Master probability concepts and statistical uncertainty measurement for data-driven decision making.
Master probability concepts and statistical uncertainty measurement for data-driven decision making.
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.
4.6
(15 ratings)
2,884 already enrolled
Instructors:
English
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
Instructor
Leader in Data Analytics and Policy at Johns Hopkins University
Dr. Jennifer Bachner is a prominent academic and Director of the Master of Science in Data Analytics and Policy program at Johns Hopkins University. She also oversees the Certificate in Government Analytics program, where she focuses on equipping students with the analytical skills necessary to address complex policy challenges. With a Ph.D. in Government from Harvard University, Dr. Bachner has authored several influential works, including "America’s State Governments: A Critical Look at Disconnected Democracies" and "What Washington Gets Wrong," both co-authored with Benjamin Ginsberg. Her report on predictive policing, published by the IBM Center for the Business of Government, reflects her expertise in using data analytics to inform public policy.In addition to her research and administrative roles, Dr. Bachner is an advocate for online education and has published work on teaching research methods in political science. Her insights have been featured in major media outlets such as the Washington Post and NPR, highlighting her influence in the fields of political behavior and analytics. Through her courses on Coursera, including "Data Literacy Capstone" and "Quantifying Relationships with Regression Models," she aims to empower learners with essential skills for navigating data-driven decision-making in governance.
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4.6 course rating
15 ratings
Frequently asked questions
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