Lectures (topics): 1.Relational Databases - RDBMS, Tables, Relationships, Attributes, Data Types, ER Diagram 2.Relational Databases II- Referential Integrity, Cardinality, Primary and Foreign Keys, Normalization (1,2,3 NF) 3.Modeling Business Decisions - Flowcharts, Iterations, Incremental Approach, Use Cases 4.Decision Making Based on Multidimensional Data Models - Business Intelligence Architecture, Hierarchies, Granularity, Data warehouse Schemas 5.OLAP Tools for Business Decisions, ETL 6.Predictive Decisions Making with Data Mining Methods - CART, Classification, Simple Linear Regression, 7.Polynomial Regression, Multivariate Regression, Decision Trees, Business Rules Seminars (topics): 1.MS Access Tables, Relationships, Referential Integrity, Data Types 2.Calculated Fields, Lookup Values, Import/Export Datasets, introduction to the Expression Builder 3.MS Access Queries 1- Like, Between, Totals (Aggregate Functions) 4.MS Access Queries 2- Enter Parameter Value, IIF and Nested Decisions 5.Data Mining with Excel - Exporting Dataset with MS Query, If expressions in Excel, Regression modelling with Excel, Linest Function 6.Data Mining with R Scripts- Importing .csv file, splitting datasets, feature scaling, classification, decision trees 7.Data Mining Regression Modeling using R-Scripts
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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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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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