Contents. PART I: COMPUTING: AN OVERVIEW. 1. Algorithms. 2. Steady state analysis of stochastic systems. 3. Parallel compute architectures. 4. Database systems. 5. Programming languages and systems. 6. Algorithms and complexity for Markov processes. PART II: MATHEMATICAL PROGRAMMING AND APPLICATIONS TO STATISTICS. 7. Mathematical programming. A computational perspective. 8. Integer programming. PART III: LEAST SQUARES ESTIMATION. 9. Numerical aspects of solving linear least squares problems. 10. The total least squares problems. PART IV: GENERAL ESTIMATION PROBLEMS. 11. Construction of reliable maximum likelihood algorithms with application to logistic and Cox regression. 12. Nonparametric function estimation. 13. Computational using the QR decomposition. 14. The EM algotithm. 15. Analysis of ordered categorical data through appropriate scaling. PART V: ARTIFICIAL INTELLIGENCE AND STATISTICS. 16. Statistical applications of artificial intelligence. 17. Some aspects of natural language processes. PART VI: SIMULATION AND RESAMPLING. 18. Gibbs sampling. 19. Bootstrap methodology. 20. The art of computer generation of random variables. 21. Jaccknife variance estimation and bias reduction. PART VII: STATISTICAL GRAPHICS. 22. Designing effective statistical graphs. 23. Graphical methods for linear models. 24. Graphics for time series analysis. 25. Graphics as visual language. 26. Statistical graphics and visualization. 27. Multivariate statistical visualization. 28. Graphical methods for process control.