The American Yawp constructs a coherent and accessible narrative from all the best of recent historical scholarship. Without losing sight of politics and power, it incorporates transnational perspectives, integrates diverse voices, recovers narratives of resistance, and explores the complex process of cultural creation. It looks for America in crowded slave cabins, bustling markets, congested tenements, and marbled halls. It navigates between maternity wards, prisons, streets, bars, and boardrooms. Whitman’s America, like ours, cut across the narrow boundaries that strangle many narratives. Balancing academic rigor with popular readability, The American Yawp offers a multi-layered, democratic alternative to the American past.

This book will initiate you into an esoteric world. You will learn and apply the methods of thought that mathematicians use to verify theorems, explore mathematical truth and create new mathematical theories. This will prepare you for advanced mathematics courses, for you will be better able to understand proofs, write your own proofs and think critically and inquisitively about mathematics.

Calculus is designed for the typical two- or three-semester general calculus course, incorporating innovative features to enhance student learning. The book guides students through the core concepts of calculus and helps them understand how those concepts apply to their lives and the world around them. Due to the comprehensive nature of the material, we are offering the book in three volumes for flexibility and efficiency. Volume 1 covers functions, limits, derivatives, and integration

Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.

Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”

Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

This textbook provides a compact introduction to using the R programming language for data analysis. While there are good, freely available resources for learning these skills, they are generally not optimized for use in the classroom. Most fail to include a range of exercises, expect a high level of prior programming or mathematical experience, or only cover a specific niche aspect of data science. Moreover, the vast majority of these free sources do not include permissive licenses that make it easy to re-mix them and adapt it to a specific course. This textbook address these concerns by providing chapters designed to be covered in a single class period, a wide variety of exercises, and a Creative Commons license that allows others to make and republish their notes according to the needs of a specific curriculum.

The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book.

The entire book is written in R Markdown, using RStudio as my text editor and the bookdown package to turn a collection of markdown documents into a coherent whole. The book’s source code is hosted on GitHub, at https://github.com/clauswilke/dataviz. If you notice typos or other issues, feel free to open an issue on GitHub or submit a pull request. If you do the latter, in your commit message, please add the sentence “I assign the copyright of this contribution to Claus O. Wilke,” so that I can maintain the option of publishing this book in other forms.

The Project Gutenberg eBook of Great Expectations, by Charles Dickens. This eBook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook.

The typical introductory real analysis text starts with an analysis of the real number system and uses this to develop the definition of a limit, which is then used as a foundation for the definitions encountered thereafter. While this is certainly a reasonable approach from a logical point of view, it is not how the subject evolved, nor is it necessarily the best way to introduce students to the rigorous but highly non-intuitive definitions and proofs found in analysis.

This book proposes that an effective way to motivate these definitions is to tell one of the stories (there are many) of the historical development of the subject, from its intuitive beginnings to modern rigor. The definitions and techniques are motivated by the actual difficulties encountered by the intuitive approach and are presented in their historical context. However, this is not a history of analysis book. It is an introductory analysis textbook, presented through the lens of history. As such, it does not simply insert historical snippets to supplement the material. The history is an integral part of the topic, and students are asked to solve problems that occur as they arise in their historical context.

This book covers the major topics typically addressed in an introductory undergraduate course in real analysis in their historical order. Written with the student in mind, the book provides guidance for transforming an intuitive understanding into rigorous mathematical arguments. For example, in addition to more traditional problems, major theorems are often stated and a proof is outlined. The student is then asked to fill in the missing details as a homework problem.

"I wrote this book to reflect the experiences and perspectives of the
teens that I encountered. Their voices shape this book just as their
stories shaped my understanding of the role of social media in their
lives. My hope is that this book will shed light on the complex and
fascinating practices of contemporary American youth as they try to
find themselves in a networked world.
As you read this book, my hope is that you will suspend your
assumptions about youth in an effort to understand the social lives of
networked teens. By and large, the kids are all right. But they want
to be understood. This book is my attempt to do precisely that"--Preface.

