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Business Intelligence and Data Warehouses II

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code460-4098/01
Number of ECTS Credits Allocated3 ECTS credits
Type of Course Unit *Optional
Level of Course Unit *Second Cycle
Year of Study *Second Year
Semester when the Course Unit is deliveredWinter Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
MOY001Ing. René Moyzes
Summary
The subject is a follow up to the Business intelligence and Data Warehouse I subject aimed to extend the knowledge in the domain of analytics over data using querying and specialized BI tools. The content of lectures is a more detailed clarification of DWH principles, specifics of data modelling, design of reporting layers, data integration including transformations and aggregations for presentation of business information hidden within data in a graphical form and layout, or data extracts for further processing. Another part of the subject is the methodology and principles of a solution design of reporting and analytics project. During practical sessions a student is given a chance to apply the knowledge in practical examples of reports and analytical cubes using market-leading BI tools.
Learning Outcomes of the Course Unit
The student is able to orient in the domain of Business Intelligence and Data Warehousing (DWH), in particular in practical knowledge of DWH data modelling methodology, Data Integration into DWH, analytics and presentation of data. Moreover, a student is able to design, create and make use of reporting and presentation layers in a DWH - data marts for analytics and reporting over data, including their graphical presentation using BI tools and web portals. As a supplement to the base content, the student is introduced to new developing trends in the domain of BI and DWH, including areas such as Big Data and analytics over massive quantity of data in real time.
Course Contents
1.-2. Analytics over data - forms of analytics over DataWarehouse (DWH), advanced analytical methods, data mining.
3.-4. Reporting - types of reportingu, dashboards, ad-hoc reporting, presentation and semantic layers, OLAP cubes, parametrization, reporting outputs.
5.-6. OLAP - Dimensions and facts, Hierarchies in dimensional tables, Measures and metrics.
7.-8. Reporting tools
9.-10. Project Management in BI and DWH projects - project roles (Business Analyst, Developer, Tester), development and implementation phases, testing, types of environments, documenting.
11.-12. Process Management - Business definition/request, analysis, functional design, technical specifications, development and testing, deployment to production and handover, daily operation.
13. Latest trends in DWH and BI - Agile BI, Big Data

Computer practices:
1. Summary of business intelligence.
2. Creation of data structures for reporting.
3. Reports Definition
4. Reports Definition - view creation in DWH.
5. Metrics definition
6. Report design - tables - OLAP cubes
7. Report design - tables - drilldown, detailed views
8. Report design - Charts
9. SAP BO / Tableau
10. QlikView
11. IBM Cognos
12. R - Data Mining
13. Final test





Recommended or Required Reading
Required Reading:
1. L. T. Moss, Shaku Atre: Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications. 576p, Addison-Wesley Professional, 2003.
1. D. Slánský, J. Pour, O. Novotný: Business Intelligence: Jak využít bohatství ve vašich datech. Grada, 256s, 2004.

Recommended Reading:
1. R. Kimball, M. Ross: The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. 600p, Wiley, 2013.
2. R. Kimball, J. Caserta: The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. 528p, Wiley, 2004.
3. C. Batini, M. Scannapieco: Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications). Springer, 2010.
1. R. Kimball, M. Ross: The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. 600p, Wiley, 2013.
2. R. Kimball, J. Caserta: The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. 528p, Wiley, 2004.
3. C. Batini, M. Scannapieco: Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications). Springer, 2010.
Planned learning activities and teaching methods
Lectures, Tutorials
Assesment methods and criteria
Task TitleTask TypeMaximum Number of Points
(Act. for Subtasks)
Minimum Number of Points for Task Passing
Graded creditGraded credit100 51