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
- define the basic concepts of Business Intelligence and Data WarehousesD
- apply the princples of DW modelling
- employ the basic ETL procedures
- operate the basic OLAP technologies
- employ basic BI tools
- 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 ExercisesApplication 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
- Introduction to the course. Introduction to Business Intelligence and Data Warehouse. Definitions of the basic concepts.
- Introduction to dimensional modelling. First homework assignment.
- Data Warehouse design approaches. Dimensional modelling (conformed dimensions, dimension roles). Second homework assignment.
- Dimensional modelling(surrogate keys, indexes, NULL values). Third homework assignment.
- Construction of a GUI client for the star join. Fourth homework. Dimensional modelling (dimension types).
- Dimensional modelling (heterogeneous dimensions and fact tables, hierarchies)
- Fifth homework assignment, dimensional modelling (fact table types, aggregates, drill accross)
- Midterm exam
- Dimensional modelling (N:N relationships, late/early arriving records, complicated events). Real-time data warehouses.
- Real-time data warehouses, OLAP.
- OLAP. Sixth homework assignment.
- ETL.
- ETL. Seventh homework assignement,
- Security, metadata, permissions, data quality.
- 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
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