Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.The book also introduces modern topics that do not normally appear in mathematical statistics texts but ar
Perfect for senior undergraduates and first-year graduate students in geophysics, physics, mathematics, geology and engineering, this book is devoted exclusively to seismic wave theory. The result is an invaluable teaching tool, with its detailed derivations of formulas, clear explanations of topics, exercises along with selected answers, and an additional set of exercises with derived answers on the book's website. Some highlights of the text include: a review of vector calculus and Fourier transforms and an introduction to tensors, which prepare readers for the chapters to come; and a detailed discussion on computing reflection and transmission coefficients, a topic of wide interest in the field; a discussion in later chapters of plane waves in anisotropic and anelastic media, which serves as a useful introduction to these two areas of current research in geophysics. Students will learn to understand seismic wave theory through the book's clear and concise pedagogy.
