Structure and Interpretation of Computer Programs has had a dramatic impact on computer science curricula over the past decade. This long-awaited revision contains changes throughout the text.

This instructor's manual and reader's guide accompanies the secondedition of Structure and Interpretation of Computer Programs,by Harold Abelson and Gerald Jay Sussman with Julie Sussman. This instructor's manual and reader's guide accompanies the second edition of Structure and Interpretation of Computer Programs, by Harold Abelson and Gerald Jay Sussman with Julie Sussman. It contains discussions of exercises and other material in the text as well as supplementary material, additional examples and exercises, and teaching suggestions. An appendix summarizes the Scheme programming language as used in the text, showing at what point in the text each element of Scheme is introduced.

This book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis? How should it be taught? What techniques work best, and for whom? How valid are the results? How much data should be tested? Which machine languages should be used, if used at all? Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy – when to use which technique – are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics.