1. komponenta

Lecture type	Total
Lectures	30
Exercises	15

* Load is given in academic hour (1 academic hour = 45 minutes)

COURSE AIMS AND OBJECTIVES:

Enable students to:
- critically think about the value of data stored in information systems and informed data supported decision-making
- implement a data warehouse and use it as an analytical tool for knowledge extraction and decision-making


COURSE DESCRIPTION AND SYLLABUS:
1. Business intelligence basics. Informed decision-making. Data warehouses. OLAP and OLTP.
2. Logical design of a data warehouse. Data warehouse models. Facts and dimensions. The full process of logical data warehouse design. Data granulation.
3. Implementing the data warehouse. Technological architecture of a warehouse. The ETL (extract, transform, load) process. Data wrangling and manipulation.
4. Data quality. Data cleansing. Missing data. Missing events.
5. Data warehouse in production. Reporting systems and user interfaces. Maintenance of a production data warehouse.

1. The Data Warehouse Toolkit - The Complete Guide to Dimensional Modeling, 3rd edition, R. Kimball, M. Ross, John Wiley & Sons, 2013.
2. The Informed Company: How to Build Modern Agile Data Stacks that Drive Winning Insights, D. Fowler, Wiley, 2021.
3. Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema, L. Corr, J. Stagnitto, DecisionOne Press, 2021.
4. Baze podataka, drugo izdanje, R. Manger, Element, Zagreb, 2014.
5. Database System Concepts, 7th Edition, A. Silberschatz, H.F. Korth, S. Sudarshan, McGraw-Hill, NY, 2019.
6. Database Design and Relational Theory - Normal Forms and All That Jazz, 2nd Edition, C.J. Date, Apress, Berkley CA, 2019.

Izborni predmet 1 i 2 - Regular study - Financial and Business Mathematics

Izborni predmet 4, 5, 6 - Regular study - Financial and Business Mathematics

Izborni predmet 4, 5, 6 - Regular study - Financial and Business Mathematics