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| data science major usc: Python for Informatics Charles Severance, 2013 This book is designed to introduce students to programming and computational thinking through the lens of exploring data. You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet. It is an easy-to-use and easy-to learn programming language that is freely available on Windows, Macintosh, and Linux computers. There are free downloadable copies of this book in various electronic formats and a self-paced free online course where you can explore the course materials. All the supporting materials for the book are available under open and remixable licenses at the www.py4inf.com web site. This book is designed to teach people to program even if they have no prior experience. This book covers Python 2. An updated version of this book that covers Python 3 is available and is titled, Python for Everybody: Exploring Data in Python 3. |
| data science major usc: Deep Learning and Its Applications Arvind Kumar Tiwari, 2021 In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, etc. This book presents an introduction to deep learning and various applications of deep learning such as recommendation systems, text recognition, diabetic retinopathy prediction of breast cancer, prediction of epilepsy, sentiment, fake news detection, software defect prediction and protein function prediction-- |
| data science major usc: Translational Biomedical Informatics Bairong Shen, Haixu Tang, Xiaoqian Jiang, 2016-10-31 This book introduces readers to essential methods and applications in translational biomedical informatics, which include biomedical big data, cloud computing and algorithms for understanding omics data, imaging data, electronic health records and public health data. The storage, retrieval, mining and knowledge discovery of biomedical big data will be among the key challenges for future translational research. The paradigm for precision medicine and healthcare needs to integratively analyze not only the data at the same level – e.g. different omics data at the molecular level – but also data from different levels – the molecular, cellular, tissue, clinical and public health level. This book discusses the following major aspects: the structure of cross-level data; clinical patient information and its shareability; and standardization and privacy. It offers a valuable guide for all biologists, biomedical informaticians and clinicians with an interest in Precision Medicine Informatics. |
| data science major usc: Careers in Information Science Louise Schultz, 1963 Presents copy for use as a reference brochure and a giveaway sheet to be distributed to guidance counselors to help them direct young people into the growing field of Information Science. Sets forth that Information Science is concerned with the properties, behavior, and flow of information. Describes how it is used, both by individuals and in large systems. Discusses the opportunities in Information Science and outlines three relatively different career areas: (1) Special Librarianship; (2) Literature Analysis; and (3) Information System Design. Details an educational program appropriate for participation in these career areas. Concludes that Information Science is a new but rapidly growing field pushing the frontiers of human knowledge and, thus, contributing to human well-being and progress. (Author). |
| data science major usc: Ranked Set Sampling Munir Ahmad, M. Hanif, Hassen A. Muttlak, 2010-09-13 Ranked Set Sampling is one of the new areas of study in this region of the world and is a growing subject of research. Recently, researchers have paid attention to the development of the types of sampling; though it was not welcome in the beginning, it has numerous advantages over the classical sampling techniques. Ranked Set Sampling is doubly random and can be used in any survey designs. The Pakistan Journal of Statistics had attracted statisticians and samplers around the world to write up aspects of Ranked Set Sampling. All of the essays in this book have been reviewed by many critics. This volume can be used as a reference book for postgraduate students in economics, social sciences, medical and biological sciences, and statistics. The subject is still a hot topic for MPhil and PhD students for their dissertations. |
