If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. You'll learn how to:Determine which data sources to use for collecting information Assess data quality and distinguish signal from noise Build basic data models to illuminate patterns, and assimilate new information into the models Cope with ambiguous information Design experiments to test hypotheses and draw conclusions Use segmentation to organize your data within discrete market groups Visualize data distributions to reveal new relationships and persuade others Predict the future with sampling and probability models Clean your data to make it useful Communicate the results of your analysis to your audience Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Data Analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

    

    Anabel Garza: No school board would have put her forward as a model principal. Pregnant and alone at sixteen, widowed by twenty-five, Anabel got along teaching English to Mexican immigrants, raising her son, and taking night school classes.But then no model candidate would have taken the job at John H. Reagan High School. Once known to sports fans across Texas as the great champion Big Blue, Reagan was collapsing. The kids were failing the standardized tests, failing on the basketball court, failing even to show up. Teenage pregnancy was endemic. If the test scores and attendance did not improve, the school was set to close at the end of the 2009-10 school year.Anabel took the assignment. Her first work was triage. She cruised the malls for dropouts. She fired ten teachers, including one who produced a ruler to bemoan the distance from the parking lot to her classroom door. She listened to angry lectures from union officials and angrier ones from black ministers. She kept going. She tailored each student's tutoring to the standardized tests. The numbers started to come up.But with the state education commissioner threatening to close the school, the real work began. Anabel set out to re-create the high school she remembered, with plays and dances, yearbooks and clubs, teachers who brought books alive and crowded bleachers to cheer on the basketball team. She reached out to the middle schools, the neighborhoods, and the churches. She gave good teachers free rein. She mixed love and expectations.The circumstances facing Reagan High are playing out all over the country. The get-tough crowd of education reformers, led by Obama's secretary of education, are redoubling their efforts to replace public schools with charter companies. But what happens when the centerpiece of a community is threatened? And what happens when one person just won't quit?For the first time, we can tally the costs of rankings and scores. In this powerful rejoinder to the prevailing winds of American education policy, Michael Brick examines the do-or-die year at Reagan High. Compelling, character-driven narrative journalism, Saving the School pays an overdue tribute to the great American high school and to the people inside.

    And oh the black dress, the black heels and the push up bra--just in case!" What would you do if your sleazy boss ran his hands all over you and then blamed you for not doing your job well? Well, I just packed off on a road trip! Just me in my old jeep, Iqbal Mastani, we travelled all the way from Delhi to this little guest house up in the mountains. I met people I had never known (cute boys who taught me to fish), did things I would have never done (sleeping drunk in a cold balcony) and somewhere in the middle of that, fell in love! Lanka in all its diversity.

    Students, researchers, historians, specialists — in short, everyone with an interest in mathematics — will find it engrossing and stimulating.Beginning with the ancient Near East, the author traces the ideas and techniques developed in Egypt, Babylonia, China, and Arabia, looking into such manuscripts as the Egyptian Papyrus Rhind, the Ten Classics of China, and the Siddhantas of India. He considers Greek and Roman developments from their beginnings in Ionian rationalism to the fall of Constantinople; covers medieval European ideas and Renaissance trends; analyzes 17th- and 18th-century contributions; and offers an illuminating exposition of 19th century concepts. Every important figure in mathematical history is dealt with — Euclid, Archimedes, Diophantus, Omar Khayyam, Boethius, Fermat, Pascal, Newton, Leibniz, Fourier, Gauss, Riemann, Cantor, and many others.For this latest edition, Dr. Struik has both revised and updated the existing text, and also added a new chapter on the mathematics of the first half of the 20th century. Concise coverage is given to set theory, the influence of relativity and quantum theory, tensor calculus, the Lebesgue integral, the calculus of variations, and other important ideas and concepts. The book concludes with the beginnings of the computer era and the seminal work of von Neumann, Turing, Wiener, and others."The author's ability as a first-class historian as well as an able mathematician has enabled him to produce a work which is unquestionably one of the best." — Nature Magazine.

