Objectives and competences
In this course, students systematically enhance their theoretical and methodological knowledge of data analysis, acquire practical skills through business case studies, learn to use methods and computer programs for data analysis, and develop the ability to identify and solve business problems with analytical methods. Additionally, they become proficient in data visualization, statistical analysis, and data interpretation, as well as in preparing reports, especially in a business context.
Content (Syllabus outline)
Data analysis process
Quantitative data analysis
Selected data analysis techniques:
- Cluster analysis
- Logistics regression
- Discriminant analysis
- Factor analysis
- Regression analysis
- Time series analysis
- Monte Carlo simulation
In-depth use of SPSS
Data visualization
Data analysis using real life data business in the field of business science
Interpretation of results and writing a research report
Learning and teaching methods
Interactive lectures
Case studies
Labor work
Active individual and group work
Intended learning outcomes - knowledge and understanding
Upon successful completition of this course students:
1. Differentiate, select, and apply data analysis methods and appropriate computer programs for collecting, preparing, and analysing data (PILO 3a).
2. Use advanced analytical techniques to discover patterns and trends in data, contributing to a better understanding of economic and business phenomena (PILO 2c).
3. Solve complex statistical dilemmas using advanced analytical tools and methods, considering the appropriateness and ethical aspects of data usage. (PILO 3a, PILO 4c).
4. Identify and solve ethical dilemmas in data analysis with a proactive approach and collaboration, ensuring integrity and trust in analytical processes. (PILO 4a)
5. Demonstrate the ability to deeply interpret data and results and their use for informed decision-making. (PILO 1a)
6. Critically synthesize information and recognize the practical value of the process and methods of data analysis (PILO 2c).
7. Lead and effectively participate in groups, using various approaches to optimize teamwork, resolve conflicts, and promote innovation. (PILO 3c)
8. Apply knowledge and skills to solve problems in a global context, taking into account cultural differences and global trends. (PILO 3a)
9. Demonstrate initiative and a high level of personal responsibility in carrying out and leading projects, adhering to ethical, professional, and international standards. (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
Upon successful completition of this course students:
1. Differentiate, select, and apply data analysis methods and appropriate computer programs for collecting, preparing, and analysing data (PILO 3a).
2. Use advanced analytical techniques to discover patterns and trends in data, contributing to a better understanding of economic and business phenomena (PILO 2c).
3. Solve complex statistical dilemmas using advanced analytical tools and methods, considering the appropriateness and ethical aspects of data usage. (PILO 3a, PILO 4c).
4. Identify and solve ethical dilemmas in data analysis with a proactive approach and collaboration, ensuring integrity and trust in analytical processes. (PILO 4a)
5. Demonstrate the ability to deeply interpret data and results and their use for informed decision-making. (PILO 1a)
6. Critically synthesize information and recognize the practical value of the process and methods of data analysis (PILO 2c).
7. Lead and effectively participate in groups, using various approaches to optimize teamwork, resolve conflicts, and promote innovation. (PILO 3c)
8. Apply knowledge and skills to solve problems in a global context, taking into account cultural differences and global trends. (PILO 3a)
9. Demonstrate initiative and a high level of personal responsibility in carrying out and leading projects, adhering to ethical, professional, and international standards. (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
Tabachnick, B.G., Fidell, L.S. (2014), Using Multivariate Statistics, 6th Edition, Pearson.
Dodatni viri/Additional Readings
Journal articles in academic journals, focused on empirical research and data analysis.
Tabachnick, B.G., Fidell, L.S. (2019), Using Multivariate Statistics, 7th Edition, Pearson.
Additional information on implementation and assessment • Active participation at lectures and tutorials (20%)
• Written exam (50%)
• Project work (30%)
Active participation at lectures and tutorials - shorter tasks than can be solved within lectures and tutorials and within one week
Written exam - a written exam with tasks covering all major topics. For a positive grade, written exam must be positive. Positive means at least 56 %.
Project work - students create project papers in which they define the economic-business problem, and analyse and solve it with selected quantitative methods.
