School of Economics and Administrative Sciences \ Economics
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
This course offers an introduction to modem tools and artificial inıeIligence (Al) applications used in economic ana|ysis. lts primary objective is to equip students with knowledge and skilIs not only in spreadsheet software (such as LibreOtİce Calc, MS Excel) and widely used econometric analysis software (such as Gretl, Stata), but also in interacting with Al-based tools
and large language models (LLM9. Throughout the course, students will learn how to download, clean, and format data from various sources such as TÜİK (Turkish Statistical lnstitue), EVDS (Electronic Data Delivery System), and the World Bank; practice data visualization, basic statistical analysis, and economic applications; and deveIop effective AI prompting skills to integrate AI-supported methods into their analytical workflows.
Textbook and / or References
Senol Aldıbas, 2012, Libre Office Calc, TÜBiTAK
http://www.pardus.org.tr/wp-content/uploads/2016/09/LibreOffice-Hesap-TablosuCalc.pdf
Halil Özmen, 2016, R Programlama (Özet Ders Notları)
http://halilozmen.com/dersler.php
Libre Office – Calc
https://tr.libreoffice.org/
Gretl (Gnu Regression, Econometrics and Time Series Library)
http://gretl.sourceforge.net/
R
https://www.r-project.org/
The aim of this course is to equip students with comprehensive knowledge and practical skills in using modern software and artificial intelligence-based methods for economic data analysis. Students will learn to effectively utilize spreadsheet and econometric analysis tools, while also developing competencies in acquiring, cleaning, and analyzing data from reliable sources such as TURKSTAT, EVDS, and the World Bank. The course enables students to explore data visualization, basic statistical analysis, and AI-assisted modeling techniques. Furthermore, through interactions with large language models and effective prompting practices, students will gain a strong understanding of contemporary analytical methods at the intersection of data science and economics.
1. Makes economic analyses
2. Uses modern data sources effectively.
3. Cleans and analyses data.
4. Works with spreadsheet and econometric tools.
5. Uses artificial intelligence based tools.
6. Directs artificial intelligence tools (prompting)
7. Develops analyses supported by artificial intelligence.
Week 1: Introduction to data analysis and visualisation tools, basic features of user interfaces
Week 2: Cell, table and data formatting, basic data structuring techniques
Week 3: Basic analysis commands, functions and calculations
Week 4: Loading data sets, establishing connection with external data sources
Week 5: Basic statistical calculations and visualisation methods
Week 6: Downloading and cleaning data from economic data sources (TurkStat, EVDS, World Bank etc.)
Week 7: Basic econometric applications for data analysis
Week 8: Short summary and review of programming languages
Week 9: Introduction to artificial intelligence based tools and models
Week 10: Effective AI prompting and basic AI integration
Week 11: Artificial intelligence-supported economic analysis and sample applications
Week 12: Artificial intelligence-supported economic analysis and sample applications
Tentative Assesment Methods
• Project 40%
• Final 40%
• Homework 20%
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