Business Intelligence

Course Description

The goal of the course is to familiarize students with key concepts and issues related to bussiness intelligences and decision support systems. Course focuses on data warehouses, design methods (dimension modelling), data extracting, transforming and loading processes and OLAP systems. Lectures are accompanied with seven homework assignements intended to validate the presented concepts.

General Competencies

Students will be able to design data warehouses and implement business intelligence systems.

Learning Outcomes

  1. define the basic concepts of Business Intelligence and Data WarehousesD
  2. apply the princples of DW modelling
  3. employ the basic ETL procedures
  4. operate the basic OLAP technologies
  5. employ basic BI tools
  6. produce a BI tool prototype

Forms of Teaching

Lectures

Theoretical fundations and paradigms presented during the lectures are illustrated with practical examples and demonstrated using a business intellence platform.

Programming Exercises

Application of knowledge acquired in lectures in the form of the homework.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Homeworks 30 % 40 % 30 % 40 %
Mid Term Exam: Written 0 % 25 % 0 %
Final Exam: Written 30 % 35 %
Exam: Written 0 % 30 %
Exam: Oral 30 %

Week by Week Schedule

  1. Introduction to the course. Introduction to Business Intelligence and Data Warehouse. Definitions of the basic concepts.
  2. Introduction to dimensional modelling. First homework assignment.
  3. Data Warehouse design approaches. Dimensional modelling (conformed dimensions, dimension roles). Second homework assignment.
  4. Dimensional modelling(surrogate keys, indexes, NULL values). Third homework assignment.
  5. Construction of a GUI client for the star join. Fourth homework. Dimensional modelling (dimension types).
  6. Dimensional modelling (heterogeneous dimensions and fact tables, hierarchies)
  7. Fifth homework assignment, dimensional modelling (fact table types, aggregates, drill accross)
  8. Midterm exam
  9. Dimensional modelling (N:N relationships, late/early arriving records, complicated events). Real-time data warehouses.
  10. Real-time data warehouses, OLAP.
  11. OLAP. Sixth homework assignment.
  12. ETL.
  13. ETL. Seventh homework assignement,
  14. Security, metadata, permissions, data quality.
  15. Final exam.

Study Programmes

University graduate
Computer Engineering (profile)
Recommended elective courses (3. semester)
Computer Science (profile)
Recommended elective courses (3. semester)
Information Processing (profile)
Recommended elective courses (3. semester)
Software Engineering and Information Systems (profile)
Specialization Course (3. semester)
Telecommunication and Informatics (profile)
Recommended elective courses (3. semester)

Literature

Ralph Kimball, Margy Ross (2002.), The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, Wiley
Joe Caserta, Ralph Kimball (2004.), The Data Warehouse Etl Toolkit, Wiley
Christopher Adamson (2010.), Star Schema The Complete Reference, McGraw Hill

General

ID 34414
  Winter semester
4 ECTS
L2 English Level
L1 e-Learning
30 Lectures
0 Exercises
0 Laboratory exercises
0 Project laboratory

Grading System

87,5 Excellent
75 Very Good
62,5 Good
50 Acceptable