This Specialization covers the use of statistical methods in today's business, industrial, and social environments, including several new methods and applications. H.G. Wells foresaw an era when the understanding of basic statistics would be as important for citizenship as the ability to read and write. Modern Statistics for Data-Driven Decision-Making teaches the basics of working with and interpreting data, skills necessary to succeed in Wells’s “new great complex world” that we now inhabit. In this course, learners will gain an ability to execute basic classification techniques, including the use of R and Python; apply the principles of experimental design; and demonstrate usage of propensity scores, causal inference, and counterfactuals in causal learning.Learn more about the instructors who developed this course. Read the instructor bios and review the learning outcomes for the course.

This module will focus on experiment design, fraction factorial design, and computer experiments. We will review a brief history of experiment design, and relevant terminology. We will review guidelines for conducting and analyzing experiments and applying design to computer models.