| data science major usc: South Central Dreams Pierrette Hondagneu-Sotelo, Manuel Pastor, 2021-07-13 Race, place, and identity in a changing urban America Over the last five decades, South Los Angeles has undergone a remarkable demographic transition. In South Central Dreams, eminent scholars Pierrette Hondagneu-Sotelo and Manuel Pastor follow its transformation from a historically Black neighborhood into a predominantly Latino one, providing a fresh, inside look at the fascinating—and constantly changing—relationships between these two racial and ethnic groups in California. Drawing on almost two hundred interviews and statistical data, Hondagneu-Sotelo and Pastor explore the experiences of first- and second-generation Latino residents, their long-time Black neighbors, and local civic leaders seeking to build coalitions. Acknowledging early tensions between Black and Brown communities. they show how Latino immigrants settled into a new country and a new neighborhood, finding various ways to co-exist, cooperate, and, most recently, demonstrate Black-Brown solidarity at a time when both racial and ethnic communities have come under threat. Hondagneu-Sotelo and Pastor show how Latino and Black residents have practiced, and adapted innovative strategies of belonging in a historically Black context, ultimately crafting a new route to place-based identity and political representation. South Central Dreams illuminates how racial and ethnic demographic shifts—as well as the search for identity and belonging—are dramatically shaping American cities and neighborhoods around the country. |
| data science major usc: Practical Analytics Nitin Kale, Nancy Jones, 2016-01-31 Practical Analytics covers analytics concepts and activities in a way that provides real-world skill building while reinforcing fundamental concepts. This book provides a much needed approach to analytics through theory, applications, and hands-on experience using the latest industry tools. This book providea a comprehensive and self-contained overview of analytics. The reader will be able to learn and apply all the concepts in the book without excessive prerequisites. |
| data science major usc: Earned Degrees Conferred Mary Diederich Ott, Curtis O. Baker, 1977 |
| data science major usc: Pandas for Everyone Daniel Y. Chen, 2017-12-15 The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning |
| data science major usc: Machine Learning Steven W. Knox, 2018-04-17 AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS PROSE Award Finalist 2019 Association of American Publishers Award for Professional and Scholarly Excellence Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection— essential elements of most applied projects. This important resource: Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods Presents R source code which shows how to apply and interpret many of the techniques covered Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions Contains useful information for effectively communicating with clients A volume in the popular Wiley Series in Probability and Statistics, Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning. STEVEN W. KNOX holds a Ph.D. in Mathematics from the University of Illinois and an M.S. in Statistics from Carnegie Mellon University. He has over twenty years’ experience in using Machine Learning, Statistics, and Mathematics to solve real-world problems. He currently serves as Technical Director of Mathematics Research and Senior Advocate for Data Science at the National Security Agency. |
| data science major usc: Data-Intensive Computing Ian Gorton, Deborah K. Gracio, 2012-10-29 The world is awash with digital data from social networks, blogs, business, science and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice. |
| data science major usc: The Promise and Peril of Big Data David Bollier, 2010 |
| data science major usc: Valuepack Thomas Connolly, 2005-08-01 |
| data science major usc: Strategies to Combat Homelessness , 2000 |
| data science major usc: Natural Language Processing for Social Media Atefeh Farzindar, Diana Inkpen, 2017-12-15 In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on NLP tools and methods for processing the non-traditional information from social media data that is available in large amounts (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, healthcare, business intelligence, industry, marketing, and security and defence. We review the existing evaluation metrics for NLP and social media applications, and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, virtual reality, and social networking. In this second edition, we have added information about recent progress in the tasks and applications presented in the first edition. We discuss new methods and their results. The number of research projects and publications that use social media data is constantly increasing due to continuously growing amounts of social media data and the need to automatically process them. We have added 85 new references to the more than 300 references from the first edition. Besides updating each section, we have added a new application (digital marketing) to the section on media monitoring and we have augmented the section on healthcare applications with an extended discussion of recent research on detecting signs of mental illness from social media. |
| data science major usc: The McGraw-Hill 36-Hour Course: Lean Six Sigma Sheila Shaffie, Shahbaz Shahbazi, 2012-04-30 Reduce Operational Cost and Risk. |
