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Lecturer(s)
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Podaras Athanasios, Ing. Ph.D.
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Lamr Marián, Ing. Ph.D.
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Nowak Stanislav, Ing. Bc.
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Course content
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Lectures (topics): 1. Data and their processing, data collection. Data import and export. 2. Division of data mining tasks, introduction of typical tasks. 3. The process of acquiring knowledge from large data structures, the CRISP-DM methodology. 4. Data preparation, data comprehension, dataset description, data matrix preparation, data selection and cleaning, data source design and integration, type homogeneity, data formatting. 5. Current tools used for advanced data analysis and data mining. 6. Basics of data mining models. 7. Using association rules to predict customer behaviour. 8. Classification and typical classification tasks. 9. Prediction and segmentation. 10. Models evaluation and assessment. Seminars (topics): 1. Introduction to the IBM SPSS Modeler. 2. Preparation, analysis and visualization of data. 3. Tasks for prediction of customer behaviour. 4. Analysis of the shopping basket using the association rules. 5. Efficiency of marketing offers. 6. Targeting a marketing campaign. 7. Migrating customers to competition. 8. Use of text to predict customer behaviour. 9. Customer segmentation. 10. Tasks for human resources analysis in an enterprise.
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Learning activities and teaching methods
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Lecture, Practicum
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Learning outcomes
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The aim of the subject is to introduce students to the issues of decision making based on different types of data. Individual steps of the knowledge gaining process will be demonstrated on practical tasks. Students will get acquainted with techniques, tools and algorithms which are used during the process. At the seminars, students will get acquainted with the IBM SPSS Modeler and other open source tools, which are used to solve a wide range of managerial decision-making tasks based on a lot of data. Data mining procedures and algorithms, as well as the CRISP-DM methodology, will be introduced.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
Pretest: Active participation in seminars, semestral thesis, defense of the semestral project. Exam: Written and oral part
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Recommended literature
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HAN, Jiawei. a Micheline. KAMBER, 2012. Data mining: concepts and techniques. 3rd ed.. Burlington, MA: Elsevier., 2012. ISBN 9780123814791.
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HOFMANN, Markus a Ralf. KLINKENBERG. RapidMiner: Data Mining Use Cases and Business Analytics Applications.. Florida: Taylor & Francis Group., 2013. ISBN 9781482205497.
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PETR, Pavel. Metody Data Miningu.. Pardubice: Univerzita Pardubice, 2014. ISBN 9788073958732.
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SHMUELI, Galit, Peter C. BRUCE, Mia L. STEPHENS a Nitin R. PATEL. Data mining for business analytics: concepts, techniques, and applications in JMP Pro. 1.. Canada: WILEY, 2016. ISBN 978-1-118-87743-2.
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WENDLER, Tilo a Sören GRÖTTRUP. Data mining with SPSS modeler: theory, exercises and solutions. 1. Switzerland: Springer, 2016. ISBN 978-3-319-28707-2.
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WITTEN, I. H. a Frank EIBE. Data mining: practical machine learning tools and techniques:Fourth Edition. Cambrige, 2017. ISBN 9780128042915.
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