Course details

Data Warehousing

DS_2 Acad. year 2022/2023 Winter semester 5 credits

Current academic year

Basic terms: relational and multidimensional database model, operational database, data warehouse, data mart, building and running of a data warehouse, modelling a data warehouse - multidimensional model, data cube, OLAP analysis, data mining.

Guarantor

Bartík Vladimír, Ing., Ph.D. (DIFS)

Language of instruction

Czech

Completion

Credit+Examination

Time span

  • 13 hrs lectures
  • 26 hrs exercises

Assessment points

  • 60 pts final exam (written part)
  • 20 pts mid-term test (written part)
  • 20 pts seminars in computer labs

Department

Department of Intelligent Systems (UITS)

Lecturer

Bartík Vladimír, Ing., Ph.D. (DIFS)

Instructor

Bartík Vladimír, Ing., Ph.D. (DIFS)

Subject specific learning outcomes and competences

Students understand the importance of data warehouses, OLAP techniques and data mining and are able to use it.

Learning objectives

Understanding the basic theory of data warehouses and acquirement of practical skills in their design and usage, understanding the basics of OLAP techniques and data mining.

Prerequisite knowledge and skills

Database systems

Fundamental literature

  • Ponniah, P.: Data Warehousing Fundamentals. John Wiley & Sons, Inc., 2001, 516 s. ISBN 0-471-41254-6

  • Humphries, M., Hawkins, M. W. a kol.: Data warehousing, Principy a praxe. Computer Press, 2002, 256 s. ISBN 8072265601.

Syllabus of lectures

1. Basic terms of data warehousing. Relationship of data warehouses and operational databases.
2. Architecture of a data warehouse. Modelling of data warehouses.
3. ETL process during creating data warehouse.
4. OLAP tachnics and OLAP operations over a data cube.
5. A case study about using of data warehouses.
6. Introduction to the design of data warehouses.
7. Introduction to data mining in data warehouses.

Syllabus of numerical exercises

1. Repetition of relational databases and SQL.
2. Data warehousing and OLAP usage analysis in Microsoft tools.
3. Use MS Excel for OLAP analysis.
4. Individual project to create a data warehouse.
5. Use of methods data mining tools in an environment of Microsoft.
6. Individual project on data mining.

Progress assessment

During the semester, students can obtain up to 40 points, including 20 points from the mid-term test consisting of questions and examples of the themes of the first half of the semester and 20 points from the projects to exercise (one to create a data warehouse and OLAP usage techniques and the other on data mining). On the credit you need to get 20 points. Final written exam for 60 points includes all topics covered in lectures and in seminars.

Teaching methods and criteria

The course contains lectures that explain basic principles, problems and methodology of the discipline, and exercises that promote the practical knowledge of the subject presented in the lectures.

Controlled instruction

Design and presentation of projects in due dates. Students can work on the projects in their own free time.

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