Objectives and competences
Students gain a strong foundation in the areas that support analytics and are able to use theoretical knowledge in data analytics practices executed in the business world. and recognise and understand professional ethics in data science.
Content (Syllabus outline)
1. Introduction to data-driven decision-making process
2. Data-driven decision-making style
3. Data strategy and data governance
4. Data culture
5. Data products
6. Data sources and analysis
7. Data visualization basics, data presentation
8. Data infrastructure
9. Data ethics and culture
10. Decision making with AI
Learning and teaching methods
lectures; case studies; discussion; individual study
Intended learning outcomes - knowledge and understanding
Upon successful completion of the course, the student will be able to:
1. CILO 1. define the basic concepts of data-driven decision-making process and their place in management decisions (PILO 2a);
2. CILO 2. understand the data-driven decision-making styles, strategy and governance (PILO 2c);
3. CILO 3. interpret management decision-making situations related to corporate data (PILO 1a);
4. CILO 4. raising students’ awareness towards ethical and cultural approaches in data science (PILO 4c);
5. CILO 5. make meaningful proposals to solve challenges in data-driven decision-making process by data product canvas and data visualization (PILO 3c);
6. CILO 6. raising students’ data visualization basics and new tools and opportunities in data visualization (PILO 3c).
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
Upon successful completion of the course, the student will be able to:
1. CILO 1. define the basic concepts of data-driven decision-making process and their place in management decisions (PILO 2a);
2. CILO 2. understand the data-driven decision-making styles, strategy and governance (PILO 2c);
3. CILO 3. interpret management decision-making situations related to corporate data (PILO 1a);
4. CILO 4. raising students’ awareness towards ethical and cultural approaches in data science (PILO 4c);
5. CILO 5. make meaningful proposals to solve challenges in data-driven decision-making process by data product canvas and data visualization (PILO 3c);
6. CILO 6. raising students’ data visualization basics and new tools and opportunities in data visualization (PILO 3c).
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
Bang, C. G. (2024): Data-Driven Decision-Making for Business. Taylor & Francis
Dodatna literature / Additional:
Nabor aktualnih člankov s področja podatkovne znanosti./Selection of scientific papers / publications in the area of data science.
Additional information on implementation and assessment Midterm exam 30 %
Seminar paper 70 %
