Published by the American Geophysical Union as part of the Special Publications Series. Whether you are a science undergraduate or graduate student, post-doc or senior scientist, you need practical career development advice. Put Your Science to Work: The Take-Charge Career Guide for Scientists can help you explore all your options and develop dynamite strategies for landing the job of your dreams. Completely revised and updated from the best-selling To Boldly Go: A Practical Career Guide for Scientists, this second edition offers expert help from networking to negotiating a job offer. This is the book you need to start moving your career in the right direction.
“The Knowledge Machine is the most stunningly illuminating book of the last several decades regarding the all-important scientific enterprise.” —Rebecca Newberger Goldstein, author of Plato at the Googleplex A paradigm-shifting work, The Knowledge Machine revolutionizes our understanding of the origins and structure of science. • Why is science so powerful? • Why did it take so long—two thousand years after the invention of philosophy and mathematics—for the human race to start using science to learn the secrets of the universe? In a groundbreaking work that blends science, philosophy, and history, leading philosopher of science Michael Strevens answers these challenging questions, showing how science came about only once thinkers stumbled upon the astonishing idea that scientific breakthroughs could be accomplished by breaking the rules of logical argument. Like such classic works as Karl Popper’s The Logic of Scientific Discovery and Thomas Kuhn’s The Structure of Scientific Revolutions, The Knowledge Machine grapples with the meaning and origins of science, using a plethora of vivid historical examples to demonstrate that scientists willfully ignore religion, theoretical beauty, and even philosophy to embrace a constricted code of argument whose very narrowness channels unprecedented energy into empirical observation and experimentation. Strevens calls this scientific code the iron rule of explanation, and reveals the way in which the rule, precisely because it is unreasonably close-minded, overcomes individual prejudices to lead humanity inexorably toward the secrets of nature. “With a mixture of philosophical and historical argument, and written in an engrossing style” (Alan Ryan), The Knowledge Machine provides captivating portraits of some of the greatest luminaries in science’s history, including Isaac Newton, the chief architect of modern science and its foundational theories of motion and gravitation; William Whewell, perhaps the greatest philosopher-scientist of the early nineteenth century; and Murray Gell-Mann, discoverer of the quark. Today, Strevens argues, in the face of threats from a changing climate and global pandemics, the idiosyncratic but highly effective scientific knowledge machine must be protected from politicians, commercial interests, and even scientists themselves who seek to open it up, to make it less narrow and more rational—and thus to undermine its devotedly empirical search for truth. Rich with illuminating and often delightfully quirky illustrations, The Knowledge Machine, written in a winningly accessible style that belies the import of its revisionist and groundbreaking concepts, radically reframes much of what we thought we knew about the origins of the modern world.
Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. "Data scientist is the sexiest job in the 21st century," according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients.
The word kanuna is an ancient term and is in use today. It is pronounced ""kah-hoo-nah"" and meant ""keeper of the secret."" The word for their secret lore was never found. The role of secrecy had been so strong and so well kept that it may never have been given a name... The name we used, for this reason, was Huna (pronounced ""hoo-nah""), which means ""secret."" Enough basic information will be incorporated in this book to give a working picture of the system of beliefs and practices upon which experimental work has been based. Interspersed with the instructions will be found explanations of the sources of conclusions, of symbols, words, and word-roots familiar to Huna, but which we also found in other religions, mainly Christianity. These will tend to show that the meanings ascribed to Huna are well founded. Huna is compatible with other systems. It interferes with none of them, but makes possible a greater understanding of the old truths. (From the Introduction) Get Your Copy Today!
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
A guide for scientists on the journey from the end of a postdoctoral career to the point of promotion to Associate Professor, this 2nd edition focuses on three aspects of the academic setting: Scholarship, Teaching, and Service. Valuable advice is provided on such topics as choosing and landing an academic job; setting up and managing the lab; obtaining funds; organizing, writing, and publishing your work; teaching and mentoring; and the promotion and tenure process.
The culmination of a lifetime spent in a variety of fields - sociology, anthropology, economics, psychology, and philosophy of science - How Does Social Science Work? takes an innovative, sometimes iconoclastic look at social scientists at work in many disciplines. It describes how they investigate and the kinds of truth they produce, illuminating the weaknesses and dangers inherent in their research.At once an analysis, a critique, and a synthesis, this major study begins by surveying philosophical approaches to hermeneutics, to examine the question of how social science ought to work. It illustrates many of its arguments with untraditional examples, such as the reception of the work of the political biographer Robert Caro to show the hermeneutical problems of ethnographers. The major part of the book surveys sociological, political, and psychological studies of social science to get a rounded picture of how social science works,Paul Diesling warns that "social science exists between two opposite kinds of degeneration, a value-free professionalism that lives only for publications that show off the latest techniques, and a deep social concern that uses science for propaganda." He argues for greater self-awareness and humility among social scientists, although he notes that "some social scientists . . . will angrily reject the thought that their personality affects their research in any way."This profound and sometimes witty book will appeal to students and practitioners in the social sciences who are ready to take a fresh look at their field. An extensive bibliography provides a wealth of references across an array of social science disciplines.
