How Science Turns Data Into Knowledge
This course is part of Understanding Data: Stats, Science, and AI Explained.
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 Understanding Data: Navigating Statistics, Science, and AI 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.
Instructors:
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What you'll learn
Understand and interpret statistical significance testing
Evaluate scientific research methods and experimental design
Assess credibility of scientific claims in media
Analyze research limitations and generalizability
Understand peer review and publication process
Identify common biases in science reporting
Skills you'll gain
This course includes:
4.5 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores how scientific research transforms data into knowledge. Students learn about significance testing, experimental design, and the scientific publication process. The curriculum covers hypothesis testing, p-values, research methodology, and science communication. Special attention is given to understanding how scientific findings are reported in media and the challenges of translating complex research for public consumption. The course emphasizes critical evaluation of scientific claims and understanding the role of replication in research.
Welcome, Introduction & Significance
Module 1 · 2 Hours to complete
Experimental Design
Module 2 · 2 Hours to complete
How Science Becomes News
Module 3 · 2 Hours to complete
Science and Society
Module 4 · 3 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: Understanding Data: Stats, Science, and AI Explained
Instructor
Lecturer IV & Research Investigator
Elle O'Brien is a lecturer and research investigator at the University of Michigan. She completed her MS in neuroscience and PhD in hearing science at the University of Washington. Elle spent a year working at an open-source software startup training data professionals to adopt principles from software engineering to make their analyses more reproducible. Now as a researcher and lecturer at the University of Michigan School of Information, Elle designs and teaches graduate courses about statistics and data science. She is also running a research program to study how scientists adopt new software, analysis methods, and technology.
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Frequently asked questions
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