A Gentle Introduction to Probability
Master fundamental probability concepts through practical applications in science and engineering with this comprehensive course.
Master fundamental probability concepts through practical applications in science and engineering with this comprehensive course.
This foundational course provides a practical introduction to probability concepts with emphasis on real-world applications. Starting with bootcamp lessons in set theory and calculus, students progress through probability axioms, counting rules, conditional probability, and Bayes Rule. The course features engaging applications including poker probability calculations and baseball line-ups, making complex concepts accessible and practical.
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What you'll learn
Master fundamental probability axioms and their applications
Apply permutations and combinations to solve practical problems
Understand and implement conditional probability concepts
Use Bayes Rule for probability updates and analysis
Develop practical modeling skills for real-world scenarios
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 2 modules in this course
This comprehensive course introduces fundamental probability concepts with a focus on practical applications in science and engineering. The curriculum begins with essential bootcamp lessons in set theory and calculus, then progresses through probability axioms, counting rules, independence, conditional probability, and culminates in Bayes Rule applications. Students develop modeling and analysis skills through real-world problems.
Course Introduction + Bootcamps
Module 1
Getting Started with Probability
Module 2
Fee Structure
Instructor
8 Courses
A Distinguished Scholar in Operations Research and Simulation
David Goldsman serves as the Coca-Cola Foundation Professor in the School of Industrial and Systems Engineering at Georgia Tech, where he has established himself as a leading expert in computer simulation and applied statistics since 1984. After completing degrees from Syracuse University (BS/BA in Math/Physics, MS in Math and Computer Science) and Cornell University (MS and PhD in Operations Research), he has made seminal contributions to simulation methodology and applications. His research focuses on computer simulation with emphasis on statistical output analysis, applied probability, ranking and selection, and applications in health systems and airline safety. His excellence has been recognized through numerous awards including the 2023 INFORMS Simulation Society Lifetime Professional Achievement Award, the 2020 INFORMS Fellow Award, and multiple teaching honors including the Alpha Pi Mu Teacher of the Year Award. Beyond his research contributions, which include over 80 journal articles garnering more than 8,800 citations, he has supervised 34 PhD students and served in key leadership roles including Director of Master's Programs at Georgia Tech's ISyE school. His impact extends beyond academia through consulting work in healthcare, airlines, automotive, and banking industries, while maintaining active involvement in professional societies and editorial boards of leading journals in the field.
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