Data, Environment and Society (ENERES 131)
Professor Duncan Callaway
Lecture (#33105) TT 9:30 – 11am
Labs (#33106) M 10am – 12pm or (#33276) W 10am – 12pm
This course will teach students to build, estimate and interpret models that describe phenomena in the broad area of energy and environmental decision-making. Students leave the course as both critical consumers and responsible producers of data-driven analysis.
The effort will be divided between (i) learning a suite of data-driven modeling and prediction tools (including linear model selection methods, classification and regression trees and support vector machines) (ii) building the programming and computing expertise to use those tools and (iii) developing the ability to formulate and answer resource allocation questions within energy and environment contexts.
We will work in Python in this course, and students must have taken Data 8 before enrolling. The course is designed to complement and reinforce Berkeley’s “data science” curriculum.
Required Prerequisites: Foundations of Data Science (Computer Science C8/Information Systems C8/ Statistics C8) and college calculus.
Recommended Preparation: An introductory computer programming course (Computer Science 61A or Computer Science 88) and Linear Algebra (Mathematics 54, Electrical Engineering 16A, or Statistics 89A).