Objects in harmonic motion have the ability to transfer some of their energy over large distances. Light Nature: This chapter covers the nature of light, polarization, and color.

    Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    Grounded in the latest knowledge and filled with illustrative examples from diverse institutions, as well as helpful reflection questions, the book's guidance can be put to immediate use. In addition, the authors suggest ways of avoiding common pitfalls and address what to do when faced with budget fluctuations and changing fiscal environments."This book is vitally important for understanding the complex financial underpinnings of higher education. Could there be a more critical time for administrators to add to their knowledge in this area? I don't think so." --EUGENE S. SUNSHINE, senior vice president for business and finance, Northwestern University"The authors have produced an easily readable and valuable resource for board members, administrators, students, faculty, or anyone interested in knowing about budgeting and the budgeting process. Their treatment of the subject is thorough and complete." --LARRY H. DIETZ, vice chancellor for student affairs, Southern Illinois University, Carbondale"This is the best 'nitty-gritty-how-to' book on university budgeting that I have found. My graduate students at both the master's and doctoral levels have found it to be a comprehensive, insightful, and useful tool in their graduate studies." --LINDA KUK, program chair, Higher Education Graduate Programs, and associate professor of education, Colorado State University

    It helps in the process of choosing the right statistical technique and includes a detailed guide to interpreting SPSS ouput.

    But many managers can't read a balance sheet, wouldn't recognize a liquidity ratio, and don't know how to calculate return on investment. Worse, they don't have any idea where the numbers come from or how reliable they really are. In Financial Intelligence, Karen Berman and Joe Knight teach the basics of finance--but with a twist. Financial reporting, they argue, is as much art as science. Because nobody can quantify everything, accountants always rely on estimates, assumptions, and judgment calls. Savvy managers need to know how those sources of possible bias can affect the financials and that sometimes the numbers can be challenged. While providing the foundation for a deep understanding of the financial side of business, the book also arms managers with practical strategies for improving their companies' performance--strategies, such as "managing the balance sheet," that are well understood by financial professionals but rarely shared with their nonfinancial colleagues. Accessible, jargon-free, and filled with entertaining stories of real companies, Financial Intelligence gives nonfinancial managers the financial knowledge and confidence for their everyday work. Karen Berman and Joe Knight are the owners of the Los Angeles-based Business Literacy Institute and have trained tens of thousands of managers at many leading organizations. Co-author John Case has written several popular books on management.

    Clear, comprehensive coverage of utility theory, 2-person zero-sum games, 2-person non-zero-sum games, n-person games, individual and group decision-making, more. Bibliography.

    Students will find concepts easy-to-understand through the clear writing style and full-color figures that illustrate current technology and trends. Examples and cases are drawn from actual systems projects that enable students to learn in the context of solving problems, much like the ones they will encounter on the job. This approach, combined with motivating tools such as the SCR Associates interactive Web-Based Case Study, Systems Analyst's Toolkit, the Student Study Tool on CD-ROM, and more, makes Systems Analysis and Design, Seventh Edition a wise and exciting choice for your introductory systems analysis and design class.

    Whether you are a student, software developer, tech entrepreneur, or researcher in computer science, this authoritative and self-contained book tells you everything you need to know about the new global money for the Internet age.How do Bitcoin and its block chain actually work? How secure are your bitcoins? How anonymous are their users? Can cryptocurrencies be regulated? These are some of the many questions this book answers. It begins by tracing the history and development of Bitcoin and cryptocurrencies, and then gives the conceptual and practical foundations you need to engineer secure software that interacts with the Bitcoin network as well as to integrate ideas from Bitcoin into your own projects. Topics include decentralization, mining, the politics of Bitcoin, altcoins and the cryptocurrency ecosystem, the future of Bitcoin, and more.An essential introduction to the new technologies of digital currencyCovers the history and mechanics of Bitcoin and the block chain, security, decentralization, anonymity, politics and regulation, altcoins, and much moreFeatures an accompanying website that includes instructional videos for each chapter, homework problems, programming assignments, and lecture slidesAlso suitable for use with the authors' Coursera online courseElectronic solutions manual (available only to professors)

    Now renewables are overtaking fossil fuels, with wind and solar energy becoming cheaper and more competitive every year. Growth in renewable energy will further accelerate as electric vehicles become less expensive than traditional automobiles. Understanding the implications of the energy transition will prepare us for the many changes ahead.This book is a primer for readers of all levels on the coming energy transition and its global consequences. Bruce Usher provides a concise yet comprehensive explanation for the extraordinary growth in wind and solar energy; the trajectory of the transition from fossil fuels to renewables; and the implications for industries, countries, and the climate. Written in a straightforward style with easy-to-understand visual aids, the book illuminates the strengths and weaknesses of renewable energy based on business fundamentals and analysis of the economic forces that have given renewables a tailwind. Usher dissects the winners and losers, illustrating how governments and businesses with a far-sighted approach will reap long-term benefits while others will trail behind. Alongside the business and finance case for renewable energy, he provides a timely illustration of the threat of catastrophic climate change and the perils of delay. A short and powerful guide to our energy present and future, this book makes it clear that, from both economic and environmental perspectives, there is no time to lose.

    Free of review and ramp-up material, Statistics Essentials For Dummies sticks to the point, with content focused on key course topics only. It provides discrete explanations of essential concepts taught in a typical first semester college-level statistics course, from odds and error margins to confidence intervals and conclusions. This guide is also a perfect reference for parents who need to review critical statistics concepts as they help high school students with homework assignments, as well as for adult learners headed back into the classroom who just need a refresher of the core concepts. The Essentials For Dummies Series Dummies is proud to present our new series, The Essentials For Dummies. Now students who are prepping for exams, preparing to study new material, or who just need a refresher can have a concise, easy-to-understand review guide that covers an entire course by concentrating solely on the most important concepts. From algebra and chemistry to grammar and Spanish, our expert authors focus on the skills students most need to succeed in a subject.

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    The many aspects of our lives that are governed by social networks make it critical to understand how they impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do. In Social and Economic Networks, Matthew Jackson offers a comprehensive introduction to social and economic networks, drawing on the latest findings in economics, sociology, computer science, physics, and mathematics. He provides empirical background on networks and the regularities that they exhibit, and discusses random graph-based models and strategic models of network formation. He helps readers to understand behavior in networked societies, with a detailed analysis of learning and diffusion in networks, decision making by individuals who are influenced by their social neighbors, game theory and markets on networks, and a host of related subjects. Jackson also describes the varied statistical and modeling techniques used to analyze social networks. Each chapter includes exercises to aid students in their analysis of how networks function.This book is an indispensable resource for students and researchers in economics, mathematics, physics, sociology, and business.

    We’ve comea long way since then. With this book, Drs. Romero and Balch liftthe veil from many of these once-opaque concepts in high-techfinance. We can all benefit from learning how the cooperationbetween wetware and software creates fitter models. This bookdoes a fantastic job describing how the latest advances in financialmodeling and data science help today’s portfolio managerssolve these greater riddles. —Michael Himmel, ManagingPartner, Essex Asset ManagementI applaud Phil Romero’s willingness to write about the hedgefund world, an industry that is very private, often flamboyant,and easily misunderstood. As with every sector of the investmentlandscape, the hedge fund industry varies dramaticallyfrom quantitative “black box” technology, to fundamental researchand old-fashioned stock picking. This book helps investorsdistinguish between these diverse opposites and understandtheir place in the new evolving world of finance. —Mick Elfers,Founder and Chief Investment Strategist, Irvington Capital