Statistical Inference for Estimation in Data Science

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Course overview

Provider
Coursera
Course type
Free online course
Level
Intermediate
Deadline
Flexible
Duration
26 hours
Certificate
Paid Certificate Available
Course author
Jem Corcoran
  • Identify characteristics of “good” estimators and be able to compare competing estimators.

  • Construct sound estimators using the techniques of maximum likelihood and method of moments estimation.

  • Construct and interpret confidence intervals for one and two population means, one and two population proportions, and a population variance.

Description

This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators, method of moments estimation, maximum likelihood estimation, and methods of constructing confidence intervals that will extend to more general settings. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.

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