| data science major usc: Sports Performance Measurement and Analytics Lorena Martin, 2016-02-03 A PRACTICAL, REAL-WORLD GUIDE TO ANALYTICS FOR THE 5 MAJOR SPORTS: FOOTBALL, BASKETBALL, BASEBALL, SOCCER, AND TENNIS GAIN A COMPETITIVE EDGE! This is the first real-world guide to building and using analytical models for measuring and assessing performance in the five major sports: football, basketball, baseball, soccer, and tennis. Unlike books that focus strictly on theory, this book brings together sports measurement and statistical analyses, demonstrating how to examine differences across sports as well as between player positions. This book will provide you with the tools for cutting-edge approaches you can extend to the sport of your choice. Expert Northwestern University data scientist, UC San Diego researcher, and competitive athlete, Lorena Martin shows how to use measures and apply statistical models to evaluate players, reduce injuries, and improve sports performance. You’ll learn how to leverage a deep understanding of each sport’s principles, rules, attributes, measures, and performance outcomes. Sports Performance Measurement and Analytics will be an indispensable resource for anyone who wants to bring analytical rigor to athletic competition: students, professors, analysts, fans, physiologists, coaches, managers, and sports executives alike. All data sets, extensive code, and additional examples are available for download at http://www.ftpress.com/martin/ What are the qualities a person must have to become a world-class athlete? This question and many more can be answered through research, measurement, statistics, and analytics. This book gives athletes, trainers, coaches, and managers a better understanding of measurement and analytics as they relate to sports performance. To develop accurate measures, we need to know what we want to measure and why. There is great power in accurate measures and statistics. Research findings can show us how to prevent injuries, evaluate strengths and weaknesses, improve team cohesion, and optimize sports performance. This book serves many readers. People involved with sports will gain an appreciation for performance measures and analytics. People involved with analytics will gain new insights into quantified values representing physical, physiological, and psychological components of sports performance. And students eager to learn about sports analytics will have a practical introduction to the field. This is a thorough introduction to performance measurement and analytics for five of the world’s leading sports. The only book of its kind, it offers a complete overview of the most important concepts, rules, measurements, and statistics for each sport, while demonstrating applications of real-world analytics. You’ll find practical, state-of-the-art guidance on predicting future outcomes, evaluating an athlete’s market value, and more. |
| data science major usc: Web Technologies and Applications Weihong Han, Zi Huang, Changjun Hu, Hongli Zhang, Li Guo, 2014-08-15 This book constitutes the refereed proceedings of the workshops held at the 16th Asia-Pacific Web Conference, APWeb 2014, in Changsha, China, in September 2014. The 34 full papers were carefully reviewed and selected from 59 submissions. This volume presents the papers that have been accepted for the following workshops: First International Workshop on Social Network Analysis, SNA 2014; First International Workshop on Network and Information Security, NIS 2014; First International Workshop on Internet of Things Search, IoTS 2014. The papers cover various issues in social network analysis, security and information retrieval against the heterogeneous big data. |
| data science major usc: American Shtetl Nomi M. Stolzenberg, David N. Myers, 2022-02-08 A compelling account of how a group of Hasidic Jews established its own local government on American soil Settled in the mid-1970s by a small contingent of Hasidic families, Kiryas Joel is an American town with few parallels in Jewish history—but many precedents among religious communities in the United States. This book tells the story of how this group of pious, Yiddish-speaking Jews has grown to become a thriving insular enclave and a powerful local government in upstate New York. While rejecting the norms of mainstream American society, Kiryas Joel has been stunningly successful in creating a world apart by using the very instruments of secular political and legal power that it disavows. Nomi Stolzenberg and David Myers paint a richly textured portrait of daily life in Kiryas Joel, exploring the community's guiding religious, social, and economic norms. They delve into the roots of Satmar Hasidism and its charismatic founder, Rebbe Joel Teitelbaum, following his journey from nineteenth-century Hungary to post–World War II Brooklyn, where he dreamed of founding an ideal Jewish town modeled on the shtetls of eastern Europe. Stolzenberg and Myers chart the rise of Kiryas Joel as an official municipality with its own elected local government. They show how constant legal and political battles defined and even bolstered the community, whose very success has coincided with the rise of political conservatism and multiculturalism in American society over the past forty years. Timely and accessible, American Shtetl unravels the strands of cultural and legal conflict that gave rise to one of the most vibrant religious communities in America, and reveals a way of life shaped by both self-segregation and unwitting assimilation. |
