# Data over Space and Time (36-467/667)

## Fall 2018

Cosma Shalizi

Tuesdays and Thursdays, 10:30--11:50, Posner Main Auditorium
This course is an introduction to the opportunities and challenges of
analyzing data from processes unfolding over space and time. It will cover
basic descriptive statistics for spatial and temporal patterns; linear methods
for interpolating, extrapolating, and smoothing spatio-temporal data; basic
nonlinear modeling; and statistical inference with dependent observations.
Class work will combine practical exercises in R, some mathematics of the
underlying theory, and case studies analyzing real problems from various fields
(economics, history, meteorology, ecology, etc.). Depending on available time
and class interest, additional topics may include: statistics of Markov and
hidden-Markov (state-space) models; statistics of point processes; simulation
and simulation-based inference; agent-based modeling; dynamical systems theory.

Course description and logistics are tentative and may change!
**Co-requisite**: For undergraduates taking the course as
36-467, 36-401. For
graduate students taking the course as 36-667, consent of the professor.

**Note:** Graduate students *must* register for the
course as 36-667; if the system does let you sign up for 36-467, you
will be dropped from the roster.

This webpage will serve as the class syllabus (once it's fleshed out).
Course materials (notes, homework assignments, etc.) will be posted here, as
available.