This workshop will cover basic concepts and tools available in QGIS and R for visualizing spatial data. We will cover vector data but will also touch upon the visualization of raster and spatial network data. Participants should have some familiarity with R, but exposure to QGIS is not required.

In the R by Example series of workshops, we’ll discuss example analyses in R as a vehicle for learning  commonly used tools and programming patterns. The “Functional Programming with dplyr” workshop will initially focus on analyzing winter home temperatures in the US using data from the Residential Energy Consumption Survey (https://www.eia.gov/consumption/residential/).  We’ll use the data.table package for […]

Survival analysis is used when working with data that may be censored, as often is the case in studies of human subjects with incomplete follow-up.  The presence of censoring makes most forms of regression and other standard statistical analyses inappropriate. A body of specialized techniques for analyzing this type of data has been developed, including […]

Prerequisites: Participant should have some familiarity with introductory statistics and be able to load data into and perform basic data manipulations in SAS. In this one-day, six-hour workshop we will discuss the basics of using SAS for statistical inference and modeling. The workshop is held in a computer lab and will alternate between instructor presentations […]

Regular expressions are perfectly suited for people who like puzzles. Regular expressions are a sequence of characters used to define a search pattern. They are commonly used to do “find” and “find and replace” string operations. They are also used to validate strings like phone numbers, passwords, etc. in data entry. Regular expression capabilities can […]

This is a two-session workshop on the use of Go for data processing.  Go is an open source language developed for general-purpose programming.  It is not more difficult to learn and use than a high-level scripting language like Python, but it is strongly typed, statically compiled, and provides native support for concurrency, leading to much […]

In this workshop, we’ll first discuss core machine learning concepts such as: choosing loss functions and evaluation metrics; splitting the data into training, validation, and testing sets; and cross-validation patterns for tuning hyper-parameters. Next, we’ll apply these concepts to train models for identifying isolated letters from speech (https://archive.ics.uci.edu/ml/datasets/isolet). Specifically, we’ll apply the elastic net (a […]