |
| Create a data mining usage plan |
| Design appropriate data mining methods |
| Design appropriate data mining methods |
| Design a way to measure the effectiveness of data mining |
| Design a way to measure the effectiveness of data mining |
| teaching methods |
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| Knowledge |
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| Lecturing |
| Lecturing |
| Dialogic (Discussion, conversation, brainstorming) |
| Dialogic (Discussion, conversation, brainstorming) |
| Skills |
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| Lecturing |
| Lecturing |
| Dialogic (Discussion, conversation, brainstorming) |
| Dialogic (Discussion, conversation, brainstorming) |
| assessment methods |
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| Knowledge |
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| Composite examination (Written part + oral part) |
| Composite examination (Written part + oral part) |
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Recommended literature
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Data mining a complete guide - 2020 edition: practical tools for self-assessment. 2020 edition. Brendale: The Art of service, 2020. ISBN 978-0-655-92523-1.
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Dostál, Petr. Soft computing v podnikatelství a veřejné správě. Brno. Akademické nakladatelství CERM, 2015. ISBN 978-80-7204-896-0.
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EAGLE, Nathan a Kate GREENE. Reality mining: using big data to engineer a better world. 2014. ISBN 9780262529839.
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GRIMMER, Justin; ROBERTS, Margaret E. a STEWART, Brandon M. Text as data: a new framework for machine learning and the social sciences. Princeton University press, 2022. ISBN 978-0-691-20755-1.
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HENDL, Jan. Big data: věda o datech - základy a aplikace. Praha: Grada Publishing, 2021. ISBN 978-80-271-3031-3.
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LEVENTHAL, Barry. Predictive analytics for marketers: using data mining for business advantage. London: Kogan Page, 2018. ISBN 9780749479930.
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WITTEN, I. H.; FRANK, Eibe; HALL, Mark A. a PAL, Christopher J. Data mining: practical machine learning tools and techniques. Fourth edition. Amsterdam: Elsevier, 2017. ISBN 9780128042915.
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