Data Management

Course Description

The lifecycle of data from their creation through transmission, processing, storage and use, both in operating systems and in analytical systems. Basic information systems components. Principles of data modeling. Different conceptual data models. Concepts of the relational data model. Normal forms. The process of converting from a conceptual to a relational model. Data Warehouse Architecture. Designing a data warehouse. Dimensional data model and OLAP. Semi-structured data models. Data Formatting for the Semantic Web. NoSQL Database. Big Data Concepts. Data lake. Ways to collect and transform data. Data cleaning procedures. Exploratory data analysis. Data mining. Retrieving data from a relational database and query optimization.

Learning Outcomes

  1. Explain the data life cycle
  2. Design different types of data models with emphasis on the relational and dimensional models
  3. Design the information system architecture
  4. Understand data modelling for the Semantic Web
  5. Prepare data for analysis
  6. Apply basic data analysis methods
  7. Understand Big Data concepts and NoSQL databases

Forms of Teaching

Lectures

3 hours a week

Laboratory

data modeling, retrieving data etc.

Other

homework

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 0 % 25 % 0 % 25 %
Homeworks 0 % 12 % 0 % 12 %
Class participation 0 % 3 % 0 % 3 %
Mid Term Exam: Written 0 % 20 % 0 %
Final Exam: Written 0 % 20 %
Final Exam: Oral 20 %
Exam: Written 0 % 40 %
Exam: Oral 20 %

Week by Week Schedule

  1. Evolution of database systems. Data models. Relational data model.
  2. Principles of data modeling, Conceptual modeling (ER model)
  3. Mapping an ER model to a relational schema
  4. Introduction to PL/SQL
  5. Semi-structured data model (expressed using XML, XML Schema, JSON). Data modeling for Semantic Web (RDF, OWL, SPARQL)
  6. Data warehouse architecture. Data warehouse design
  7. Dimensional model, On-Line Analytical Processing (OLAP). Building a data warehouse
  8. Midterm exam
  9. NoSQL databases, Big Data, data lake
  10. Cassandra - Big Data and distrubuted data processing
  11. Data preparation and exploratory data analysis
  12. Data preparation and exploratory data analysis
  13. Data mining
  14. Retrieving data from a relational database and query optimization
  15. Final exam

Study Programmes

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Theoretical Course (1. semester)

Literature

Z. Skočir, I. Matasić, B. Vrdoljak (2007.), Organizacija obrade podataka, MERKUR A.B.D., Zagreb
Thomas Connolly, Carolyn Begg (2014.), Database Systems – A Practical Approach to Design, Implementation, and Management, Addison‐Wesley
Ralph Kimball, Margy Ross (2013.), The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, John Wiley & Sons
Marko Banek (2013.), Ontologije i Semantički Web (interna skripta),

Associate Lecturers

For students

General

ID 223711
  Winter semester
5 ECTS
L1 English Level
L2 e-Learning
45 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory

Grading System

86 Excellent
74 Very Good
62 Good
50 Sufficient