Scientific Research Under Uncertainty: Fundamentals
Learn scientific research methods, uncertainty quantification, and Bayesian analysis for robust investigations.
Learn scientific research methods, uncertainty quantification, and Bayesian analysis for robust investigations.
Dive into the fundamentals of scientific research with a focus on managing uncertainty. This course provides a comprehensive introduction to the research landscape, scientific inquiry, and the application of probability theory in research. You'll explore various research types, understand the scientific method, and learn how to formulate and test hypotheses using Bayesian uncertainty quantification. Designed for beginners, the course emphasizes critical thinking and practical research skills applicable across disciplines. Through five modules, you'll gain a solid foundation in research methodologies, probability concepts, and uncertainty modeling, preparing you to conduct systematic and rigorous scientific investigations.
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What you'll learn
Understand the landscape of scientific research and its importance
Distinguish between scientific and non-scientific inquiries
Master the steps of the scientific method and research process
Apply probability theory concepts to model uncertainty in research
Formulate hypotheses and design research plans using uncertainty quantification
Perform Bayesian hypothesis testing in scientific investigations
Skills you'll gain
This course includes:
3.3 Hours PreRecorded video
12 assignments
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There are 5 modules in this course
This course offers a comprehensive introduction to the fundamentals of scientific research, with a special focus on managing uncertainty. Students will explore the diverse landscape of scientific research, including different types of research, where they are conducted, and their importance. The curriculum covers the principles of scientific inquiry, the scientific method, and the research process. A key component of the course is the introduction to probability theory and Bayesian analysis, providing learners with tools to quantify and manage uncertainty in research. Through five modules, students will learn how to formulate research hypotheses, design scientific investigations, and apply Bayesian hypothesis testing. The course emphasizes critical thinking and practical research skills applicable across various disciplines, making it valuable for beginners in research fields or professionals looking to enhance their research capabilities.
Introduction to the Research Landscape
Module 1 · 3 Hours to complete
Scientific Inquiry
Module 2 · 3 Hours to complete
Scientific Method & the Research Process
Module 3 · 2 Hours to complete
Uncertainty & Probability
Module 4 · 2 Hours to complete
Research as an Exercise in Uncertainty Quantification (UQ)
Module 5 · 2 Hours to complete
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Instructor
Expert in Uncertainty Quantification and Associate Professor at Johns Hopkins University
Michael Shields is an Associate Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, specializing in uncertainty quantification for a variety of challenges in computational mechanics, materials science, and physics. He holds dual bachelor’s degrees in Physics from Loyola University Chicago and Civil Engineering from Columbia University, along with a Master's and PhD in Civil Engineering and Engineering Mechanics from Columbia. With over 15 years of research experience, Shields has made significant contributions to engineering and physical modeling, particularly in uncertainty quantification. His work has been recognized with several early career awards, including the NSF CAREER Award and the Department of Energy Early Career Award. He is also the director of the Center on High-Throughput Materials Discovery for Extremes and actively participates in various professional organizations.
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