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University of Colorado Boulder

Data Acquisition, Risk, and Estimation

University of Colorado Boulder via Coursera

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Overview

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Engineering and Business professionals often have access to many sources of data. The best way to way to ensure your data is both valid and reliable is to plan for it ahead of time. Through this class, you will be able to plan for accurate and precise data generation, then use that data for the purpose of estimation and risk reduction related to capital investments. This specialization can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder.

Syllabus

  • Fundamentals of Sampling
    • Upon completion of this module, students will be able to classify types of sampling used for data acquisition, describe sampling error, and construct random number sequences for sampling.
  • Estimation
    • Upon completion of this module, students will be able to calculate point and interval estimates using RStudio and ROIStat.
  • Best Case / Worst Case Analysis
    • Upon completion of this module, students will be able to use point and interval estimates to determine best- and worst-case scenarios.
  • Foundations of Hypothesis Testing
    • Upon completion of this module, students will be able to plan for data acquisition to minimize risk in decision making, including sample size, effect size and power calculations.
  • Essentials for Effect Size Calculations
    • Upon completion of this module, students will be able to use return on investment calculations to determine effect size, sample size and power using RStudio and ROIStat.

Taught by

Wendy Martin

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