Objectives and competences
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.
Content (Syllabus outline)
• 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 and teaching methods
• lectures,
• computer lab work.
Intended learning outcomes - knowledge and understanding
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.
Intended learning outcomes - transferable/key skills and other attributes
• Communication skills: oral laboratory work defense, manner of expression at oral examination.
• Use of information technology: use of software tools for cleaning and transformation of data and for
• business intelligence
• Problem solving: managing of dirty data of various types, using business intelligence with machine learning models.
Readings
• J. E. Olsen: Data Quality: The Accuracy Dimension, Morgan Kaufmann Publishers, New York, 2003.
• D. Pyle: Data preparation for Data Mining, Morgan Kaufmann, San Francisco, 1999
• L. P. English: Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits, John Wiley & Sons, New York, 1999.
• D. Loshin: Enterprise Knowledge Management: The Data Quality Approach, Morgan Kaufmann Publishers, New York, 2001.
• Moss T. L. and Atre S.: Busienss Inteligence RoadMap: The Complete project Lifecycle for Decision-Support Application, Addison-Wesley Information Technologies Series, May, 2003.
• Volitich D.: Cognos B, Business Intelligence: The Official Guide, McGraw-Hill, 2008.
• Yao, M., Zhou, A. and Jia, M., Applied artificial intelligence: A handbook for business leaders, Topbots Inc., 2018.
• Shmueli, G., Bruce, P.C., Yahav, I., Patel, N.R. and Lichtendahl Jr, K.C., Data mining for business analytics:
• concepts, techniques, and applications in R. John Wiley & Sons, 2017.
Additional information on implementation and assessment Each individual component has to be positively finished. The exam can be replaced by written midterm examinations in the weight of 50%.
