Gordon Institute Open School

A workflow for turning a research question, evidence and analysis into a clear and defensible article or report.

A practical introduction to organizing data, code, assumptions and outputs so that analytical work can be checked and repeated.

A practical framework for checking percentages, benchmarks and empirical claims before accepting their interpretation.

A structured method for reading research papers efficiently without confusing technical detail with the central contribution.

A practical course in evaluating claims, using evidence and constructing analytical arguments. Evidence Writing Critical Thinking Assessment Course facts

An intuitive introduction to strategic interaction, uncertainty about others and reasoning in multi-agent environments.

A business-track course in structured reasoning, evidence, assumptions and concise decision analysis.

Short-form quantitative reasoning for technical admissions, analytical work and disciplined problem solving.

A rigorous introduction to the mathematical structures underlying statistical learning, prediction and modern machine-learning models.

A technical introduction to how large language models represent text, predict tokens and generate useful but imperfect outputs.

A mathematical introduction to learning from data, designed for learners who want to understand models rather than only use software.

An introduction to models that learn representations of environments, dynamics and consequences beyond next-token prediction.

A first look at the statistical ideas that appear after introductory inference and regression.

A practical course in reasoning under uncertainty, from conditional probability to estimation and evidence.

A compact quantitative refresher for learners preparing for graduate study in economics, finance, data science and artificial intelligence.

An applied introduction to vectors, matrices, projection and optimization for modern quantitative modelling.

A rigorous introduction to the mathematical structures underlying statistical learning, prediction and modern machine-learning models.