1. SylabUZ
2. Faculty of Computer Science, Electrical Engineering and Automatics
3. winter term 2022/2023
4. Computer Science - Second-cycle Erasmus programme
5. Business intelligence systems

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Business intelligence systems - course description

Aim of the course

• developing skills in the design and implementation of data warehouses
• familiarize students with the methods of business data mining

Prerequisites

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Scope

Data Warehouses. Architectures. Review and characteristics of typical data transformation operations. Multidimensional data modelling. Design and implementation of OLAP cubes. Developing ETL packages. Columnar databases. Analytical queries in SQL. Reporting using Power BI.

Data mining. Methods for discovering association rules and sequences. Data clustering: k-means and agglomerative algorithm. Data classification: logistic regression, k-nearest neighbors algorithm, decision trees artificial neural networks. Practical exercises in data mining.

Teaching methods

Lecture, laboratory exercises.

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

Lecture – the passing condition is to obtain a positive mark from the final test.

Laboratory – the passing condition is to obtain positive marks from all laboratory exercises to be planned during the semester.

Calculation of the final grade: lecture 50% + laboratory 50%

Recommended reading

1. Aggarwal C.C.: Data mining, Springer, 2015.
2. James G, Witten D., Hastie T., Tibshirani R. An Introduction to Statistical Learning, Springer, 2014
3. Russo M., Ferrari A. Tabular Modeling in Microsoft SQL Server Analysis Services, Microsoft Press, 2017
4. SQL Server 2012 Tutorials: Analysis Services - Multidimensional Modeling SQL Server 2012 Books Online, Microsoft, 2012
5. Sarka D., Lah M. Jerkic, Implementing a Data Warehouse with Microsoft SQL Server 2012, O’Reilly, 201
6. Cote C., Lah M., Sarka D., SQL Server 2017 Integration Services Cookbook: Powerful ETL techniques to load and transform data from almost any source, Packt Publishing, 2017.
7. Deckler G., Learn Power BI, Packt Publishing, 2019.

Further reading

1. Kimball R., Ross M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (Second Edition), Wiley, 2002.
2. Kimball, R., Caserta J., The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data, Wiley, 2004.
3. Goodfellow I., Bengio Y. Courville A. Deep learning, MIT, 2016

Notes

Modified by dr hab. inż. Marek Kowal, prof. UZ (last modification: 20-04-2022 17:33)