#hardcover #nonfiction #predictions #statistics #economics #data #datascience #analytics #politics #socialscience

Hardcover (1 to 2 lbs)

Michigan

Why So Many Predictions Fail-But Some Don't

UPDATED FOR 2020 WITH A NEW PREFACE BY NATE SILVER "One of the more momentous books of the decade." —The New York Times Book Review Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future. In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball to global pandemics, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise. With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.

The Signal and the Noise

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#datascience #salsburg #statistics #college #paperback 

Paperback (1 to 2 lbs)

Minnesota

Science

How Statistics Revolutionized Science in the Twentieth Century

An insightful, revealing history of the magical mathematics that transformed our world. The Lady Tasting Tea is not a book of dry facts and figures, but the history of great individuals who dared to look at the world in a new way.

At a summer tea party in Cambridge, England, a guest states that tea poured into milk tastes different from milk poured into tea. Her notion is shouted down by the scientific minds of the group. But one man, Ronald Fisher, proposes to scientifically test the hypothesis. There is no better person to conduct such an experiment, for Fisher is a pioneer in the field of statistics.

The Lady Tasting Tea spotlights not only Fisher's theories but also the revolutionary ideas of dozens of men and women which affect our modern everyday lives. Writing with verve and wit, David Salsburg traces breakthroughs ranging from the rise and fall of Karl Pearson's theories to the methods of quality control that rebuilt postwar Japan's economy, including a pivotal early study on the capacity of a small beer cask at the Guinness brewing factory. Brimming with intriguing tidbits and colorful characters, The Lady Tasting Tea salutes the spirit of those who dared to look at the world in a new way.

The Lady Tasting Tea

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Light Paperback (1 lb or less)

Texas

Stripping the Dread from the Data

A New York Times bestseller

"Brilliant, funny...the best math teacher you never had." --San Francisco Chronicle


Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called "sexy." From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you'll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.

For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.

And in Wheelan's trademark style, there's not a dull page in sight. You'll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let's Make a Deal--and you'll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

Naked Statistics

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South Carolina

Computer Science

13th IFIP WG 5. 11 International Symposium, ISESS 2020, Wageningen, the Netherlands, February 5-7, 2020, Proceedings

This book constitutes the refereed proceedings of the 13th IFIP WG 5.11 International Symposium on Environmental Software Systems, ISESS 2020, held in Wageningen, The Netherlands, in February 2020.

The 22 full papers and 3 short papers were carefully reviewed and selected from 29 submissions. The papers cover a wide range of topics on environmental informatics, including data mining, artificial intelligence, high performance and cloud computing, visualization and smart sensing for environmental, earth, agricultural and food applications.

Environmental Software Systems. Data Science in Action

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Freakonomics with a unique and fun cover. Read entertaining anecdotes about real life applications in economics. #paperback #economics #freakonomics #podcast #datascience #freaky #nonfiction #science #rare

Idaho

A Rogue Economist Explores the Hidden Side of Everything

Which is more dangerous, a gun or a swimming pool? What do schoolteachers and sumo wrestlers have in common? Why do drug dealers still live with their moms? How much do parents really matter? What kind of impact did Roe v. Wade have on violent crime? These may not sound like typical questions for an economist to ask--but Levitt is not a typical economist. He studies the stuff and riddles of everyday life--from cheating and crime to sports and child rearing--and his conclusions regularly turn the conventional wisdom on its head. The authors show that economics is, at root, the study of incentives--how people get what they want, or need, especially when other people want or need the same thing. In this book, they set out to explore the hidden side of everything. If morality represents how we would like the world to work, then economics represents how it actually does work.--From publisher description.

Freakonomics

7e1a0622-928f-4105-ad17-d4ae12cc4f5d-1

Charles Wheelan

Some highlights and underlines. #nonfiction #hardcover #business #economics #data #science #datascience #math

New Jersey

The Key to Managing Chaos

Understanding Variation

e85c263e-13e6-4e7e-93f7-b96a25fbeaa8

#hardcover #nonfiction #predictions #math #bigdata #datascience it's in pretty good condition. Some of the pages were dogeared and I tried to show that damage 

Massachusetts

#datascience #machinelearning #engineering #ai #ml #oreilley

California

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. 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

Data Science from Scratch

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Math

Straight Talk from the Frontline

A guide to the usefulness of data science covers such topics as algorithms, logistic regression, financial modeling, data visualization, and data engineering.

Doing Data Science

709571de-5b1a-4608-a50f-de7065bf1e3f-1

Heavy Hardcover (2 to 4 lbs)

Fundamentals of Nonlinear Digital Filtering is the first book of its kind, presenting and evaluating current methods and applications in nonlinear digital filtering. Written for professors, researchers, and application engineers, as well as for serious students of signal processing, this is the only book available that functions as both a reference handbook and a textbook. Solid introductory material, balanced coverage of theoretical and practical aspects, and dozens of examples provide you with a self-contained, comprehensive information source on nonlinear filtering and its applications.

