FI:PA220 DB systems for data analytics - Course Information
PA220 Database systems for data analytics
Faculty of InformaticsAutumn 2023
- Extent and Intensity
- 2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- doc. RNDr. Vlastislav Dohnal, Ph.D. (lecturer)
RNDr. Miriama Jánošová (assistant)
Mgr. David Procházka (assistant) - Guaranteed by
- doc. RNDr. Vlastislav Dohnal, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Wed 10:00–11:50 D3
- Prerequisites
- Knowledge of relational database systems, query and transaction processing and principles of indexing, preferably in the scope of PB154 or PB168 courses or their equivalents from other universities.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Bioinformatics and systems biology (programme FI, N-UIZD)
- Discrete algorithms and models (programme FI, N-TEI)
- Design and development of software systems (programme FI, N-SWE)
- Machine learning and artificial intelligence (programme FI, N-UIZD)
- Processing and analysis of large-scale data (programme FI, N-UIZD)
- Natural language processing (programme FI, N-UIZD)
- Course objectives
- To get acquainted with the possibilities of database systems and their use for data analytics: design and implementation of data warehouses; query languages and tools for integration with external computing and analytics platforms; analytical databases.
- Learning outcomes
- Student will be able to: - understand the principles of data warehouses; - describe typical examples of data warehouse use-cases; - design a data warehouse; - create a solution to analytical tasks.
- Syllabus
- Introduction to data warehouses and business intelligence.
- Data modeling for data warehouses: dimensions, facts.
- Data warehouse lifecycle.
- Data warehouse creation processes: ETL.
- Data warehouse applications: sales, CRM.
- Analytical databases.
- Languages for analytical tasks.
- Big data analytics.
- Literature
- recommended literature
- KIMBALL, Ralph and Margy ROSS. Data warehouse toolkit : the definitive guide to dimensional modeling. Third edition. Indianapolis: John Wiley & Sons, 2013, xxxiv, 564. ISBN 9781118530801. info
- INMON, William H. Building the data warehouse. 4th ed. Indianapolis: Wiley Publishing, 2005, xxviii, 54. ISBN 0764599445. info
- not specified
- Apache Impala
- Apache Hive
- Microsoft Corporation. Implementing a Data Warehouse with Microsoft SQL Server 2012. 2012. info
- Oracle Warehouse Builder 11gR2getting started : extract, transform, and load data to build a dynamic, operational data warehouse. Edited by Bob Griesemer. Olton, Birmingham: Packt Pub., 2011, v, 408 p. ISBN 9781849683456. info
- Teaching methods
- Lectures.
- Assessment methods
- written exam -- includes both test questions (choice of options) and free-hand answers.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually.
- Enrolment Statistics (Autumn 2023, recent)
- Permalink: https://is.muni.cz/course/fi/autumn2023/PA220