Objectives and competences
Students are able to distinguish and analyse the application areas of artificial intelligence in business information solutions. They are able to apply appropriate AI tools and platforms.
Content (Syllabus outline)
This Artificial Intelligence in Business Information Solutions course focuses on using artificial intelligence, including advanced predictive modelling, machine learning, AI and other data science tools to automate and optimize business functions (business processes) supported by advanced business information solutions (i.e., modern ERP solutions). Students will be able to apply their skills in data visualization, data mining tools, predictive modelling, and advanced optimization techniques to address business function challenges. Effective data engineering is essential in building an analytics-driven competitive advantage in the market. Modern data engineering platforms reduce manual data preparation by automating processes, enabling companies to focus on deriving efficiencies in data processing to develop impactful business insights. This course provides students with a thorough understanding of the fundamentals of data engineering platforms for both operational and analytical use cases while gaining hands-on expertise in building these platforms in a way to develop analytical solutions effectively. The course will address technological ecosystems for big data analysis.
Learning and teaching methods
- lectures
- guided classes in computer room
- case analysis
Intended learning outcomes - knowledge and understanding
Students in this course:
1. review the concepts of business informatics and business information systems
2. systematically build on their knowledge of business information solutions and artificial intelligence concepts in this field (PILO 2a, PILO 3b)
3. be trained to apply theoretical knowledge in robotic process automation (PILO 2a, PILO 3a)
4. be trained to apply theoretical knowledge in data mining in the context of business information solutions (PILO 2a, PILO 3a)
5. be able to select artificial intelligence tools in business information solutions environments (PILO 2a, PILO 3a)
6. gain practical experience of the use of artificial intelligence tools and platforms in a business information solutions environment (PILO 3b, PILO 3c)
7. be aware of the ethical and sustainability aspects of the use of artificial intelligence in business information solutions (PILO 4a, PILO 4c)
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 in this course:
1. review the concepts of business informatics and business information systems
2. systematically build on their knowledge of business information solutions and artificial intelligence concepts in this field (PILO 2a, PILO 3b)
3. be trained to apply theoretical knowledge in robotic process automation (PILO 2a, PILO 3a)
4. be trained to apply theoretical knowledge in data mining in the context of business information solutions (PILO 2a, PILO 3a)
5. be able to select artificial intelligence tools in business information solutions environments (PILO 2a, PILO 3a)
6. gain practical experience of the use of artificial intelligence tools and platforms in a business information solutions environment (PILO 3b, PILO 3c)
7. be aware of the ethical and sustainability aspects of the use of artificial intelligence in business information solutions (PILO 4a, PILO 4c)
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
STERNAD ZABUKOVŠEK, Simona, TOMINC, Polona, BOBEK, Samo. Business informatics principles. V: PÁSZTO, Vít (ur.), et al. Spationomy : spatial exploration of economic data and methods of interdisciplinary analytics. Cham: Springer. cop. 2020, str. 93-118. https://doi.org/10.1007/978-3-030-26626-4_4, https://link.springer.com/content/pdf/10.1007%2F978-3-030-26626-4_4.pdf
Singh, S. Enterprise Resource Plannig. Lovelely Professional University, Punjab (India). https://ebooks.lpude.in/computer_application/bca/term_5/DCAP302_DCAP514_ENTERPRISE_RESOURCE_PLANNING.pdf
Additional information on implementation and assessment Final exam (40%)
Exam in the computer lab (40%)
Homework/project (20%)
Final exam - written exam with at least one question from each section within the course.
Exam in the computer lab - practical exam with at least one task from each set of computer tools/solutions included in the course.
Homework/project - ongoing assignment with at least one assignment from each section of the course.
