Relational databases and other collections of data are increasingly prevalent across a wide range of professions and disciplines. However, most courses on databases and data mining are designed for computer science majors, and students who wish to familiarize themselves with these topics must first take several prerequisite courses. This paper presents the design and implementation of a course for non-majors that provides a data-centric introduction to computer science. The course covers the basics of databases and data mining, and it also offers an introduction to programming and data visualization. By teaching a variety of computational methods for processing collections of data, the course provides students with tools they can use in their fields of study and future professions, while also exposing them to key concepts from computer science. The course has been well received by students from a variety of majors, and survey results suggest that it has had a positive impact on their perceptions of computer science.

Manually tuning the parameters or "knobs" of a complex software system is an extremely difficult task. Ideally, the process of software tuning should be automated, allowing software systems to reconfigure themselves as needed in response to changing conditions. We present a methodology that uses a probabilistic, graphical model known as an influence diagram as the foundation of an effective, automated approach to software tuning. We have used our methodology to simultaneously tune four knobs from the Berkeley DB embedded database system, and our results show that an influence diagram can effectively generalize from training data for this domain.