Through examples and analogies, Computational Thinking for the Modern Problem Solver introduces computational thinking as part of an introductory computing course and shows how computer science concepts are applicable to other fields. It keeps the material accessible and relevant to noncomputer science majors. With numerous color figures, this classroom-tested book focuses on both foundational computer science concepts and engineering topics.

It covers abstraction, algorithms, logic, graph th...

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.

Offers a unified discussion of generalized linear models and correlated data methods. Provides information suitable for graduate non-statistics majors or advanced undergraduate statistics majors. Includes case studies with real data. Offers material on R at the end of each chapter. Provides a solutions manual.

Waar komt de wiskunde vandaan? Wie bedacht al die rekensymbolen, en waarom? Wat is het verhaal achter π? … negatieve getallen? … het metrieke stelsel? … kwadratische vergelijkingen? … sinus en cosinus? … logaritmen? Deze vragen en vele andere worden beantwoord in 30 los te lezen schetsen, op een informele en prettige manier, toegankelijk voor leraren, studenten en anderen die nieuwsgierig zijn naar de geschiedenis van wiskundige ideeën. De schetsen worden voorafgegaan door een rui...

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage app...