PA220 Database systems for data analytics

Faculty of Informatics
Autumn 2023
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
doc. RNDr. Vlastislav Dohnal, Ph.D. (lecturer)
Mgr. 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
there are 6 fields of study the course is directly associated with, display
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. xxxiv, 564. ISBN 9781118530801. 2013. info
  • INMON, William H. Building the data warehouse. 4th ed. Indianapolis: Wiley Publishing. xxviii, 54. ISBN 0764599445. 2005. 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. v, 408 p. ISBN 9781849683456. 2011. 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.
The course is also listed under the following terms Autumn 2020, Autumn 2021, Autumn 2022.

PA220 Database systems for data analytics

Faculty of Informatics
Autumn 2022
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
doc. RNDr. Vlastislav Dohnal, Ph.D. (lecturer)
Mgr. 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
Tue 16:00–17:50 D1
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
there are 6 fields of study the course is directly associated with, display
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. xxxiv, 564. ISBN 9781118530801. 2013. info
  • INMON, William H. Building the data warehouse. 4th ed. Indianapolis: Wiley Publishing. xxviii, 54. ISBN 0764599445. 2005. 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. v, 408 p. ISBN 9781849683456. 2011. 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.
The course is also listed under the following terms Autumn 2020, Autumn 2021, Autumn 2023.

PA220 Database systems for data analytics

Faculty of Informatics
Autumn 2021
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
doc. RNDr. Vlastislav Dohnal, Ph.D. (lecturer)
Mgr. Miriama Jánošová (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
Thu 16. 9. to Thu 9. 12. Thu 10:00–11:50 D2
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
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 (Apache Hive); analytical databases (Apache Impala).
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. xxxiv, 564. ISBN 9781118530801. 2013. info
  • INMON, William H. Building the data warehouse. 4th ed. Indianapolis: Wiley Publishing. xxviii, 54. ISBN 0764599445. 2005. info
    not specified
  • 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. v, 408 p. ISBN 9781849683456. 2011. info
Teaching methods
Lectures.
Assessment methods
written exam -- includes both test questions (choice of options) and free-hand answers.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2020, Autumn 2022, Autumn 2023.

PA220 Database systems for data analytics

Faculty of Informatics
Autumn 2020
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught online.
Teacher(s)
doc. RNDr. Vlastislav Dohnal, Ph.D. (lecturer)
Mgr. Miriama Jánošová (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
Tue 12:00–13:50 A320
Prerequisites
Knowledge of relational database systems, query and transaction processing and principles of query optimization, preferably in the scope of PA152 course.
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
there are 29 fields of study the course is directly associated with, display
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 (Apache Hive); analytical databases (Apache Impala).
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. xxxiv, 564. ISBN 9781118530801. 2013. info
  • INMON, William H. Building the data warehouse. 4th ed. Indianapolis: Wiley Publishing. xxviii, 54. ISBN 0764599445. 2005. info
    not specified
  • 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. v, 408 p. ISBN 9781849683456. 2011. info
Teaching methods
Lectures.
Assessment methods
written exam -- includes both test questions (choice of options) and free-hand answers.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2021, Autumn 2022, Autumn 2023.

PA220 Database systems for data analytics

Faculty of Informatics
Autumn 2019

The course is not taught in Autumn 2019

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)
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
Prerequisites
Knowledge of relational database systems, query and transaction processing and principles of query optimization, preferably in the scope of PA152 course.
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
there are 29 fields of study the course is directly associated with, display
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 (Apache Hive); analytical databases (Apache Impala).
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. xxxiv, 564. ISBN 9781118530801. 2013. info
  • INMON, William H. Building the data warehouse. 4th ed. Indianapolis: Wiley Publishing. xxviii, 54. ISBN 0764599445. 2005. info
    not specified
  • 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. v, 408 p. ISBN 9781849683456. 2011. info
Teaching methods
Lectures.
Assessment methods
written exam -- includes both test questions (choice of options) and free-hand answers.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (recent)