| data science major usc: Mathletics Wayne L. Winston, 2012-03-18 How math can be used to improve performance and predict outcomes in professional sports Mathletics is a remarkably entertaining book that shows readers how to use simple mathematics to analyze a range of statistical and probability-related questions in professional baseball, basketball, and football, and in sports gambling. How does professional baseball evaluate hitters? Is a singles hitter like Wade Boggs more valuable than a power hitter like David Ortiz? Should NFL teams pass or run more often on first downs? Could professional basketball have used statistics to expose the crooked referee Tim Donaghy? Does money buy performance in professional sports? In Mathletics, Wayne Winston describes the mathematical methods that top coaches and managers use to evaluate players and improve team performance, and gives math enthusiasts the practical tools they need to enhance their understanding and enjoyment of their favorite sports—and maybe even gain the outside edge to winning bets. Mathletics blends fun math problems with sports stories of actual games, teams, and players, along with personal anecdotes from Winston's work as a sports consultant. Winston uses easy-to-read tables and illustrations to illuminate the techniques and ideas he presents, and all the necessary math concepts—such as arithmetic, basic statistics and probability, and Monte Carlo simulations—are fully explained in the examples. After reading Mathletics, you will understand why baseball teams should almost never bunt, why football overtime systems are unfair, why points, rebounds, and assists aren't enough to determine who's the NBA's best player—and much, much more. In a new epilogue, Winston discusses the stats and numerical analysis behind some recent sporting events, such as how the Dallas Mavericks used analytics to become the 2011 NBA champions. |
| data science major usc: The Scientist in the Crib Alison Gopnik, Andrew N. Meltzoff, Patricia Katherine Kuhl, 1999 A review of research on learning and infancy, drawn from hundreds of case studies, shows how children by the age of three are virtual learning machines and discusses how parents can help this learning process. |
| data science major usc: Installing Efficiency Methods Charles Edward Knoeppel, 1915 |
| data science major usc: The American Research University from World War II to World Wide Web Charles M. Vest, 2007-06-01 Forty years after Clark Kerr coined the term multiversity, the American research university has continued to evolve into a complex force for social and economic good. This volume provides a unique opportunity to explore the current state of the research university system. Charles M. Vest, one of the leading advocates for autonomy for American higher education, offers a multifaceted view of the university at the beginning of a new century. With a complex mission and funding structure, the university finds its international openness challenged by new security concerns and its ability to contribute to worldwide opportunity through sharing and collaboration dramatically expanded by the Internet. In particular, Vest addresses the need to nurture broad access to our universities and stay true to the fundamental mission of creating opportunity. |
| data science major usc: Supply Chain and Logistics Management Made Easy Paul Myerson, 2015 This easy guide introduces the modern field of supply chain and logistics management, explains why it is central to business success, shows how its pieces fit together, and presents best practices you can use wherever you work. Myerson explains key concepts, tools, and applications in clear, simple language, with intuitive examples that make sense to any student or professional. |
| data science major usc: Coded Computing Songze Li, A. Salman Avestimehr, 2020 We introduce the concept of “coded computing”, a novel computing paradigm that utilizes coding theory to effectively inject and leverage data/computation redundancy to mitigate several fundamental bottlenecks in large-scale distributed computing, namely communication bandwidth, straggler’s (i.e., slow or failing nodes) delay, privacy and security bottlenecks. |
| data science major usc: The Atlas of AI Kate Crawford, 2021-04-06 The hidden costs of artificial intelligence, from natural resources and labor to privacy and freedom What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequality. Drawing on more than a decade of research, award-winning science, and technology, Crawford reveals how AI is a technology of extraction: from the energy and minerals needed to build and sustain its infrastructure, to the exploited workers behind automated services, to the data AI collects from us. Rather than taking a narrow focus on code and algorithms, Crawford offers us a political and a material perspective on what it takes to make artificial intelligence and where it goes wrong. While technical systems present a veneer of objectivity, they are always systems of power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world. |
