• Course code:63536A
  • Credits:6
  • Semester: summer
  • Contents

Each year the lecturer is a visiting professor from other universities.

The course will be held  by: Ivan Luković

The course is intended for established visiting researchers and lecturers and for experts in computer and information science which will introduce students to topics that are interesting due to recent theoretical findings and methodological breakthroughs or for their applicative value, and are as such not included into the existing curriculum.

The specific contents of the course are determined yearly.

 

The course will include selected advanced topics in:

  • Business Intelligence (BI) and Data Warehouse (DW) Systems in organization and business
  • Characteristics, tasks, architectures, and application domains of BI and DW systems
  • Strategic planning and development process of DW and BI systems
  • Requirement specification of a DW & BI project work
  • Database schema design for DW systems
  • Physical database structures and operational performances of DW systems
  • Extraction, Transforming and Loading (ETL) process in DW systems
  • Aggregated data in DW systems
  • Decision Support Systems (DSS), Data Analytics, Business Reporting, and application domains for BI and DW systems
  • On-Line Analytical Processing (OLAP) – Concepts, Architectures and SQL capabilities
  • Big Data and Data Analytics Thinking
  • Data Science, Data Engineering and Data-Driven Decision Making
  • Data Mining process and techniques for BI and Data Science
  • Data Science and Business Strategy
  • Design specification of a DW & BI project work

Objectives and competences:

The goal of the course is to introduce basic theoretical ideas as well as practical implementations of new methods and technologies in the field of computer and information science.

 

Intended learning outcomes:

Knowledge and understanding: A broader overview and understanding of the field of study, and of up to date methods and concepts.

Application: Applying current approaches and techniques from the specific field of computer and information science.

Reflection: Understanding the advantages of the chosen approaches in computer and information science in solving specific practical tasks.

Transferable skills: Solving complex problems, designing complex systems.

 

Learning and teaching methods:

Lectures, lab exercises

 

Assessment:

Type (examination, oral, coursework, project):

  • Continuing (homework, midterm exams, project work)
  • Final (written and oral exam)

Grading: 6-10 pass, 1-5 fail.

  • Study programmes
  • Distribution of hours per semester
45
hours
lectures
30
hours
tutorials