Objectives and competences
Develop deep understanding of data visualisation and analytics and data engineering, specifically in the business domain.
Build the ability to use databases effectively achieving cohesion of unstructured massive data for optimal use,
Upgrade analytical modelling skills using complex financial data sets,
Content (Syllabus outline)
The course Artificial Business Intelligence answers the question of how to integrate AI in the business environment, by enhancing the business user's capacity to utilize business intelligence tools.
The course involves reporting personalization, intelligent Key Performance Indicators (KPI) application, integration of production and reporting environments, and business users experience (B-UX) utilization in both real and virtual settings.
Students will learn the concepts and practical applications of integrating AI in Business intelligence by designing adaptive KPIs, interactive dashboards, how to integrate predictions in reporting, and to adapt user environment for improved business user experience and communication. They will employ visualization technologies on desktops, mobile devices, and virtual reality environments.
In the course students will experience a combination of reporting visualization tools for dashboards design, predictive analytic tools, prescriptive alerts, hyper intelligence, and virtual reality tools.
After completing the course, students will be capable of designing a productive business intelligence space and working closely with the software developers which are providing the underlying support.
Learning and teaching methods
- lectures,
- case analysis with computer exercises,
- group project work
Intended learning outcomes - knowledge and understanding
Students will:
1. Develop great depth and systematic understanding of business predictive analytics. (PILO 1a)
2. Ethical issues: will develop the capacity to analyse and manage the implications of ethical dilemmas of smart digitalisation transformation and work proactively with others to formulate solutions. (PILO 4a)
3. Disciplinary methodologies: will gain a comprehensive understanding of techniques/methodologies to conduct business research projects. (PILO 2a)
4. Analysis: with critical awareness, can undertake analysis, managing complexity, incompleteness of data or contradictions in the area of Artificial business intelligence. (PILO 2b)
5. - Application: can act independently and with originality in problem-solving, is able to lead in planning and implementing tasks of artificial business intelligence projects at a professional level. (PILO 3a)
6. Group work: work effectively in a group and lead small groups. Can clarify tasks, manage the capacities of group members, negotiate and handle conflict with confidence. (PILO 3c)
7. Management of information: can undertake innovative research tasks competently and independently. (PILO 2c)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Intended learning outcomes - transferable/key skills and other attributes
Students will:
1. Develop great depth and systematic understanding of business predictive analytics. (PILO 1a)
2. Ethical issues: will develop the capacity to analyse and manage the implications of ethical dilemmas of smart digitalisation transformation and work proactively with others to formulate solutions. (PILO 4a)
3. Disciplinary methodologies: will gain a comprehensive understanding of techniques/methodologies to conduct business research projects. (PILO 2a)
4. Analysis: with critical awareness, can undertake analysis, managing complexity, incompleteness of data or contradictions in the area of Artificial business intelligence. (PILO 2b)
5. - Application: can act independently and with originality in problem-solving, is able to lead in planning and implementing tasks of artificial business intelligence projects at a professional level. (PILO 3a)
6. Group work: work effectively in a group and lead small groups. Can clarify tasks, manage the capacities of group members, negotiate and handle conflict with confidence. (PILO 3c)
7. Management of information: can undertake innovative research tasks competently and independently. (PILO 2c)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Readings
• - Bidgoli, H. (2021). Management information systems. Cengage Learning. ISBN - 978-0-357-41869-7
COBISS.SI-ID - 144315139
Additional
• Felix Weber (2023) Artificial Intelligence for Business Analytics: Algorithms, Platforms and Application Scenarios
• Bill Hibbard (2015) Ethical Artificial Intelligence. Pridobljeno 26 aprila 2023: https://arxiv.org/ftp/arxiv/papers/1411/1411.1373.pdf.
• Igor Perko (2021) Hybrid reality development - can social responsibility concepts provide guidance?. Pridobljeno 26. aprila 2023: https://www.emerald.com/insight/content/doi/10.1108/K-01-2020-0061/full/html.
Additional information on implementation and assessment Written exam or ongoing knowledge checks (33,3%)
Seminar or project (33,3%)
Lab exercises (33,3%)
Written exam or ongoing knowledge checks - a written exam or two midterm tests covering all major topics. Students must achieve 56%.
Seminar or project - group project or individual seminar work.
Lab exercises - A computer exam or two midterm tests covering all major topics. Students must achieve 56%.
