Apache Spark is a powerful open source processing engine built around speed, ease of use, and sophisticated analytics. Industry has quickly adopted Spark and deployed it at scale for processing big data. Its main advantage include in-memory processing and a rich set of operations for wrangling data using DataFrames. In this workshop, we’ll introduce attendees […]

Part of the Structural Equation Modeling (SEM) series.  This workshop will help participants develop skills in understanding and conducting latent variable models, particularly from the perspective of structural equation modeling. After a conceptual overview, a broad view of matrix factorization techniques will be provided along with specific examples (e.g. PCA, ‘factor analysis’).  In addition, measurement […]

Deep Neural Networks (DNNs) are used as a machine learning method for both regression and classification problems. Keras is a high-level, Python interface running on top of multiple neural network libraries, including the popular library TensorFlow. In this workshop, participants will learn how to quickly use the Keras interface to perform nonlinear regression and classification with standard fully-connected DNNs, as well as image classification […]

Nonlinear relationships abound in nature, though typical statistical models ignore this in favor of simplicity, often at a cost of both predictive capabilities and better understanding of the underlying phenomenon of interest.  One means to explore such relationships is through generalized additive models (GAM). This workshop will introduce participants to GAMs as a means to […]

Learn data analysis with Python. We’ll be using pandas, the go-to Python library used for data wrangling and analysis. We’ll be practicing with several different real-world datasets (e.g. time-series, text) and learning how to read, write, clean, transform, merge and reshape data. The workshop is intended for users with basic Python knowledge. Anaconda Python 3.6 […]

Mixed models are an extremely useful modeling tool for situations in which there is some dependency among observations in the data, where the correlation typically arises from the observations being clustered in some way. For example, it is quite common to have data in which we have repeated measurements for the units of observation, or […]

This is a four-part workshop introducing programming concepts to those with little-to-no programming experience. The four 2-hour sessions will take place over two weeks, with Python being taught in the first three sessions, and Matlab in the fourth session. Computers are provided. Session 1: Feb 6, 11am - 1pm - Location: MLB 2001a Session 2: […]