Machine Learning with Caret
Join Max Kuhn on a tour through Machine Learning in R. You'll learn about data preparation, model fitting, model assessment and predictions. Prior experience with lm is enough to get started and learn advanced modeling techniques.
Geospatial Statistics and Mapping in R
Geospatial expert and Columbia Professor Kaz Sakamoto is leading this class on all things GIS. You'll learn how about map projections, spatial regression, plotting interactive heatmaps with leaflet and working with shapefiles.
Git for Data Science
Daniel Chen, author of Pandas for Everyone, has given multiple talks at the New York R Conference about the data science workflow. In this workshop he'll teach how to use Git and project management for better organization and faster iteration.
Let's start over and learn Git from the beginning with the goal to use it for tracking collaborative work. But Git can be used for more than tracking code and data science projects. For example, if you're a student you can have a place to store your class notes and materials. Let's learn Git so you can be less afraid, and see how it can integrate into your life.
While Git is mainly thought of as a collaboration tool. There's a lot you can do with Git on your own without collaborating with other people. Many of the Git work flows (e.g., Git-flow) can be done on solo projects too. Thus, we'll focus on the skills of using Git on your own, with remotes (e.g., GitHub), and branches. In essence, you will be "collaborating with yourself", before we go through the process of collaborating with other people.
Let's move beyond "memorizing .. shell command and type[ing] them to sync up... [and when errors occur], sav[ing] your work elsewhere, delet[ing] the project, and download[ing] a fresh copy". https://xkcd.com/1597/
Introduction to Survival Analysis
Time-to-event outcomes are common in a variety of statistical applications, but the statistical techniques needed to appropriately analyze data in the presence of censoring or when predictor variables are not observed at baseline are not always taught as part of a standard statistics curriculum. This workshop will introduce the statistical techniques needed to address common questions in the context of time-to-event outcomes. Topics covered will include types of censoring, the Kaplan-Meier estimator of the survival function, Cox proportional hazards regression, analysis of time-dependent covariates, and competing risks methods to handle situations where more than one type of event is possible. All common statistical analyses will be demonstrated in R, including use of the survival and ggsurvplot packages.