Probability and Uncertainty in Statistics
This course is part of Data Literacy Specialization.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Literacy Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Calculate and interpret probability in various scenarios
Understand random variables and probability distributions
Compute and interpret confidence intervals
Conduct effective hypothesis testing
Evaluate statistical significance in regression analysis
Assess uncertainty in polling and statistical estimates
Skills you'll gain
This course includes:
1.6 Hours PreRecorded video
15 quizzes, 1 peer review
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores probability theory and statistical uncertainty measurement. Students learn key probability concepts, probability distributions, and their application in quantifying uncertainty. The curriculum covers hypothesis testing, confidence intervals, and regression analysis interpretation. Special attention is given to statistical significance testing and its practical applications in data-driven decision making. The course combines theoretical foundations with practical examples to develop strong statistical reasoning skills.
Probability Theory
Module 1 · 2 Hours to complete
Random Variables and Distributions
Module 2 · 2 Hours to complete
Confidence Intervals and Hypothesis Testing
Module 3 · 2 Hours to complete
Quantifying Uncertainty in Regression Analysis and Polling
Module 4 · 2 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Data Literacy Specialization
Instructor
Leading Expert in Data Analytics and Policy at Johns Hopkins University
Dr. Jennifer Bachner is the Director of the Master of Science in Data Analytics and Policy program and the Certificate in Government Analytics program at Johns Hopkins University. With a robust academic background, she earned her Ph.D. in Government from Harvard University and holds undergraduate degrees in political science and social studies education from the University of Maryland, College Park. Dr. Bachner is a prolific author, having co-written significant works such as America’s State Governments: A Critical Look at Disconnected Democracies and What Washington Gets Wrong, alongside Benjamin Ginsberg. Her research focuses on the intersection of analytics, political behavior, and online education, contributing to reports like Predictive Policing: Preventing Crime with Data and Analytics, published by the IBM Center for the Business of Government. As an expert in her field, she has been featured in major media outlets including the Washington Post and NPR. Dr. Bachner's commitment to advancing data-driven policy solutions and her leadership in educational programs underscore her vital role at Johns Hopkins University, where she continues to shape the future of governance and analytics education.
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