April 27, 2017
9:00am - 5:00pm
Instructors: Greg Britten, Yara Mohajerani
Data collected in time and/or space exhibit unique properties that require attention to draw proper conclusions from statistical analyses. In this workshop, students are introduced to statistical concepts that are particularly useful for analyzing spatial-temporal data.
Who: This course is targeted primarily at graduate students and researchers who are interested in applied spatial-temporal data analysis
Requirements: Participants must bring a laptop with a few specific software packages installed (see Pre-Workshop Instructions).
Prerequisites: Previous experience with R or Python and knowledge of basic statistical concepts (at least one statistics course or experience statistically analyzing data).
|8:30-9:00||Sign-in (coffee & bagels)
Come early to fix pre-install issues
|9:00-9:30||Jupyter Notebooks, R/Python packages, GitHub repo|
|9:30-11:00||Introduction to Spatial-Temporal Statistics|
|11:00 - 11:15||Break (coffee)|
|11:15-12:30||Applied Time Series Analysis|
|1:00-2:30||Spatial Analysis and Model Selection|
|2:45-4:30||Spatial Interpolation and Regression|
See the course’s GitHub page for instructions: https://github.com/UCIDataScienceInitiative/SpaceTime We’ll expect you to have the Anaconda Python distribution installed with version 2.7 activated: https://www.continuum.io/downloads