Communication in the Real World: An Introduction to Communication Studies overviews the time-tested conceptual foundations of the field, while incorporating the latest research and cutting-edge applications of these basics. Each chapter will include timely, concrete, and real-life examples of communication concepts in action. A key feature of this book is the integration of content regarding diversity and organizational communication in each chapter through examples and/or discrete sub-sections. Discussions of diversity are not relegated to feature boxes. Also integrated into the content are examples that are inclusive in terms of race, gender, sexuality, ability, age, marital status, religion, and other diverse identity characteristics.

Concepts of Biology is designed for the introductory biology course for nonmajors taught at most two- and four-year colleges. The scope, sequence, and level of the program are designed to match typical course syllabi in the market. Concepts of Biology includes interesting applications, features a rich art program, and conveys the major themes of biology.

A Cool, Brisk Walk Through Discrete Mathematics, an innovative and non-traditional approach to learning Discrete Math, is available for low cost from Blurb or via free download.

This is Volume Two of the Crystal Ball series: Foundations for Data Science. The author titled the first volume “Introduction to Data Science” because it led readers through a dip-your-toes-in-the-water
experience. Readers took a brief tour through the various elements in this diverse field and got a feel for what it was all about. In Foundations, which is the next level, the reader's growing knowledge is further developed to a firm base on which to build everything else.

A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks. This engaging read will allow any dedicated learner to build the skills necessary to contribute to the Data Science revolution, regardless of background.

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.

How can we effectively and efficiently teach data science to students with little to no background in computing and statistical thinking? How can we equip them with the skills and tools for reasoning with various types of data and leave them wanting to learn more? This introductory data science course is our (working) answer to this question.
The source code for everything you see here can be found on GitHub.

The core content of the course focuses on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modelling, and effective communication of results. Time permitting, the course also introduces additional concepts and tools like interactive visualization and reporting, text analysis, and Bayesian inference. A heavy emphasis is placed on a consistent syntax (with tools from the tidyverse), reproducibility (with R Markdown), and version control and collaboration (with Git and GitHub). In addition, out-of-class learning is supplemented with interactive tutorials. The goal of the course is to bring students from zero to being able to work in a team on a fully reproducible data science project analysing a dataset of their choice and answering questions they care about.

Data Science in a Box contains the materials required to teach (or learn from) the course described above, all of which are freely-available and open-source. They include course materials such as slide decks, lecture and live coding videos, homework assignments, guided labs, sample exams, a final project assignment, as well as materials for instructors such as pedagogical tips, information on computing infrastructure, technology stack, and course logistics.

Majority of the materials linked live in the GitHub repo serving this website.

Please note that Data Science in a Box uses a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

""Life is made up of a never-ending sequence of decisions. Many decisions – such as what to
watch on television or what to eat for breakfast – are rather unimportant. Other decisions –
such as what career to pursue, whether or not to invest all of one’s savings in the purchase
of a house – can have a major impact on one’s life. This book is concerned with Decision
Making, which the Oxford Dictionary defines as “the process of deciding about something
important”. We will not attempt to address the issue of what decisions are to be considered
“important”. After all, what one person might consider an unimportant decision may be
viewed by another individual as very important. What we are interested in is the process of
making decisions and what it means to be a “rational” decision maker""--Introductory paragraph.

Find 32 Youtube videos of lectures based on the Decision Making book through this link: http://faculty.econ.ucdavis.edu/faculty/bonanno/DM_Book.html.

Copyright information: You are free to redistribute this book in pdf format. If you make use of any part of this book you must give appropriate credit to the author. You may not remix, transform, or build upon the material without permission from the author. You may not use the material for commercial purposes.

Discrete Mathematics: An Open Introduction is a free, open source textbook appropriate for a first or second year undergraduate course for math majors, especially those who will go on to teach. The textbook has been developed while teaching the Discrete Mathematics course at the University of Northern Colorado. Primitive versions were used as the primary textbook for that course since Spring 2013, and have been used by other instructors as a free additional resource. Since then it has been used as the primary text for this course at UNC, as well as at other institutions.

Electromagnetics Volume 1 by Steven W. Ellingson is a 225-page, peer-reviewed open educational resource intended for electrical engineering students in the third year of a bachelor of science degree program. It is intended as a primary textbook for a one-semester first course in undergraduate engineering electromagnetics. The book employs the “transmission lines first” approach in which transmission lines are introduced using a lumped-element equivalent circuit model for a differential length of transmission line, leading to one-dimensional wage equations for voltage and current.

Suggested citation: Ellingson, Steven W. (2018) Electromagnetics, Vol. 1. Blacksburg, VA: VT Publishing. https://doi.org/10.21061/electromagnetics-vol-1 CC BY-SA 4.0

Three formats of this book are available:
Print (ISBN 978-0-9979201-8-5)
PDF (ISBN 978-0-9979201-9-2)
LaTeX source files

If you are a professor reviewing, adopting, or adapting this textbook please help us understand a little more about your use by filling out this form: http://bit.ly/vtpublishing-updates

Additional Resources
Problem sets and the corresponding solution manual are also available.
Community portal for the Electromagnetics series https://www.oercommons.org/groups/electromagnetics-user-group/3455/
Faculty listserv for the Electromagnetics series https://groups.google.com/a/vt.edu/d/forum/electromagnetics-g
Submit feedback and suggestions http://bit.ly/electromagnetics-suggestion

