This paper reports on our experience teaching introductory programming by means of real-world data analysis. We have found that students can be motivated to learn programming and computer science concepts in order to analyze DNA, predict the outcome of elections, detect fraudulent data, suggest friends in a social network, determine the authorship of documents, and more. The approach is more than just a collection of "nifty assignments"; rather, it affects the choice of topics and pedagogy.

Abstract—This paper presents the iRobot corporation’s Roomba vacuum as a low-cost resource for robotics research and education. Sensor and actuation models for unmodified Roombas are presented in the context of both special- and general-purpose spatial-reasoning algorithms, including Monte Carlo Localization and FastSLAM. Further tests probe the feasibility of sensor extensions to the platform. Results demonstrate that, with some caveats, the Roomba is a viable foundation for both classroom and laboratory use, especially for work seeking to leverage robots to other ends, as well as robotics per se with a computational focus. I.