The world is full of possibilities, but how likely are any of them to occur? Immerse yourself in probability theory and discover how to evaluate the likelihood of different outcomes in a variety of real-life circumstances. You will get started by examining the different methods for describing and representing data, including the basic axioms that form the core of probability theory. You will then explore real-world applications and discover how engineers and scientists use random variables and associated analytical functions to quantify the likelihood of various occurrences in experiments and processes.

You will gain experience using discrete random variables, or when the number of possible outcomes can be counted, as well as continuous random variables, or when the number of possible outcomes is infinite. You will also explore data transformation and several kinds of probability distributions, including ones that assess the joint probability of two or more related variables. Toward the end of the program, you will apply what you have studied and investigate ways of modeling and predicting variability and patterns in data through the use of “ordered collections” and graphical representations, including normal, lognormal, Gumbel, and Weibull distributions.

Note: You will need a working knowledge of algebra and univariate calculus prior to enrolling and will need to get access to the course's textbook, the ninth edition of “Probability and Statistics for Engineering and the Sciences” by Jay Devore. We suggest you obtain the book prior to the start of the course and consider the interactive “eTextbook” version, available for purchase online or from other retailers.

 

How It Works

Course Length
15 weeks

Effort
6 to 8 hours of study per week

Format
100% online, self-paced
  • Engineers (mechanical, electrical, software, civil, systems, chemical, biomedical)
  • Quality control professionals
  • Materials analysts
  • Product developers
  • Data scientists and analysts
  • Aspiring data scientists, product developers, and analysts who seek a foundation in probability theory
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