Teaching

In Fall 2019, I developed and taught a new course in Dimension Reduction for the Department of Statistics at Harvard University intended for upper level undergraduates.  Since then, the focus has shifted to more general methods for unsupervised learning.

Overtime, I have begun collecting the course notes and numerical demos into an e-book. A working draft may be found here.

• Special thanks to Paul Bendich, Duke University, for his insight on content and course goals on earliest development of this course.

In thise class we cover point processes, areal data, and geostatistics balancing theory (and assumptions), techniques for exploratory analysis, and inference and prediction.  Techniques for Bayesian methods with MCMC are covered at the end of the course.

• Point processes 

  • Uses sf for visualization; spatstat for EDA and inference

• Areal data

  • Uses sp, sfdep for spatial weights and EDA; spautolm for regression

• Geostatistics

  • Uses sf for visualization, gstats for EDA and kriging

Data sources include spData and NYCOpenData.