(in Polish) Narzędzia informatyczne w ekonomii 2400-ZL1NIE
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
Type of course
Course coordinators
Learning outcomes
Knowledge
Upon completion of the course, the student:
•Has knowledge of the operating principles of IT tools used in economics, particularly spreadsheet software, statistical analysis environments, and generative artificial intelligence. Understands their limitations and potential applications in economic data analysis.
•Understands the fundamentals of descriptive statistics and the basics of modeling economic phenomena, enabling effective use of Excel and IT tools, including AI, in quantitative analyses.
•Possesses knowledge necessary for the conscious, responsible, and ethical use of IT tools in analytical work.
•Is prepared to further develop knowledge and skills in the use of modern digital tools, responding to the evolving needs of organizations and the labor market (digitalization, AI development, automation).
Skills
Upon completion of the course, the student:
•Is able to efficiently use Excel for organizing data, performing calculations, and visualizing economic information.
•Is able to analyze statistical data using Excel functions, including calculating basic descriptive statistics and creating charts and data summaries.
•Is capable of integrating traditional and modern tools for data analysis, modeling economic phenomena, and preparing datasets for further analytical work.
•Is able to use artificial intelligence tools consciously—formulate effective prompts, verify AI-generated responses, and recognize limitations and potential errors of AI systems.
•Is able to apply ethical principles when working with IT tools and AI, taking into account user responsibility and data protection.
Social Competences
Upon completion of the course, the student:
•Is prepared for continuous development of digital competences, including seeking new sources of knowledge and improving the ability to use IT tools in an evolving work environment.
•Understands the need to update knowledge in response to dynamic technological changes, including the development of artificial intelligence and the automation of analytical processes.
•Is able to collaborate with professionals from other fields (e.g. IT, economics, data analysis, data science), using digital tools as a shared platform for problem-solving.
•Demonstrates responsibility in the ethical and conscious use of AI and IT tools, taking into account issues of privacy, transparency, and the impact of technology on economic and social decision-making.
Learning outcomes (codes): K_W02, K_U02, K_K01
Assessment criteria
To pass the course, the student must:
1. Attend classes (two absences are allowed),
2. Pass the classes based on the criteria specified by the instructor.
The basis for passing the classes includes two in-class tests and active participation during classes:
- the criteria for evaluating active participation are specified by the instructor,
- a minimum of 51% of the total points from both in-class tests is required to pass.
Bibliography
Zhou H., Eksploracja danych za pomocą Excela. Metody uczenia maszynowego krok po kroku, Helion 2024
Gutman A. J., Goldmeier J., Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym, Helion 2023
Mount G., Zaawansowana analiza danych. Jak przejść z arkuszy Excela do Pythona i R, Helion 2022
Zumel N., Mount J., Język R i analiza danych w praktyce, Helion 2021
Wenger K., Czy algorytm spiskuje przeciwko nam? Co każdy powinien wiedzieć o koncepcjach i pułapkach sztucznej inteligencji, Helion 2025
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: