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Data warehouses and reporting services - course description

General information
Course name Data warehouses and reporting services
Course ID 11.3-WE-BizElP-DWandRS-Er
Faculty Faculty of Engineering and Technical Sciences
Field of study e-business
Education profile practical
Level of studies First-cycle Erasmus programme
Beginning semester winter term 2024/2025
Course information
Semester 3
ECTS credits to win 6
Course type obligatory
Teaching language english
Author of syllabus
  • dr hab. inż. Marek Kowal, prof. UZ
Classes forms
The class form Hours per semester (full-time) Hours per week (full-time) Hours per semester (part-time) Hours per week (part-time) Form of assignment
Lecture 15 1 - - Credit with grade
Laboratory 30 2 - - Credit with grade
Project 15 1 - - Credit with grade

Aim of the course

Familiarizing students with the concept of data warehouses and presenting the data lifecycle in a data warehouse. Developing students' skills in designing and implementing data warehouses. Learning methods of creating Business Intelligence type reports using descriptive and exploratory analytics. Developing skills in data visualization for reporting purposes

Prerequisites

Databases

Scope

Data warehouse architectures. Data lifecycle in data warehouses. Relational and dimensional data models. Analytical queries in SQL. Data cubes. Row-based and column-based databases. Descriptive and exploratory analytics. Self-service Business Intelligence. Reporting using Business Intelligence methods. Management dashboards.

Teaching methods

Lecture - conventional lecture using a video projector.
Laboratory - practical exercises in the computer laboratory.
Project - project implementation in a computer laboratory.

 

Learning outcomes and methods of theirs verification

Outcome description Outcome symbols Methods of verification The class form

Assignment conditions

Lecture - the passing criteria is to obtain positive grades from tests carried out at least once in a semester.

Laboratory - the passing criterion is to obtain positive marks for laboratory exercises and tests.

Project - positive assessment of the project or projects realized during the semester

Final mark components = lecture: 30% + teaching laboratory: 40% + project: 30%

Recommended reading

  1. Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (Second Edition), Wiley, 2002
  2. Russo M., Ferrari A. Tabular Modeling in Microsoft SQL Server Analysis Services, Microsoft Press, 2017
  3. Ferrari A., Russo M. Introducing Microsoft Power BI, Mictrosoft, 2016.
  4. Deckler G., Powell B., Mastering Microsoft Power BI - Second Edition: Expert techniques to create interactive insights for effective data analytics and business intelligence, Packt Publlishing, 2022.
  5. Deckler G., Learn Power BI - Second Edition: A comprehensive, step-by-step guide for beginners to learn real-world business intelligence, 2022
  6. Goldwasser M., Malik U., Johnston B., The Applied SQL Data Analytics Workshop: Develop your practical skills and prepare to become a professional data analyst, 2nd Edition. Wydanie II, Packt Publishing, 2020.
  7. SQL Server 2012 Tutorials: Analysis Services - Multidimensional Modeling SQL Server 2012 Books Online, Microsoft, 2012
  8. Sarka D., Lah M. Jerkic, Implementing a Data Warehouse with Microsoft SQL Server 2012, O’Reilly, 2012
  9. Serra J., Anton B., Reporting with Microsoft SQL Server 2012, Packt Publishing, 2014
  10. Inmon W.H.: Building the Data Warehouse, Wiley, 2005
  11. Corr L., Stagnitto J.: Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema, DecisionOne Press, 2011

Further reading

Notes


Modified by dr hab. inż. Marek Kowal, prof. UZ (last modification: 23-04-2024 15:27)