Descriptive statistics, estimation, properties of estimators and estimation methods, Confidence intervals, testing of statistical hypothesis, tests about the expectation and proportion of a population, goodness of fit and independence tests, simple linear regression logistic regression. Using data analysis software.
The course is intended for students who wish to acquire practical tools for addressing real-world decision-making challenges. It introduces advanced aspects of modeling and solution techniques from operations research, enabling the solution of deterministic, stochastic, and sequential problems, with the integration of machine learning capabilities where appropriate. Students will work in groups on practical assignments involving “real-life” problems across diverse domains, modeling them, developing solution approaches, and implementing these approaches in code to obtain actual solutions. Emphasis will be placed on creativity in modeling, problem-solving skills, and effective implementation in code.