With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools.RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You'll gain valuable programming skills and support your work as a data scientist at the same time.Work hands-on with three practical data analysis projects based on casino gamesStore, retrieve, and change data values in your computer's memoryWrite programs and simulations that outperform those written by typical R usersUse R programming tools such as if else statements, for loops, and S3 classesLearn how to write lightning-fast vectorized R codeTake advantage of R's package system and debugging toolsPractice and apply R programming concepts as you learn them

    In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak.In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jorn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science.An irreverent review of econometric essentials A focus on tools that applied researchers use most Chapters on regression-discontinuity designs, quantile regression, and standard errors Many empirical examples A clear and concise resource with wide applications

    Internet giants, leading retailers, and other firms are voraciously gathering data with little oversight from anyone.In Las Vegas, no company knows the value of data better than Caesars Entertainment. Many thousands of enthusiastic clients pour through the ever-open doors of their casinos. The secret to the company’s success lies in their one unrivaled asset: they know their clients intimately by tracking the activities of the overwhelming majority of gamblers. They know exactly what games they like to play, what foods they enjoy for breakfast, when they prefer to visit, who their favorite hostess might be, and exactly how to keep them coming back for more.Caesars’ dogged data-gathering methods have been so successful that they have grown to become the world’s largest casino operator, and have inspired companies of all kinds to ramp up their own data mining in the hopes of boosting their targeted marketing efforts. Some do this themselves. Some rely on data brokers. Others clearly enter a moral gray zone that should make American consumers deeply uncomfortable.We live in an age when our personal information is harvested and aggregated whether we like it or not. And it is growing ever more difficult for those businesses that choose not to engage in more intrusive data gathering to compete with those that do. Tanner’s timely warning resounds: Yes, there are many benefits to the free flow of all this data, but there is a dark, unregulated, and destructive netherworld as well.

    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.

    In addition to changing how we live, these tools enable us to collect and process data about human behavior on a scale never before imaginable, offering entirely new approaches to core questions about social behavior. Bit by Bit is the key to unlocking these powerful methods--a landmark book that will fundamentally change how the next generation of social scientists and data scientists explores the world around us.Bit by Bit is the essential guide to mastering the key principles of doing social research in this fast-evolving digital age. In this comprehensive yet accessible book, Matthew Salganik explains how the digital revolution is transforming how social scientists observe behavior, ask questions, run experiments, and engage in mass collaborations. He provides a wealth of real-world examples throughout and also lays out a principles-based approach to handling ethical challenges.Bit by Bit is an invaluable resource for social scientists who want to harness the research potential of big data and a must-read for data scientists interested in applying the lessons of social science to tomorrow's technologies.Illustrates important ideas with examples of outstanding researchCombines ideas from social science and data science in an accessible style and without jargonGoes beyond the analysis of "found" data to discuss the collection of "designed" data such as surveys, experiments, and mass collaborationFeatures an entire chapter on ethicsIncludes extensive suggestions for further reading and activities for the classroom or self-study

    Written by A330 Captain, Bill Palmer, this book opens to understanding the actions of the crew, how they failed to understand and control the problem, and how the airplane works and the part it played. All in easy to understand terms.Addressed are the many contributing aspects of weather, human factors, and airplane system operation and design that the crew could not recover from. How each contributed is covered in detail along with what has been done, and needs to be done in the future to prevent this from happening again.Also see the book's companion website: UnderstandingAF447.com for supplemental materials referred to in the book or to contact the author.

    It adopts a novel approach, by using the programming language c to teach data structures. The book discusses concepts like arrays, algorithm analysis, strings, queues, trees and graphs. Well-designed animations related to these concepts are provided in the cd-rom which accompanies the book. This enables the reader to get a better understanding of the complex procedures described in the book through a visual demonstration of the same. Data structure through c is a comprehensive book which can be used as a reference book by students as well as computer professionals. It is written in a clear, easy-to-understood manner and it includes several programs and examples to explain clearly the complicated concepts related to data structures. The book was published by bpb publications in 2003 and is available in paperback. Key features: the book contains example programs that elucidate the concepts. It comes with a cd that visually demonstrates the theory presented in the book.

    Employing a methodology that is primarily learning by example and “thinking with our eyes,” this manual features graphs and practical analytical techniques that can be applied to a broad range of data analysis tools—including the most commonly used Microsoft Excel. This approach is particularly valuable to those who need to make sense of quantitative business data by discerning meaningful patterns, trends, relationships, and exceptions that reveal business performance, potential problems and opportunities, and hints about the future. It provides practical skills that are useful to managers at all levels and to those interested in keeping a keen eye on their business.

    Intuitive Biostatistics covers all the topics typically found in an introductory statistics text, but with the emphasis on confidence intervals rather than P values, making it easier for students to understand both. Additionally, it introduces a broad range of topics left out of most other introductory texts but used frequently in biomedical publications, including survival curves. multiple comparisons, sensitivity and specificity of lab tests, Bayesian thinking, lod scores, and logistic, proportional hazards and nonlinear regression. By emphasizing interpretation rather than calculation, this text provides a clear and virtually painless introduction to statistical principles for those students who will need to use statistics constantly in their work. In addition, its practical approach enables readers to understand the statistical results published in biological and medical journals.

    The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

    The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.About the TechnologyR is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data.About the BookR in Action is a language tutorial focused on practical problems. It presents useful statistics examples and includes elegant methods for handling messy, incomplete, and non-normal data that are difficult to analyze using traditional methods. And statistical analysis is only part of the story. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's InsidePractical data analysis, step by stepInterfacing R with other softwareUsing R to visualize dataOver 130 graphsEight reference appendixes================================Table of ContentsPart I Getting startedIntroduction to RCreating a datasetGetting started with graphsBasic data managementAdvanced data managementPart II Basic methodsBasic graphsBasic statisticsPart III Intermediate methodsRegressionAnalysis of variancePower analysisIntermediate graphsRe-sampling statistics and bootstrappingPart IV Advanced methodsGeneralized linear modelsPrincipal components and factor analysisAdvanced methods for missing dataAdvanced graphics

    Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started.Use R's default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    It provides a thorough understanding of the fundamentals of Data Warehousing and aims to impart a sound knowledge to users for creating and managing a Data Warehouse.The book introduces the various features and architecture of a Data Warehouse followed by a detailed study of the Business Requirements and Dimensional Modelling. It goes on to discuss the components of a Data Warehouse and thereby leads up to the core area of the subject by providing a thorough understanding of the building and maintenance of a Data Warehouse. This is then followed up by an overview of planning and project management, testing and growth and then finishing with Data Warehouse solutions and the latest trends in this field. The book is finally rounded off with a broad overview of its related field of study, Data Mining. The text is ably supported by plenty of examples to illustrate concepts and contains several review questions and other end-chapter exercises to test the understanding of students. The book also carries a running case study that aims to bring out the practical aspects of the subject. This will be useful for students to master the basics and apply them to real-life scenario.

    Pure Substances, The First And Second Laws, Gases, Psychrometrics, The Vapor, Gas And Refrigeration Cycles, Heat Transfer, Compressible Flow, Chemical Reactions, Fuels, And More Are Presented In Detail And Enhanced With Practical Applications. This Version Presents The Material Using SI Units And Has Ample Material On SI Conversion, Steam Tables, And A Mollier Diagram. A CD-ROM, Included With The Print Version Of The Text, Includes A Fully Functional Version Of Quickfield (Widely Used In Industry), As Well As Numerous Demonstrations And Simulations With MATLAB, And Other Third Party Software.