    The Authors Discuss The Mathematical Theory In Detail And Then Provide The Geometric Interpretation Necessary To Make 3D Math Intuitive. Working C++ Classes Illustrate How To Put The Techniques Into Practice, And Exercises At The End Of Each Chapter Help Reinforce The Concepts. This Book Explains Basic Concepts Such As Vectors, Coordinate Spaces, Matrices, Transformations, Euler Angles, Homogenous Coordinates, Geometric Primitives, Intersection Tests, And Triangle Meshes. It Discusses Orientation In 3D, Including Thorough Coverage Of Quaternions And A Comparison Of The Advantages And Disadvantages Of Different Representation Techniques. The Text Describes Working C++ Classes For Mathematical And Geometric Entities And Several Different Matrix Classes, Each Tailored To Specific Geometric Tasks. Also Included Are Complete Derivations For All The Primitive Transformation Matrices.

    There are equally many advanced textbooks which delve into the far reaches of statistical theory, while bypassing practical applications. But between these two approaches is an unfilled gap, in which theory and practice merge at an intermediate level. Professor M. G. Bulmer's Principles of Statistics, originally published in 1965, was created to fill that need. The new, corrected Dover edition of Principles of Statistics makes this invaluable mid-level text available once again for the classroom or for self-study.Principles of Statistics was created primarily for the student of natural sciences, the social scientist, the undergraduate mathematics student, or anyone familiar with the basics of mathematical language. It assumes no previous knowledge of statistics or probability; nor is extensive mathematical knowledge necessary beyond a familiarity with the fundamentals of differential and integral calculus. (The calculus is used primarily for ease of notation; skill in the techniques of integration is not necessary in order to understand the text.)Professor Bulmer devotes the first chapters to a concise, admirably clear description of basic terminology and fundamental statistical theory: abstract concepts of probability and their applications in dice games, Mendelian heredity, etc.; definitions and examples of discrete and continuous random variables; multivariate distributions and the descriptive tools used to delineate them; expected values; etc. The book then moves quickly to more advanced levels, as Professor Bulmer describes important distributions (binomial, Poisson, exponential, normal, etc.), tests of significance, statistical inference, point estimation, regression, and correlation. Dozens of exercises and problems appear at the end of various chapters, with answers provided at the back of the book. Also included are a number of statistical tables and selected references.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    Intuition and computational abilities are stressed. Original material on DE and multiple integrals has been expanded.

    It proposes enlightening similarities between, for instance, maneuvering in limited war and in a traffic jam; deterring the Russians and one's own children; the modern strategy of terror and the ancient institution of hostages.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    This revised edition includes a CD-Rom with exercises that will help the student have a better understanding of equations, formulas, etc.

    The discussion of problem solving strategies is extensive. It is written for trainers and participants of contests of all levels up to the highest level: IMO, Tournament of the Towns, and the noncalculus parts of the Putnam Competition. It will appeal to high school teachers conducting a mathematics club who need a range of simple to complex problems and to those instructors wishing to pose a "problem of the week", "problem of the month", and "research problem of the year" to their students, thus bringing a creative atmosphere into their classrooms with continuous discussions of mathematical problems. This volume is a must-have for instructors wishing to enrich their teaching with some interesting non-routine problems and for individuals who are just interested in solving difficult and challenging problems. Each chapter starts with typical examples illustrating the central concepts and is followed by a number of carefully selected problems and their solutions. Most of the solutions are complete, but some merely point to the road leading to the final solution. Very few problems have no solutions. Readers interested in increasing the effectiveness of the book can do so by working on the examples in addition to the problems thereby increasing the number of problems to over 1300. In addition to being a valuable resource of mathematical problems and solution strategies, this volume is the most complete training book on the market.

    A timeless introduction to the field and a landmark in symbolic logic, showing that classical logic can be treated algebraically.

    The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo MCMC. Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.