CSDS Adds Spatial Perspective to Data Analytics Training

Julia Koschinsky, the Center for Spatial Data Science's Research Director, presented at a new training program on Applied Data Analytics for Public Policy at the University of Maryland to convince participants to think spatially about their research problems. The presentation included examples of how a spatial perspective adds value to traditional non-spatial perspectives of analyzing data, supplemented by how to run spatial queries in PostgreSQL/PostGIS. 

The new program is jointly offered by Julia Lane, Rayid Ghani, Frauke Kreuter at three leading universities; the University of Chicago, New York University and the University of Maryland. It is based on their popular textbook Big Data and Social Science: A Practical Guide to Methods and Tools and features hands-on and collaborative work that addresses how to approach real social policy problems using real world data and modern technology. It also offers a unique opportunity to learn alongside and network with practitioners from other cities and states. Participants are learning how to scrape the web, use APIs, manage complex data, apply machine learning, text and network analysis, as well as think about how to think about inference issues, and privacy and confidentiality. This City & State piece features the program.

For more information about the program and the upcoming third class in fall 2017, go to dataanalytics.umd.edu or email dataanalytics@umd.edu.

Faculty and participants of the second class on welfare recipiency (the first class focused on justice).

(Originally Published on 03-30-2017)