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Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you'll find the information, insight, and tools you need to flourish in today's data-driven economy. You'll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today's business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It's about the exciting things being done around Big Data to run a flourishing business. It's about the precepts, principals, and best practices that you need know for best-in-class business data science.

Epigenetics is currently one of the fastest-growing fields in the sciences. Epigenetic information not only controls DNA expression but links genetic factors with the environmental experiences that influence the traits and characteristics of an individual. What we eat, where we work, and how we live affects not only the activity of our genes but that of our offspring as well. This discovery has imposed a revolutionary theoretical shift on modern biology, especially on evolutionary theory. It has helped to uncover the developmental processes leading to cancer, obesity, schizophrenia, alcoholism, and aging, and to facilitate associated medial applications such as stem cell therapy and cloning. Above the Gene, Beyond Biology explores how biologists in this booming field investigate and explain living systems. Jan Baedke offers the first comprehensive philosophical discussion of epigenetic concepts, explanations, and methodologies so that we can better understand this “epigenetic turn” in the life sciences from a philosophical perspective.

Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from p-value computations. Pruim develops the students'practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment $\mathsf{R}$ is used throughout the text and is integral to the exposition. Attention is paid to developing students'mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations. In the second edition, the $\mathsf{R}$ code has been updated throughout to take advantage of new $\mathsf{R}$ packages and to illustrate better coding style. New sections have been added covering bootstrap methods, multinomial and multivariate normal distributions, the delta method, numerical methods for Bayesian inference, and nonlinear least squares. Also, the use of matrix algebra has been expanded, but remains optional, providing instructors with more options regarding the amount of linear algebra required.

Essays examining the role of shame as an American cultural practice and how public shaming enforces conformity and group coherence.On any given day in America's news cycle, stories and images of disgraced politicians and celebrities solicit our moral indignation, their misdeeds fueling a lucrative economy of shame and scandal. Shame is one of the most coercive, painful, and intriguing of human emotions. Only in recent years has interest in shame extended beyond a focus on the subjective experience of this emotion and its psychological effects. The essays collected here consider the role of shame as cultural practice and examine ways that public shaming practices enforce conformity and group coherence. Addressing abortion, mental illness, suicide, immigration, and body image among other issues, this volume calls attention to the ways shaming practices create and police social boundaries; how shaming speech is endorsed, judged, or challenged by various groups; and the distinct ways that shame is encoded and embodied in a nation that prides itself on individualism, diversity, and exceptionalism. Examining shame through a prism of race, sexuality, ethnicity, and gender, these provocative essays offer a broader understanding of how America's discourse of shame helps to define its people as citizens, spectators, consumers, and moral actors.“An eclectic anthology, it offers the readers more than one argument and perspective, which makes the volume itself lively and rich.” —Ron Scapp, coeditor of Fashion Statements: On Style, Appearance, and Reality

This title includes a number of Open Access chapters. Nutrition is becoming ever more central to our understanding of metabolic processes. Nutritional biochemistry offers insight into the mechanisms by which diet influences human health and disease. This book focuses on five aspects of this complex field of study: nutritional genomics, clinical nut

Ten years after the Human Genome Project's completion the life sciences stand in a moment of uncertainty, transition, and contestation. The postgenomic era has seen rapid shifts in research methodology, funding, scientific labor, and disciplinary structures. Postgenomics is transforming our understanding of disease and health, our environment, and the categories of race, class, and gender. At the same time, the gene retains its centrality and power in biological and popular discourse. The contributors to Postgenomics analyze these ruptures and continuities and place them in historical, social, and political context. Postgenomics, they argue, forces a rethinking of the genome itself, and opens new territory for conversations between the social sciences, humanities, and life sciences.Contributors. Russ Altman, Rachel A. Ankeny, Catherine Bliss, John Dupré, Michael Fortun, Evelyn Fox Keller, Sabina Leonelli, Adrian Mackenzie, Margot Moinester, Aaron Panofsky, Sarah S. Richardson, Sara Shostak, Hallam Stevens

Multiple Sclerosis (MS) is an enigmatic immune mediated disease of the central nervous system that affects about 350,000 individuals in the US, and many more around the world. The mechanism of this disease is largely unknown and there is no cure for it. However, there are several well-characterized experimental animal models that help us understand and speculate about potential mechanisms of pathology in this disease. Many of the experimental therapies designed for this disease rely on testing the drugs in animal models before using it in clinical trials. This book combines for the first time the different experimental models for MS (including immune-mediated and viral) under one roof, and highlights aspects that are different or shared among these experimental models. It's aim is to improve our understanding of this devastating disease and help us think about potential additional therapies for it.