This course was developed with financial support from the National Institute of Transportation and Communities under grant number 854.
Transportation -- Research -- Data processing, Transportation -- Research -- Information technology, Transportation -- Study and teaching (Higher), Transportation -- Planning -- Data processing
Building on the successful scientific computing training program offered by the Software Carpentry (http://www.software-carpentry.org/), this course exposes students to the best practices in data science through hands-on lab sessions. Using transportation data and examples, it also aims to help students tackle the challenge of “drinking from a hose” when dealing with the overwhelming amount of data that is increasingly common in transportation research and practice.
Although computing is now an integral part of every aspect of science and engineering, transportation research included, most students of science, engineering, and planning are never taught how to build, use, validate, and share software well. As a result, many spend hours or days doing things badly that could be done well in just a few minutes.
The goal of this course is to help students gain the necessary skills to spend less time wrestling with software and more time doing useful research.
Wang, Liming. Introduction to Data Science for Transportation Researchers, Planners, and Engineers. NITC-ED-854. Portland, OR: Transportation Research and Education Center (TREC), 2018.