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This course serves as an introduction to statistics for the Earth and Environmental Sciences. Topics include an introduction to probability theory, experimental design, statistical hypothesis testing, regression, clustering, Kriging and other forms of spatial analysis, time series analysis, and an introduction to machine learning. All material is covered theoretically and with practical implementation in Matlab. Computing proficiency is required for a passing grade in this course. Writing proficiency is required for a passing grade in this course. A student who does not write with the skill normally required of an upper-division student will not earn a passing grade, no matter how well the student performs in other areas of the course. The course includes two lectures and one computer lab weekly.