Fundamentals of Nonlinear Digital Filtering

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#hardcover #selfhelp #nonfiction #Economics #bigdata #datascience #socialscience #data

Using Data to Get What You Really Want in Life

"Seth Stephens-Davidowitz is more than a data scientist. He is a prophet for how to use the data revolution to reimagine your life. Don't Trust Your Gut is a tour de force--an intoxicating blend of analysis, humor, and humanity." -- Daniel H. Pink, #1 New York Times bestselling author of When, Drive, and To Sell Is Human

Big decisions are hard. We consult friends and family, make sense of confusing "expert" advice online, maybe we read a self-help book to guide us. In the end, we usually just do what feels right, pursuing high stakes self-improvement--such as who we marry, how to date, where to live, what makes us happy--based solely on what our gut instinct tells us. But what if our gut is wrong? Biased, unpredictable, and misinformed, our gut, it turns out, is not all that reliable. And data can prove this.

In Don't Trust Your Gut, economist, former Google data scientist, and New York Times bestselling author Seth Stephens-Davidowitz reveals just how wrong we really are when it comes to improving our own lives. In the past decade, scholars have mined enormous datasets to find remarkable new approaches to life's biggest self-help puzzles. Data from hundreds of thousands of dating profiles have revealed surprising successful strategies to get a date; data from hundreds of millions of tax records have uncovered the best places to raise children; data from millions of career trajectories have found previously unknown reasons why some rise to the top.

Telling fascinating, unexpected stories with these numbers and the latest big data research, Stephens-Davidowitz exposes that, while we often think we know how to better ourselves, the numbers disagree. Hard facts and figures consistently contradict our instincts and demonstrate self-help that actually works--whether it involves the best time in life to start a business or how happy it actually makes us to skip a friend's birthday party for a night of Netflix on the couch. From the boring careers that produce the most wealth, to the old-school, data-backed relationship advice so well-worn it's become a literal joke, he unearths the startling conclusions that the right data can teach us about who we are and what will make our lives better.

Lively, engrossing, and provocative, the end result opens up a new world of self-improvement made possible with massive troves of data. Packed with fresh, entertaining insights, Don't Trust Your Gut redefines how to tackle our most consequential choices, one that hacks the market inefficiencies of life and leads us to make smarter decisions about how to improve our lives. Because in the end, the numbers don't lie.

Don't Trust Your Gut

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#datascience #machinelearning #engineering #ai #ml #oreilley #computervision

Heavy Paperback (2 to 4 lbs)

A Geometric Viewpoint

This monograph by one of the world's leading vision researchers provides a thorough, mathematically rigorous exposition of a broad and vital area in computer vision: the problems and techniques related to three-dimensional (stereo) vision and motion. The emphasis is on using geometry to solve problems in stereo and motion, with examples from navigation and object recognition. Faugeras takes up such important problems in computer vision as projective geometry, camera calibration, edge detection, stereo vision (with many examples on real images), different kinds of representations and transformations (especially 3-D rotations), uncertainty and methods of addressing it, and object representation and recognition. His theoretical account is illustrated with the results of actual working programs.Three-Dimensional Computer Vision proposes solutions to problems arising from a specific robotics scenario in which a system must perceive and act. Moving about an unknown environment, the system has to avoid static and mobile obstacles, build models of objects and places in order to be able to recognize and locate them, and characterize its own motion and that of moving objects, by providing descriptions of the corresponding three-dimensional motions. The ideas generated, however, can be used indifferent settings, resulting in a general book on computer vision that reveals the fascinating relationship of three-dimensional geometry and the imaging process.

Three-Dimensional Computer Vision

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Oversized Paperback (4 to 6 lbs)

Learning to Program with AI, Big Data and the Cloud

For introductory-level Python programming and/or data-science courses.

A groundbreaking, flexible approach to computer science and data science

The Deitels' Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs), and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science.

The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.

Save this search

Nate Silver

The Signal and the Noise

David Salsburg

The Lady Tasting Tea

Charles Wheelan

Naked Statistics

Ioannis N. Athanasiadis (Editor); Steven P. Frysinger (Editor); Gerald Schimak (Editor); Willem Jan Knibbe (Editor)

Environmental Software Systems. Data Science in Action

Steven D. Levitt

Freakonomics

Charles Wheelan

Naked Statistics

Donald J. Wheeler

Understanding Variation

Nate Silver

The Signal and the Noise

Joel Grus

Data Science from Scratch

Cathy O'Neil; Rachel Schutt

Doing Data Science

Jaakko Astola; Pauli Kuosmanen

Fundamentals of Nonlinear Digital Filtering

Seth Stephens-Davidowitz

Don't Trust Your Gut

Olivier Faugeras

Three-Dimensional Computer Vision

Paul Deitel; Harvey Deitel

Intro to Python for Computer Science and Data Science