FI:PV274 Data Quality Management - Course Information
PV274 Data Quality Management Seminar
Faculty of InformaticsAutumn 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
- there are 32 fields of study the course is directly associated with, display
- 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
- 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
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/autumn2020/PV274