Teaching
Osman Gulseven teaching, applied econometrics, international trade, agricultural finance, TINA, R, Python, Google Colab, Quarto
Teaching is organized around applied skills, transparent reasoning, reproducible analysis, and policy communication. Course details remain high-level unless a term, syllabus, or public teaching link has been verified.
Teaching areas
Applied Econometrics
Econometric modeling, panel data, time-series analysis, research methods, and reproducible research using R, Python, Google Colab, and Quarto.
International Agricultural Trade
International trade, WTO, gravity models, regional integration, food security, non-tariff measures, TINA simulations, trade datasets, policy briefs, and PPML.
Agricultural Finance
Agricultural finance, insurance, risk, sustainable development, household finance, case studies, and applied quantitative examples.
Agricultural Entrepreneurship
Entrepreneurship, agricultural economics, student projects, policy communication, case studies, policy briefs, and data visualization.
Data Visualization
Economic communication, reproducible research, public data, Python, R, Google Colab, Quarto, and chart examples for applied economics.
Quantitative Methods
Quantitative research design, statistics, empirical methods, data-driven policy analysis, R, Python, and Google Colab.
Public-materials policy
- Do not post exams, grades, private student records, or restricted institutional materials.
- Use public or teaching-approved datasets only.
- Label planned materials as
[coming soon]until reviewed. - Keep notebooks, policy briefs, and visualization examples reproducible and text-first where possible.