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SYST 468 / OR 568: Applied Predictive Analytics

This course introduces the fundamentals of data analysis and some of the most widely used models in applied predictive analytics. The focus includes data understanding, summarization, exploratory analysis and visualization, linear and non-linear predictive models, logistic regression, classification, and clustering among others. Students will be introduced to a powerful open source statistical programming language (R) and work on applied data analysis projects. While no prior knowledge on R is required, students must be well prepared in programming.

OR 531: Analytics and Decision Analysis

This course introduces the fundamentals of prescriptive analytics and some of the most widely used models in predictive analytics. The focus includes mathematical optimization, sensitivity analysis, networks modeling, stochastic modeling, multi-objective modeling, Monte Carlo simulation, and decision analysis using decision trees among others. Students will be introduced to Analytic Solver Platform, Python programming language, and Gurobi and work on various case studies. 

OR 645: Stochastic Processes 

Many real-world processes are fundamentally stochastic and uncertain. This course introduces an in-depth survey of models that can be used to analyze a wide variety of stochastic processes. The focus includes the Poisson process and exponential distribution, renewal theory, discrete- and continuous-time Markov Chains, Queuing theory, and Markovian Decision Processes. Both theoretical analysis and applications of stochastic processes will be presented. This course assumes some prior knowledge of probability and basic stochastic models (like Markov chains).

OR 635: Discrete System Simulation

Simulation is a powerful tool to analyze complex, dynamic and stochastic systems. This course introduces the fundamentals of discrete-event simulation in theory and practice. The focus includes stochastic modeling of discrete-event systems, input modeling, random number generation, statistical analysis of simulation outputs, techniques to improve the efficiency and accuracy of simulation results, and case studies. Students will be introduced to simulation packages such as Arena and AnyLogic. Students have the opportunity to develop simulation models using a general programming language (Java, Python, etc.). Other types of simulation including Monte Carlo, agent-based, and system dynamics will be briefly introduced.

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