Objectives and competences
This course aims to provide students with a more in-depth understanding of the impacts of computer science on business performance and to train them to work with comprehensive information solutions and their use of artificial intelligence. The course is focused on the information renewal of business processes (content and methodological aspects) and on the paradigm and models of business information solutions by presenting the most commonly used modern business information solutions. Modern business information solutions include multiple elements of artificial intelligence in various areas, including advanced predictive modelling, machine learning, and other data science tools. In the practical component of the course, the focus is on a more detailed analysis of the use of artificial intelligence in modern business information solutions, both for operational and analytical use cases and subsequently in the redesign of business processes.
Transferable/Key Skills and other attributes:
ability to apply knowledge in practice; ability to generate new ideas; ability to work in interdisciplinary teams; ability to use business information solutions (Microsoft Dynamics, SAP); effective use of databases; deep understanding and use of big data within business information solutions; in-depth understanding of data visualization and analytics within business information solutions; knowledge of the latest AI trends integrated into business information solutions; discovering patterns and trends through data analysis to create business strategies; selecting appropriate business information solutions for key business decisions; communication, organizational, and professional skills, as well as problem-solving orientation for successful operation in a global environment.
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, case analysis, discussion
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
Students:
Knowledge base: has depth and systematic understanding of knowledge in business information solutions and can work with theoretical / research-based knowledge at the forefront of their academic discipline
Ethical issues: has the awareness and ability to manage the implications of ethical dilemmas and work pro-actively with others to formulate solutions
Disciplinary methodologies: Knows and understands a wide range of methods/techniques applicable to AI in business information solutions.
Intended learning outcomes - transferable/key skills and other attributes
Cognitive/Intellectual skills:
Analysis: with critical awareness, can undertake analysis of complex, incomplete or contradictory areas of knowledge, communicating the business information solutions effectively
Synthesis: with critical awareness, can synthesize information in a manner that may be innovative, utilizing knowledge or processes from the forefront of business information solutions
Evaluation: has a level of conceptual understanding of the knowledge of conceiving and using AI in business information solutions that will allow her/him critically to evaluate research, advanced scholarship and methodologies and argue alternative approaches to business information solutions
Application: can demonstrate initiative and originality in problem-solving. Can act autonomously in planning and implementing business information solutions at a professional or equivalent level, making decisions in complex and unpredictable situations in the area of business information solutions
Key/Transferable skills
Group working: can work effectively with a group as a leader or member. Can clarify tasks and make appropriate use of the capacities of group members. Is able to negotiate and handle conflict with confidence
Learning resources: is able to use a full range of learning resources.
Self-evaluation: is reflective on own and others' functioning in order to improve practice
Management of information: can competently undertake research tasks with minimum guidance
Autonomy: is an independent and self-critical learner, guiding the learning of others and managing his/her own requirements for continuing professional development.
Communications: can engage confidently in academic and professional communication with others, reporting on action clearly, autonomously and competently
Problem-solving: has independent learning ability required for continuing professional study, making professional use of others where appropriate
Practical skills:
Application of skills: can operate in complex, unpredictable, and/or specialized contexts and has an overview of the issues governing good practice.
Autonomy in skill use: is able to exercise initiative and personal responsibility in professional practice
Technical expertise: has the technical expertise, performs smoothly with precision and effectiveness; can adapt skills and design or develop new skills and/or procedures for new situations.
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 • written exam
• seminar work
• practial work
