Gain first-hand knowledge of agribusiness operations using a blend of in-class lectures and site visits.

Topics: Strategic decision-making, valuation methods, long-term sustainability strategies, climate mitigation.

Explore quantitative decision-making models essential for business and resource management decisions.

Topics: Decision, predictive, visual, and prescriptive analytics models.

Explores policy and project evaluation in the global food and agriculture sector using real-world cases.

Topics: Policy Analysis Matrix, Cost-Benefit Analysis, critical evaluation of economic and environmental policies.

Explore futures and options for hedging and speculation through cases in the lumber and foreign exchange markets.

Topics: Real-world cases and and an interactive trading game in options, hedges, opts, calls, and arbitrage.

Explore focus group and survey research design, implementation and data analysis.


Topics: R Studio data analytics, Cluster analysis, Interview techniques, coding of content data.

Learn the critical issues of sustainability, especially in managing “common pool” resources such as capture fishery resources.

Topics: Sustainable economic management models, global capture (wild) fisheries and aquaculture modeling.

Explore the current global landscape of climate emissions regulations and the frameworks.


Topics: Climate change mitigation, methodologies for evaluating the merits and impacts.

Introduces core environmental datasets spanning weather, ecology, and satellite imagery data sources, which are routinely used to support environmental metrics and decision-making in the food and resource sector.


Topics: Practical skills in data extraction, processing, and analysis using R, Python, and JavaScript for Google Earth Engine.

Build expertise in econometric methods such as instrumental variables, panel data, and difference-in-differences.


Topics: Causal relationships such as impact of forest fires on health, income growth on deforestation, national policies on carbon emissions reduction.

Gain hands-on skills in Data Wrangling, SQL, and Python, by applying these tools to real-world finance, climate, and environmental data.


Topics: Build efficient SQL and Python workflows, validate and document your work for handover, and present visual stories that inform decisions for producers, retailers, investors, NGOs, or policymakers.

Introduces modern machine learning methods in R through the lens of applied economics, with a focus on climate, food, environmental, and resource applications. Students learn why and when machine learning can outperform traditional econometric approaches.


Topics: Identify when an applied economics problem could benefit from a machine learning approach (e.g., prediction problems, large amounts of unstructured data, modelling high-dimensional relationships without restrictive functional forms). Trade-offs associated with machine learning approaches versus classic econometric approaches.

Explains the role of governments, business, civil society and international institutions in global food and resource policy-making.


Topics: Global food crises, large-scale land acquisitions, WTO agriculture negotiations, alternatives to current global policies and governance arrangements. Not offered this year as Dr. Margulis is on sabbatical.

Investigate the techniques of time series econometrics models and apply them to policy analysis and forecasting. applications in the business, finance and resource sectors.


Topics: Univariate and multivariate time series models, stationary and non-stationary forecasting models, ARIMA, Vector Auto-regression (VAR), and error correction models.

Explains the role of governments, business, civil society and international institutions in global food and resource policy-making.

Topics: Global food crises, large-scale land acquisitions, WTO agriculture negotiations, alternatives to current global policies and governance arrangements. Not offered 2026: Dr. Margulis on Sabbatical