| data science major usc: Artificial Intelligence in Society OECD, 2019-06-11 The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises. |
| data science major usc: Strengthening Deep Neural Networks Katy Warr, 2019-07-03 As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately fool them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come |
| data science major usc: Fit to be Citizens? Natalia Molina, 2006 Shows how science and public health shaped the meaning of race in the early twentieth century. Examining the experiences of Mexican, Japanese, and Chinese immigrants in Los Angeles, this book illustrates the ways health officials used complexly constructed concerns about public health to demean, diminish, discipline, and define racial groups. |
| data science major usc: The Tortilla Curtain T. Coraghessan Boyle, 2011 The lives of two different couples--wealthy Los Angeles liberals Delaney and Kyra Mossbacher, and Candido and America Rincon, a pair of Mexican illegals--suddenly collide, in a story that unfolds from the shifting viewpoints of the various characters. |
| data science major usc: Workflows for e-Science Ian J. Taylor, Ewa Deelman, Dennis B. Gannon, Matthew Shields, 2007-12-31 This is a timely book presenting an overview of the current state-of-the-art within established projects, presenting many different aspects of workflow from users to tool builders. It provides an overview of active research, from a number of different perspectives. It includes theoretical aspects of workflow and deals with workflow for e-Science as opposed to e-Commerce. The topics covered will be of interest to a wide range of practitioners. |
| data science major usc: Industrial Arts Index , 1920 |
| data science major usc: Heroes and Scoundrels Matthew C. Ehrlich, Joe Saltzman, 2015-03-15 Whether it's the rule-defying lifer, the sharp-witted female newshound, or the irascible editor in chief, journalists in popular culture have shaped our views of the press and its role in a free society since mass culture arose over a century ago. Drawing on portrayals of journalists in television, film, radio, novels, comics, plays, and other media, Matthew C. Ehrlich and Joe Saltzman survey how popular media has depicted the profession across time. Their creative use of media artifacts provides thought-provoking forays into such fundamental issues as how pop culture mythologizes and demythologizes key events in journalism history and how it confronts issues of race, gender, and sexual orientation on the job. From Network to The Wire, from Lois Lane to Mikael Blomkvist, Heroes and Scoundrels reveals how portrayals of journalism's relationship to history, professionalism, power, image, and war influence our thinking and the very practice of democracy. |
| data science major usc: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 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. |
| data science major usc: The Cartoon Introduction to Statistics Grady Klein, Alan Dabney, 2013-07-02 The Cartoon Introduction to Statistics is the most imaginative and accessible introductory statistics course you'll ever take. Employing an irresistible cast of dragon-riding Vikings, lizard-throwing giants, and feuding aliens, the renowned illustrator Grady Klein and the award-winning statistician Alan Dabney teach you how to collect reliable data, make confident statements based on limited information, and judge the usefulness of polls and the other numbers that you're bombarded with every day. If you want to go beyond the basics, they've created the ultimate resource: The Math Cave, where they reveal the more advanced formulas and concepts. Timely, authoritative, and hilarious, The Cartoon Introduction to Statistics is an essential guide for anyone who wants to better navigate our data-driven world. |
| data science major usc: Newgotiation For Public Administration Professionals Yann Duzert, Frank V Zerunyan, 2019-07-23 Newgotiation for Public Administration Professionals conveys practical tools for students, executives, public and private administrators, managers and professionals to improve performance and relationships in this highly competitive and global marketplace. While the book is oriented towards Public Administration Professionals, the principles taught inside can apply almost anywhere. As you'll soon discover, authors Yann Duzert, Ph.D. and Frank Zerunyan, J.D. have coined the term newgotiation to describe their methodological approach to negotiation. The groundbreaking Newgotiation process involves reframing negotiation practices around the principles of collaboration, building relationships, and gaining (and maintaining) trust--which provides the parties with a new, more effective way to negotiate. Inside, you'll learn all about the 4-10-10 Newgotiation technique. This innovative approach to negotiation teaches practitioners the skills to apply four simple steps to ten elements and ten indicators for implementation and evaluation. With