Table of Contents:
Chapter 1: Preliminary Concepts
Chapter 2: Electric and Magnetic Fields
Chapter 3: Transmission Lines
Chapter 4: Vector Analysis
Chapter 5: Electrostatics
Chapter 6: Steady Current and Conductivity
Chapter 7: Magnetostatics
Chapter 8: Time-Varying Fields
Chapter 9: Plane Waves in Lossless Media
Appendixes
A. Constitutive Parameters of Some Common Materials
B. Mathematical Formulas
C. Physical Constants

About the Author: Steven W. Ellingson (ellingson@vt.edu) is an Associate Professor at Virginia Tech in Blacksburg, Virginia in the United States. He received PhD and MS degrees in Electrical Engineering from the Ohio State University and a BS in Electrical & Computer Engineering from Clarkson University. He was employed by the US Army, Booz-Allen & Hamilton, Raytheon, and the Ohio State University ElectroScience Laboratory before joining the faculty of Virginia Tech, where he teaches courses in electromagnetics, radio frequency systems, wireless communications, and signal processing. His research includes topics in wireless communications, radio science, and radio frequency instrumentation. Professor Ellingson serves as a consultant to industry and government and is the author of Radio Systems Engineering (Cambridge University Press, 2016).

This textbook is part of the Open Electromagnetics Project led by Steven W. Ellingson at Virginia Tech. The goal of the project is to create no-cost openly-licensed content for courses in undergraduate engineering electromagnetics. The project is motivated by two things: lowering learning material costs for students and giving faculty the freedom to adopt, modify, and improve their educational resources.

Accessibility features of this book: Screen reader friendly, navigation, and Alt-text for all images and figures.

Publication of this book was made possible in part by the Open Education Faculty Initiative Grant program at the University Libraries at Virginia Tech. http://guides.lib.vt.edu/oer/grants

This textbook is intended for use in introductory Entrepreneurship classes at the undergraduate level. Due to the wide range of audiences and course approaches, the book is designed to be as flexible as possible. Theoretical and practical aspects are presented in a balanced manner, and specific components such as the business plan are provided in multiple formats. Entrepreneurship aims to drive students toward active participation in entrepreneurial roles and exposes them to a wide range of companies and scenarios.

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.

Exploring Computer Science is intended for those with no experience with computer science at all. Its focus is on developing algorithms and solving problems. Python is used as the programming language.

Table of Contents:
Preface
Chapter 1: The Road Ahead
Chapter 2: Getting Started
Chapter 3: First Programs
Chapter 4: Types and Operations
Chapter 5: Making Decisions
Chapter 6: Going Back Again
Chapter 7: Algorithms
Chapter 8: Using Libraries
Chapter 9: Lists
Chapter 10: Functions

In this survey text, readers will explore the foundations of American education through a critical lens. Topics include the teaching profession, influences on student learning, philosophical and historical foundations, structures of schools, ethical and legal issues, curriculum, classroom environment, and the path forward.

Foundations of Computation is a free textbook for a one-semester course in theoretical computer science. It has been used for several years in a course at Hobart and William Smith Colleges. The course has no prerequisites other than introductory computer programming. The first half of the course covers material on logic, sets, and functions that would often be taught in a course in discrete mathematics. The second part covers material on automata, formal languages, and grammar that would ordinarily be encountered in an upper level course in theoretical computer science.

Fundamentals of Business, Second Edition (2018) is an 372-page open education resource intended to serve as a no-cost, faculty customizable primary text for one-semester undergraduate introductory business courses. It covers the following topics in business: Teamwork; economics; ethics; entrepreneurship; business ownership, management, and leadership; organizational structures and operations management; human resources and motivating employees; managing in labor union contexts; marketing and pricing strategy; hospitality and tourism, accounting and finance, and personal finances. The textbook was designed for use in Virginia Tech’s Pamplin College of Business introductory level business course, MGT1104 Foundations of Business and is shared under a Creative Commons Non-Commercial ShareAlike 4.0 license.

2018 version formats include: PDF, Accessible "screen reader friendly" PDF, ePub, Mobi, XML/Pressbooks (editable), and open document format.
The Pressbooks online version (HTML) is available at: https://doi.org/10.21061/fundamentals-of-business
The 2016 version of this book includes editable MSWord files.

If you are an instructor reviewing, adopting, or adapting this textbook, please help us understand your use by filling out this form http://bit.ly/business-interest

Instructor resource sharing portal: https://www.oercommons.org/groups/fundamentals-of-business-user-group/1379

Fundamentals of Business, third edition (2020) is an 370-page open education resource intended to serve as a no-cost, faculty customizable primary text for one-semester undergraduate introductory business courses. It covers the following topics in business: Teamwork; economics; ethics; entrepreneurship; business ownership, management, and leadership; organizational structures and operations management; human resources and motivating employees; managing in labor union contexts; marketing and pricing strategy; hospitality and tourism, accounting and finance, and personal finances. The textbook was designed for use in Virginia Tech’s Pamplin College of Business introductory level business course, MGT1104 Foundations of Business and is shared under a Creative Commons Non-Commercial ShareAlike 4.0 license.

Read more about the book and see various access links at: https://blogs.lt.vt.edu/openvt/2021/01/06/announcing-open-textbook-fundamentals-of-business-third-edition

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.