Objectives and competences
In this course students:
- Systematically enhance their theoretical and methodological knowledge of data analysis.
- Acquire practical working knowledge by case studies in business, of the selected data analysis techniques, and by application of statistical software.
- Gain the ability to formulate business problems that can be solved by data analysis techniques.
- Understand in-depth data visualization and analytics, and statistical analysis, gain the ability to interpret the data and the results, and prepare the data analysis report, particularly in the business field.
Content (Syllabus outline)
Data analysis process
Quantitative data analysis
Selected data analysis techniques:
- Cluster analysis
- Cohort 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
Knowledge and understanding:
Students:
- Differentiate, select, and apply the data analysis techniques, together with appropriate computer programs, to collect, clean, and analyze the data.
- Detect patterns and trends by data analysis.
- Solve complex statistical dilemmas in data analysis.
- Solve ethical dilemmas, using the proactive approach with others.
Intended learning outcomes - transferable/key skills and other attributes
Cognitive/Intellectual skills:
- Demonstrate the ability to interpret data and results.
- With critical awareness can synthesize information and see the utility potential of data analysis process and techniques.
Key/Transferable skills
- Can work effectively with a group as a leader or member. Can clarify tasks and make appropriate use of the capacities of group members. Can negotiate and handle conflict with confidence.
- They are geared towards solving problems for successful operation in the global environment.
Practical skills:
- Demonstrate initiative and personal responsibility in professional practice.
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 Type (examination, oral, coursework, project):
Active participation at lectures and tutorials 20%
Examination 50%
Project work 30%
