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 this workshop we will use Make to manage build dependency in a multi-file, multi-language software project.  We will learn how to use Make functions, automatically generate dependencies, and inquire the operating system about available packages and libraries.  Also, we will briefly review alternative build dependency managers. At the end of the workshop you will […]

Multilevel modeling is the state-of-the-art approach for handling data with complex dependence structure in a regression analysis.  This workshop will discuss fitting multilevel models in Python using the Statsmodels package. We will discuss the motivation and main use cases for multilevel modeling, and illustrate by example how to fit linear and generalized linear mixed models. […]

GEE is an extension of the generalized linear modeling (GLM) framework for dependent data.  GEE can be used with longitudinal data, clustered data, and other forms of dependent data where a GLM may not be appropriate.  In this workshop we will discuss fitting models using GEE in Python with the Statsmodels package. We will briefly […]

In this workshop we will discuss the concept of dependency management, with the primary focus on build dependencies between software components.  We will learn how to express the dependencies and how to automate the software building process using Make utility and language.  We will go through hands-on exercises and will see how expressing the dependencies […]

This workshop will cover fitting generalized linear models (GLMs) in Python, using the Statsmodels package.  We will cover logistic regression, but the Poisson, negative binomial, and gamma regression. We will provide an overview of the underlying foundation for GLMs, focusing on the mean/variance relationship and the link function.  Participants should have familiarity with linear regression […]

This workshop will cover regression analysis using linear models and least squares in Python.  We will discuss the goals and main use-cases for linear regression, and how to interpret a fitted linear model.  We will then discuss methods for fitting more complex models with larger data sets, including the use of interactions, dummy-coding of categorical […]

This workshop will introduce the Julia programming language, with a focus on using Julia for data analysis.  No prior exposure to Julia is needed. We will discuss some aspects of the core language, cover some basic techniques for data manipulation, and fit a model using linear regression.