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

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

University graduate
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Communication and Space Technologies (profile)
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Computational Modelling in Engineering (profile)
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Computer Engineering (profile)
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Computer Science (profile)
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Control Systems and Robotics (profile)
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Data Science (profile)
Elective Courses (1. semester)
Electrical Power Engineering (profile)
Elective Courses (1. semester)
Electric Machines, Drives and Automation (profile)
Elective Courses (1. semester)
Electronic and Computer Engineering (profile)
Elective Courses (1. semester)
Electronics (profile)
Elective Courses (1. semester)
Information and Communication Engineering (profile)
Elective Courses (1. semester) Elective Courses of the Profile (1. semester)
Software Engineering and Information Systems (profile)
Elective Courses (1. semester)
Telecommunication and Informatics (profile)
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
L3 English Level
L2 e-Learning
45 Lectures
15 Laboratory exercises

Grading System

Excellent
Very Good
Good
Acceptable