1. SylabUZ
2. Wydział Informatyki, Elektrotechniki i Automatyki
3. semestr zimowy 2022/2023
4. Informatyka - Program Erasmus drugiego stopnia
5. Business intelligence systems

Wygeneruj PDF dla tej strony

Business intelligence systems - opis przedmiotu

Cel przedmiotu

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

Wymagania wstępne

Zapisz zmiany

Zakres tematyczny

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.

Metody kształcenia

Lecture, laboratory exercises.

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

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%

Literatura podstawowa

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.

Literatura uzupełniająca

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

Uwagi

Zmodyfikowane przez dr hab. inż. Marek Kowal, prof. UZ (ostatnia modyfikacja: 20-04-2022 17:33)