Teaching Elementary Physical Education: Strategies for the Classroom Teacher gives elementary education teachers the tools and strategies they need to teach physical education using a skills-based approach. Designed for the non-physical education teacher, the textbook provides a shorter, more focused presentation of how and what to teach in physical education. For many elementary education students, this course is the only exposure they will have to teaching physical education. As such, Teaching Elementary Physical Education: Strategies for the Classroom Teacher builds a strong foundation in the subject, featuring just the right amount of coverage on key topics including safety, child development, developing a PE curriculum, assessment, management, and discipline. The text's skills-theme approach focuses on teaching broadly applicable movements, such as throwing and catching, instead of specific games.
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, marketing, and 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 approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
The stories of black American professionals, both historic and contemporary, reveal the hardships and triumphs they faced in overcoming racism to succeed in their chosen fields. This extraordinary four-volume work is the first of its kind, a comprehensive exploration of the obstacles black men and women, both historic and contemporary, have faced and overcome to succeed in professional positions. Voices of Historical and Contemporary Black American Pioneers includes the life and career histories of black American pioneers, past and present, who have achieved extraordinary success in fields as varied as aviation and astronautics, education, social sciences, the humanities, the fine and performing arts, law and government, and medicine and science. The set covers well-known figures, but is also an invaluable source of information on lesser-known individuals whose accomplishments are no less admirable. Arranged by career category, each section of the work begins with a biographical narrative of early black pioneers in the field, followed by original interviews conducted by the editors or autobiographical narratives written by the subjects. In all, more than 150 scholars and professionals share inspiring insights into how they persevered to overcome racism and succeed in an often-hostile world.
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
Amelie Hastie rethinks female authorship within film history by expanding the historical archive to include dollhouses, scrapbooks, memoirs, cookbooks, and ephemera.
