Business Intelligence

Higher education teachers: Zupan Blaž

Subject description

Prerequisits:

• Enrollment in the study year.

Content (Syllabus outline):

What is Business Intelligence? Presentation of the field through a review of typical applications. The role of technologies and approaches to business intelligence in information systems and electronic commerce. Knowledge technologies.Computer-based decision making. Presentation and capture of knowledge. Decision models. Dealing with incomplete and uncertain decision-making data. Interpretation and analysis of the decision.Methods and techniques for computer support for decision making in groups.
Introduction to knowledge discovery in multidimensional data. The role of data warehousing and data preprocessing. Introduction to the techniques machine based of model-making and predictive models. Construction and use of descriptive models of data and an introduction to techniques for clustering. Data visualization and modeling.Techniques of business intelligence on the web. Techniques for making recommendations on the recordings of behavior of target groups. Detecting groups. Techniques for investigation of documents and the automatic classification of documents. Knowledge discovery from textual data. Introduction to the construction, analysis and use of social networks. Semantic networks.Tools and development of business intelligence systems. Integration with information systems. Designing user interfaces for decision support.

Objectives and competences:

The main objective is to understand the methodological bases of intelligent systems and to learn to identify their possible applications in practice. It is also the objective to apply the learned knowledge in practical situations that is in the design of decision models and developing business intelligence systems. During this course students will learn basic methods, techniques and business intelligence tools that we use in modern information systems and online. In particular, we look in detail techniques for decision support and techniques for construction of decision models and data mining.

Intended learning outcomes:

Knowledge of methods, techniques and business intelligence tools.

Learning and teaching methods:

Lectures with the support of audio-visual equipment, lab exercises in a computer lab with basic computer equipment. Working individually and in groups. The strong emphasis on continuous studying and solving practical problems (homework, projects).

Study materials