The Project Gutenberg eBook of Jane Eyre, by Charlotte Brontë.

This eBook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook.

Legal Aspects of Marketing and Sales is an up-to-date textbook that covers legal issues that students who will work in marketing or with marketing managers must understand. The text is organized to permit instructors to tailor the materials to their particular approach. The authors take special care to engage students by relating law to everyday events with their clear, concise and readable style.

Moral dilemmas are a pervasive feature of working life. Moral Reasoning at Work offers a fresh perspective on how to live with them using ethics and moral psychology research. It argues that decision-makers must go beyond compliance and traditional approaches to ethics to prepare for moral dilemmas. The second edition has been updated with a range of examples from the author’s more recent research, to reflect current issues affecting organizations in the digital age. With two new chapters on artificial intelligence and social media, this new edition provides an up-to-date overview of ethical challenges in organizations.

Noba is a high-quality, flexibly structured digital introduction to psychology resource for higher-ed classrooms and virtual classrooms. Noba consists of nearly 90 short (2500-4000 word) chapters authored by leading instructors and researchers including 7 winners of the William James Award. Chapters are organized in familiar categories (Development, Learning & Memory, Personality, etc.) for easy reference. All Noba materials are licensed through Creative Commons under the CC BY-NA-SA license terms.

The Noba website allows anyone to combine chapters in any order to create unique psychology textbooks to suit virtually any curriculum. In addition to allowing users to build their own customized collections, Noba provides a series of "Ready-Made" digital textbooks curated from the Noba chapters to conform to the scope and sequence of some of the most commonly taught 100/200-level psych courses (Intro-to-Psych, Psych as a Biological Science, Psych as a Social Science, etc.). The Ready-made books can also be edited to add or remove chapters, or sections so that they better conform to the specific course an instructor will teach.

Custom-made books, Ready-made books, or even individual chapters can be used online, downloaded as PDFs or shared withe learners via email and social media using easy-share tools built in to the website.

The Project Gutenberg eBook of North and South, by Elizabeth Gaskell. This eBook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away, or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook.

OpenIntro Statistics offers a traditional introduction to statistics at the college level. This textbook is widely used at the college level and offers an exceptional and accessible introduction for students from community colleges to the Ivy League. The textbook has been thoroughly vetted with an estimated 20,000 students using it annually.

Open Source Property: A Free Casebook is a free resource for instructors and students of the first-year Property Law course at American law schools, and anyone else with an interest in the subject.

This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details.

The book is written for three audiences: (1) people finding themselves doing forecasting in business when they may not have had any formal training in the area; (2) undergraduate students studying business; (3) MBA students doing a forecasting elective. We use it ourselves for master's students and third-year undergraduate students at Monash University, Australia.

For most sections, we only assume that readers are familiar with introductory statistics, and with high-school algebra. There are a couple of sections that also require knowledge of matrices, but these are flagged.

At the end of each chapter, we provide a list of “further reading”. In general, these lists comprise suggested textbooks that provide a more advanced or detailed treatment of the subject. Where there is no suitable textbook, we suggest journal articles that provide more information.

We use R throughout the book and we intend for students to learn how to forecast with R. R is free and available on almost every operating system. It is a wonderful tool for all statistical analysis, not just for forecasting.

This fourth edition (published in 2019) was co-authored by Rajiv S. Jhangiani (Kwantlen Polytechnic University), Carrie Cuttler (Washington State University), and Dana C. Leighton (Texas A&M University—Texarkana) and is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Revisions throughout the current edition include changing the chapter and section numbering system to better accommodate adaptions that remove or reorder chapters; continued reversion from the Canadian edition; general grammatical edits; replacement of “he/she” to “they” and “his/her” to “their”; removal or update of dead links; embedded videos that were not embedded; moved key takeaways and exercises from the end of each chapter section to the end of each chapter; a new cover design. In addition, the following revisions were made to specific chapters:

This is the website for “R for Data Science”. This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.