Lecture
1.The role of data in the digital economy. Discussion on the importance of data analysis in decision-making, digitalization processes, and the automation of work performed by economists, analysts, and managers.
2.Integration of traditional and modern IT tools in data analysis. Overview of the evolution of analytical tools: from Excel, through the R environment for statistical analysis, to generative language models.
Classes
- Excel in Data Analysis
3.Introduction to organizing economic data and performing calculations in Excel using formulas, relative and absolute referencing.
4.Creating charts for data visualization and formatting spreadsheet cells.
5.Importing and exporting data, organizing worksheets, and preparing clear summaries for further analysis or presentation.
6.Calculating basic descriptive statistics and preparing data for analysis.
7.Using advanced Excel functions.
8.In-class test 1
- Artificial Intelligence in IT tools for Data Analysis
9.The functioning of large language models (LLMs), the process of response generation, and their limitations.
10.Crafting effective prompts tailored to user needs and context.
11.Practical applications of AI: integrating tools, improving queries, and automating tasks.
12.Assessing the quality of AI-generated responses, identifying errors, and avoiding misinformation.
13.Ethical and responsible use of AI in education, work, and everyday life, with attention to ethical principles and data privacy.
14.In-class test 2
Estimated student workload: 2ECTS x 25h = 50h
(K) - contact hours (S) - hours of independent work
lectures (classes): 4h (K) 0h (S)
exercises (classes): 24h (K) 0h (S)
consultations: 2h (K) 0h (S)
preparation for exercises: 0h (K) 10h (S)
work with additional materials: 0h (K) 8h (S)
preparation for in-class tests: 0h (K) 2h (S)
Total: 30h (K) + 20h (S) = 50h