At its August 1992 meeting in Boston, the Committee on Applied and Theoretical Statistics (CATS) noted widespread sentiment in the statistical community that upper-level undergraduate and graduate curricula for statistics majors and postdoctoral training for statisticians are currently structured in ways that do not provide sufficient exposure to modern statistical analysis, computational and graphical tools, communication skills, and the ever-growing interdisciplinary uses of statistics. Approaches and materials once considered standard are being rethought. The growth that statistics has undergone is often not reflected in the education that future statistician receive. There is a need to incorporate more meaningfully into the curriculum the computational and graphical tools that are today so important to many professional statisticians. There is a need for improved training of statistics students in written and oral communication skills, which are crucial for effective interaction with scientists and policy makers. More realistic experience is needed in various application areas for which statistics is now a key to further progress. In response to this sentiment, CATS initiated a project on modern interdisciplinary university statistics education. With support from the National Science Foundation, CATS organized and held a one-and-one-half-day symposium on that topic in conjunction with the August 1993 San Francisco Joint Statistical Meetings. The symposium's focus was what changes in statistics education are needed to (1) incorporate interdisciplinary training into the upper-undergraduate, graduate, and postdoctoral statistics programs, (2) bring the upper-undergraduate and graduate statistics curricula up to date, and (3) improve apprenticing of statistics graduate and postdoctoral students and appropriately reward faculty mentors. These proceedings have been compiled to capture the timely and important presentations and discussions that took place at that symposium. It should be noted that the opinions expressed in this volume are those of the speakers of discussants and do not necessarily represent the views of CATS or of the National Research Council.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education