PV274 Data Quality Management Seminar

Faculty of Informatics
Autumn 2020
Extent and Intensity
0/2/0. 1 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. Mouzhi Ge, Ph.D. (lecturer)
Guaranteed by
doc. Mouzhi Ge, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics
Timetable
Tue 3. 11. 14:00–17:50 A220, Tue 10. 11. 14:00–17:50 A220, Tue 24. 11. 14:00–18:50 A220
Prerequisites
Database Design and Data Modelling
Basic statistics or software experience like using SAS, R, SPSS is preferred
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 12 student(s).
Current registration and enrolment status: enrolled: 1/12, only registered: 0/12, only registered with preference (fields directly associated with the programme): 0/12
fields of study / plans the course is directly associated with
Course objectives
(This course requires very interactive discussion, it is designed for the second-year or final-year students) This course is designed to let students learn practical and scientific knowledge of data quality management. The main objective is to exploit the techniques used in data quality management and data integration. The course will also provide theoretical knowledge of data quality management and the real-world system implementation guidelines to students such as Talend DI and DQ. The students will learn and discuss the applications according to the case studies in ETL with Talend software.
Learning outcomes
After completing the course, a student will be able to:
- understand the classic research methods in the data quality management;
- apply the data quality management solutions in practice;
- understand the data quality dimensions and their measurement;
- analyze current scientific knowledge in the field of data quality management;
- conduct the data quality measurement;
- design real-world data quality management scenarios;
- identify and describe data models;
- apply management principles to big data;
- understand the data integration and ETL concepts ;
- implement the real-world data quality measurement solution;
- understand the theoretical knowledge of data quality management;
Syllabus
  • Data quality management
  • Data quality dimensions
  • Big Data quality
  • Master Data Management
  • Talend Software: DI and DQ
  • Data quality assessment
  • ETL and Data Integration
  • Data quality costs
  • Data cleansing
  • Data quality management strategy
  • Data analytics
  • A/B test in practice
  • Delone and Mclean IS model
  • Optimizing Information Value
  • Information Lifecycle Concepts
  • Data modelling in different domains
  • Data quality and smart city
Literature
  • BATINI, Carlo and Monica SCANNAPIECA. Data quality : concepts, methodologies and techniques. Berlin: Springer, 2006, xix, 262. ISBN 3540331727. info
  • Data quality. Edited by Richard Y. Wang - Mostapha Ziad - Yang W. Lee. Boston, Mass.: Kluwer Academic, 2002, xv, 167 p. ISBN 0792372158. info
Teaching methods
paper reading, interactive discussion and student presentation
Assessment methods
the students need to perform a written exam. The writen exam consists of 7 questions, in which 6 questions are with corresponding answers and 1 question is open. In the open question, the student can design and write down their own idea, thinking, and argument about this question.

Question 1 (5%)
Question 2 (5%)
Question 3 (10%)
Question 4 (10%)
Question 5 (20%)
Question 6 (20%)
Question 7 (open question, 30%)

Answer's correctness that is more than 60% will be considered as a pass to the exam.
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
https://www.muni.cz/en/people/239833-mouzhi-ge/cv
The course is also listed under the following terms Autumn 2019.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2020/PV274