The goal of the course is to equip students with the understanding of basic concepts and tools of business intelligence, and to apply them in data analysis and informed business decision-making.
Additional info
Introduction to Business Intelligence. Definition and importance of business intelligence. Key concepts and components of BI systems.
BI System Architecture. Data Warehousing. ETL processes (Extract, Transform, Load).
Tools and Technologies in BI. Overview of BI tools (e.g., Power BI, Tableau, QlikView). The role of OLAP (Online Analytical Processing).
Data Collection and Processing. Data sources and collection methods. Data preparation and cleansing.
Data Visualisation. Fundamentals of report design and visualisation. Practical examples of data visualisation.
Fundamentals of Data Analysis. Statistical methods and analytical tools. Application of data analysis in business.
Implementation of BI Solutions. Planning and management of BI projects. The CRISP-DM methodology.
Applications of BI in Industry. Case studies from various industries. Examples of successful BI system implementation.
Ethics and Data Security in BI. Ethical issues and privacy protection. Data security and regulatory compliance.
The Future of Business Intelligence. New trends and technologies in BI. The role of artificial intelligence and machine learning in BI.
There are no listed course associates.
Lectures: 30
Seminars: 0
Exercises: 0
Students will be able to define the key concepts and components of Business Intelligence (BI) systems, and to use BI tools for data collection, analysis, and visualisation.
Students will acquire skills in analysing business problems using BI methods and tools, and in making informed decisions based on analytical data.
Students will be able to apply the acquired knowledge to practical projects, developing BI solutions that support strategic and operational decision-making within organisations.
