SIME5000

This course provides a broad overview of analytics with a focus on using quantitative tools to better manage systems. The first third of the course focuses on developing introductory and basic python programming skills to read, store and graphically represent data. The topics covered include: arrays, loops, data structures and visualizing data. The second third of the class focuses on statistical analysis of data. The topics covered include: f and t hypothesis testing, confidence intervals, regression and clustering. The final third of the course presents how to use computers to generate improved or optimal solutions. The topics covered include neighborhood search, hill climbing or simulated annealing heuristic, linear programming and integer programming. The homeworks, projects and tests will draw data from a wide range of systems where analytic tools will identify better solutions for improved systems management.

Prerequisites: 'C' or better in one of (ME EN1010/CS1400/CS1420/CH EN1703/MSE2001)& one of (ME EN2550/MATH3070/BME3070/CH EN2550/CS3130/ECE3530/MET E3070/OSC2030)& (Major status in the College of Engineering OR College of Mines OR College of Science)

Fall