PV274 Data Quality Management Seminar

Fakulta informatiky
podzim 2020
Rozsah
0/2/0. 1 kr. (plus ukončení). Ukončení: zk.
Vyučující
doc. Mouzhi Ge, Ph.D. (přednášející)
Garance
doc. Mouzhi Ge, Ph.D.
Katedra počítačových systémů a komunikací - Fakulta informatiky
Dodavatelské pracoviště: Katedra počítačových systémů a komunikací - Fakulta informatiky
Předpoklady
Database Design and Data Modelling
Basic statistics or software experience like using SAS, R, SPSS is preferred
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Předmět si smí zapsat nejvýše 12 stud.
Momentální stav registrace a zápisu: zapsáno: 0/12, pouze zareg.: 0/12, pouze zareg. s předností (mateřské obory): 0/12
Mateřské obory/plány
předmět má 32 mateřských oborů, zobrazit
Cíle předmětu
(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.
Výstupy z učení
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;
Osnova
  • 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
Literatura
  • BATINI, Carlo a 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
Výukové metody
paper reading, interactive discussion and student presentation
Metody hodnocení
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.
Vyučovací jazyk
Angličtina
Informace učitele
https://www.muni.cz/en/people/239833-mouzhi-ge/cv
Další komentáře
Předmět je vyučován každoročně.
Výuka probíhá blokově.
Předmět je zařazen také v obdobích podzim 2019.