Johns Hopkins University

Statistics for Civic Life Specialization

Johns Hopkins University

Statistics for Civic Life Specialization

Statistical Reasoning for Data-Driven Decisions.

Build skills in data analysis, inference, probability, and simulation for real-world decisions

Joseph W. Cutrone, PhD
Justine Stauffer

Instructors: Joseph W. Cutrone, PhD

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Get in-depth knowledge of a subject
Beginner level

Recommended experience

4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Beginner level

Recommended experience

4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Analyze and visualize data to identify patterns, relationships, and meaningful differences in real-world contexts.

  • Apply statistical inference to evaluate evidence, estimate population characteristics, and communicate uncertainty.

  • Use probability models, simulation, and random variables to quantify uncertainty and support data-driven decisions.

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Taught in English
Recently updated!

September 2026

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Specialization - 3 course series

Statistics for Civic Life I: Understanding Data

Statistics for Civic Life I: Understanding Data

Course 1, 7 hours

What you'll learn

  • Collect, visualize, and summarize data using appropriate statistical methods.

  • Analyze relationships using correlation and linear regression.

  • Evaluate statistical claims and communicate data-driven conclusions clearly.

Statistics For Civic Life II: Statistical Inference

Statistics For Civic Life II: Statistical Inference

Course 2, 9 hours

What you'll learn

  • Explore data distributions, correlation, and linear regression models using R and StatKey.

  • Construct and interpret confidence intervals and hypothesis tests using traditional, bootstrap, and randomization methods.

Statistics for Civic Life III: Probability & Chance

Statistics for Civic Life III: Probability & Chance

Course 3, 10 hours

What you'll learn

  • Apply probability rules, expected values, and discrete or continuous probability distributions to solve real-world problems.

  • Analyze discrete and continuous probability distributions using expected value, variance, and basic calculus.

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Instructors

Justine Stauffer
Johns Hopkins University
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