this approach, the authors of this book have created a common negotiation process that can be used by anyone. The 4-10-10 Newgotiation technique was developed to be a unified dialect, helping both practitioners and organizations speak the same language. Each party to the Newgotiation process is encouraged to engage in moments of reflection alternating with moments of action, which is designed to end in a win/win for both parties. Newgotiation methodology is all about identifying the frame of the negotiation, potential problems, crafting solutions, and structuring value creation and value distribution based on organizational priorities. The Newgotiation technique is designed to improve: The Probability to close a better deal The Value of a deal by inventing The Productivity of a deal through collaboration With the knowledge gained in this book, you'll be in a better position to have more successful negotiation outcomes. The invaluable 4-10-10 Newgotiation technique will quickly have you negotiating your way to better deals, with many other benefits along the way. |
| data science major usc: Bioinformatics Algorithms Phillip Compeau, Pavel Pevzner, 1986-06 Bioinformatics Algorithms: an Active Learning Approach is one of the first textbooks to emerge from the recent Massive Online Open Course (MOOC) revolution. A light-hearted and analogy-filled companion to the authors' acclaimed online course (http://coursera.org/course/bioinformatics), this book presents students with a dynamic approach to learning bioinformatics. It strikes a unique balance between practical challenges in modern biology and fundamental algorithmic ideas, thus capturing the interest of students of biology and computer science students alike.Each chapter begins with a central biological question, such as Are There Fragile Regions in the Human Genome? or Which DNA Patterns Play the Role of Molecular Clocks? and then steadily develops the algorithmic sophistication required to answer this question. Hundreds of exercises are incorporated directly into the text as soon as they are needed; readers can test their knowledge through automated coding challenges on Rosalind (http://rosalind.info), an online platform for learning bioinformatics.The textbook website (http://bioinformaticsalgorithms.org) directs readers toward additional educational materials, including video lectures and PowerPoint slides. |
| data science major usc: The Endangered Species Act Stanford Environmental Law Society, 2001 This handbook is a guide to the federal Endangered Species Act, the primary U.S. law aimed at protecting species of animals and plants from human threats to their survival. It is intended for lawyers, government agency employees, students, community activists, businesspeople, and any citizen who wants to understand the Act--its history, provisions, accomplishments, and failures. |
| data science major usc: Occupational Science for Occupational Therapy Doris E. Pierce, 2014 Occupational Science for Occupational Therapy shows how different types of occupational science research support occupational therapy. The book is research based and moves firmly away from presenting theories and models that are unsupported by research within the field. The book regards occupational therapy as actively involved in producing a science highly responsive to its knowledge needs, instead of as a profession that consumes and applies research that is produced within other disciplines and for other purposes--Provided by publisher. |
| data science major usc: Communicating at Work Ronald B. Adler, Ronald Brian Adler, Jeanne Marquardt Elmhorst, Kristen Lucas, 2012-10 The 11th edition of Communicating at Work enhances the strategic approach, real-world practicality, and reader-friendly voice that have made this text the market leader for three decades. On every page, students learn how to communicate in ways that enhance their own career success and help their organization operate effectively. This edition retains the hallmark features that have been praised by faculty and students--a strong emphasis on ethical communication and cultural diversity, discussions of evolving communication technologies, and self-assessment tools--while incorporating important updates and ground-breaking digital teaching and learning tools to help students better connect to the course material and apply it to real world business situations. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with minimum time …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, released in …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process from …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical barriers …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be collected, …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with minimum time …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, released in …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process from …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical barriers …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be collected, …
