Description

The objective of this course is to qualify students for carrying out the requirements collection, analyzing and design of data model of high quality, analyzing and managing of dirty data, as well as understand and use the principles of correct data visualization. Students should be able to apply the business intelligence in complex information systems, and apply machine learning in business intelligence.

Requirements

None.

Instructors

izr. prof. dr. MUHAMED TURKANOVIĆ

izr. prof. dr. SAŠO KARAKATIČ

Contents

• Database models' quality, database schema quality, data definition quality. 
• Data quality assessments. 
• Data quality improvement processes. 
• Quality policy. • Business rules. 
• Data preparation. 
• Managing of non-numerical and missing data. 
• Data-driven companies. 
• Data-driven processes. 
• Examples of using machine learning in business intelligence processes. 
• Implementation of machine learning in business processes. 
• Artificial intelligence in business intelligence. 
• Business intelligence, performance and efficiency management. 
• Data warehouses, dimensional model, ETL and OLAP cube.

Learning Outcomes

On completion of this course the student will be able to 

• analyze and evaluate the quality of data and data sources,design appropriate processes for improving the quality of data and data sources, 
• evaluate the success of the quality improvement process, 
• identify appropriate techniques for addressing the low quality of data and data sources, 
• use techniques to deal with the low quality of data and data sources, 
• identify the benefits and possibilities of using business intelligence, 
• design a dimensional model, 
• execute ETL process, 
• prepare a data warehouse, 
• design a comprehensive business intelligence solution, 
• design the application of machine learning models in the business intelligence analysis, 
• identify the possibilities of implementing the machine learning models in the business processes, 
• evaluate the usefulness and the quality of the machine learning models in companies and its processes.

Planned Activities

• lectures, 
• computer lab work.

Assessment Methods and Criteria

• Written examination: 50%
• Laboratory work: 50%