PHSCS 383
Download as PDF
Physical Reasoning with Data
Physics and Astronomy
College of Computational, Mathematical, & Physical Sciences
Course Description
An experiential learning physics-based approach to data science that includes designing physical experiments, collecting data, and investigating uncertainties, causality and correlation in datasets while developing cross-discipline technical communication skills.
When Taught
Winter
Fixed/Max
3
Fixed
3
Title
Information Physics
Learning Outcome
Understand the concepts of information, entropy, probability, and surprise and their use in data science, machine learning, and the modeling of physical systems
Title
Design and Perform Physical Experiments
Learning Outcome
Plan physical experiments and observations; decide what data are needed, determine the sampling parameters; and evaluate limitations in the dataset
Title
Physical Interpretation of Data
Learning Outcome
Recognize erroneous data; detect biases and outliers; apply scaling and nondimensionalization to obtain insights into the data
Title
Causal Relationships in Data
Learning Outcome
Comprehend the difference between correlation and causality and their roles in data science and data-limited information
Title
Scientific Communication and Collaboration
Learning Outcome
Practice communicating across disciplines; participate in active learning classroom, including small group discussion; complete final group project and presentation
