Integrating Computing into the Statistics Curricula

This project is a continued effort to explicitly address how we can integrate various aspects of computing into the statistics curricula, for both undergraduate and graduate programs. The aim is to facilitate professors/instructors in different universisties and colleges to both

The primary aim is to teach computing as an additional dimension of the statistics program, and not just to leverage it as a medium by which to teach "traditional" statistical topics. Nor are we focusing on using computing within the introductory statistics class within this project.

While the project focuses on teaching computing content, we also feel that this is a suitable vehicle for

We believe this combination of topical problem-based data analysis, new methodology and rich computing and visualization is important for statistical education and practice in this era of multi-disciplinary, data-centric scientific collaboration. We also feel that it will help to attract talented students from different sources than we have previously drawn from and to retain existing students who are interested in ensuring that statistics and data analysis have an impact on science and knowledge discovery.


It is of course obvious that all statistical analyses are done via a computer, and that technology and computing advances dramatically influence statistical practice. Yet very few programs teach students more than elementary utilitarian computing skills.


The tangible outputs from this project include
  • Sample syllabi for different courses. See also links to courses at Links to existing courses.
  • Resources for teaching - lecture notes, exercises/homeworks/projects/case-studies,
  • Workshops
  • Papers to evangelize computing, technologies & data analysis within the community
  • Talks (also to evangelize)
  • Working with instructors in various institutions to introduce and implement model classes,
  • Text book on Data Technologies for Statistics.
  • Acknowledgements

    This project is supported in part by the National Science Foundation grant DUE 0618865 and DMS DMS 0636667.

    Last modified: Sat Mar 7 14:03:50 PDT 2009