Special Section: Teaching Computational Statistics

Statisticianshave always been heavyusers of computingfacilities. In the past these facilities were calculators and the “computers” were those who operated them. Most applied statisticians knew how to use the calculators and even had personal calculators. Students in applied statistics courses were given instruction in the use of calculators, usually in labs associatedwith the courses. When “computers”became machines and use of the computing facilities required software programs, a divergence developed in how the computing facilities were used. A statistician who just wanted to do a regression analysis could Ž nd a program already written that would do the computations for the regression. Students in applied statistics courses were told about these programs—already written by someone else and available for standard statistical analyses. The training required was only marginally greater than that required for use of calculators for simpler tasks. Statisticians who wanted to do something “nonstandard” had a whole new world before them. They could now write instructions for the computer to perform whatever calculations, no matter how intricate, to implement their new analysis method. The training required to do this correctly was of a different order of magnitude. An important question is how to integrate education in computing into the statistics curriculum. The answers to this question obviously must change with technology. They must also account for the differing types of computer usage. Almost all statisticians,whether their academicdegrees are bachelors,masters, or PhDs must use prepackaged statistical analysis software to analyze data. Their statistics training should prepare them for this. Research statisticians who develop new methodology must somehow understand the methods implemented in software. Statisticians who just want to study and compare different statisticalmethodsare often faced with a certain amount of computer programming. These activities require a different level of computer expertise than what is required to do an analysis using a statistics software package. It is helpful to examine how computing is taught in academic statistics programs. The three articles in this special section represent a small (and nonrandom!) sampling of computing curriculum in graduate programs in statistics. Also they re ect the personal perspectives of the authors on the topics that should receive special emphasis. Certainly “one size does not Ž t all”; different programs will have different emphases and should approach the question differently. But the articles should be useful in guiding the curriculum in computing for undergraduate and graduate programs in statistics.