To succeed at work, first you need to understand your own brain If you're in a job interview, how should you think about the mindset of the interviewer? If you've just been promoted, how do you handle the tensions of managing former peers? And what are the telltale mental signs that it's time to start planning your next career move? We know that psychology can teach us much about behaviors and challenges relevant to work, such as making better decisions, influencing people, and dealing with stress. But many popular books on these topics analyze them as universal human phenomena without providing real-life, constructive career help. Bring Your Brain to Work changes all that. Professor, author, and popular radio host Art Markman focuses on three essential elements of a successful career--getting a job, excelling at work, and finding your next position--and expertly illustrates how cognitive science, especially psychology, sheds fascinating and useful light on each of these elements. To succeed at a job interview, for example, you need to understand the mindset of the interviewer and know how to come across as exactly the individual the company wants to hire. To keep that job, it's critical to master the mental challenge of learning every day. Finally, careers require constant development, so you need to be able to sense when it's time to move up or out and to prepare yourself for the move. So many of the hurdles you face throughout your career are, first and foremost, psychological challenges, and Markman shows you how to use your different mental systems--motivational, social, and cognitive--to manage them more effectively. Integrating the latest research with engaging stories and examples from across the professional spectrum, Bring Your Brain to Work gets inside your head, helping you to succeed through a better understanding of yourself and those around you.
Like fast food, fast science is quickly prepared, not particularly good, and it clogs up the system. Efforts to tackle our most pressing issues have been stymied by conflict within the scientific community and mixed messages symptomatic of a rushed approach. What is more, scientific research is being shaped by the bubbles and crashes associated with economic speculation and the market. A focus on conformism, competitiveness, opportunism and flexibility has made it extremely difficult to present cases of failure to the public, for fear that it will lose confidence in science altogether. In this bold new book, distinguished philosopher Isabelle Stengers shows that research is deeply intertwined with broader social interests, which means that science cannot race ahead in isolation but must learn instead to slow down. Stengers offers a path to an alternative science, arguing that researchers should stop seeing themselves as the 'thinking, rational brain of humanity' and refuse to allow their expertise to be used to shut down the concerns of the public, or to spread the belief that scientific progress is inevitable and will resolve all of society's problems. Rather, science must engage openly and honestly with an intelligent public and be clear about the kind of knowledge it is capable of producing. This timely and accessible book will be of great interest to students, scholars and policymakers in a wide range of fields, as well anyone concerned with the role of science and its future.
The essential guide for data scientists and for leaders who must get more from their data science teams The Economist boldly claims that data are now "the world's most valuable resource." But, as Kenett and Redman so richly describe, unlocking that value requires far more than technical excellence. The Real Work of Data Science explores understanding the problems, dealing with quality issues, building trust with decision makers, putting data science teams in the right organizational spots, and helping companies become data-driven. This is the work that spells the difference between a good data scientist and a great one, between a team that makes marginal contributions and one that drives the business, between a company that gains some value from its data and one in which data truly is "the most valuable resource." "These two authors are world-class experts on analytics, data management, and data quality; they've forgotten more about these topics than most of us will ever know. Their book is pragmatic, understandable, and focused on what really counts. If you want to do data science in any capacity, you need to read it." —Thomas H. Davenport, Distinguished Professor, Babson College and Fellow, MIT Initiative on the Digital Economy "I like your book. The chapters address problems that have faced statisticians for generations, updated to reflect today's issues, such as computational Big Data." —Sir David Cox, Warden of Nuffield College and Professor of Statistics, Oxford University "Data science is critical for competitiveness, for good government, for correct decisions. But what is data science? Kenett and Redman give, by far, the best introduction to the subject I have seen anywhere. They address the critical questions of formulating the right problem, collecting the right data, doing the right analyses, making the right decisions, and measuring the actual impact of the decisions. This book should become required reading in statistics and computer science departments, business schools, analytics institutes and, most importantly, by all business managers." —A. Blanton Godfrey, Joseph D. Moore Distinguished University Professor, Wilson College of Textiles, North Carolina State University
