Data Smart: Using Data Science to Transform Information into Insight


John W. Foreman - 2013
    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

Information Visualization: Perception for Design


Colin Ware - 2000
    Ware's updated review of empirical research and interface design examples will do much to accelerate innovation and adoption of information visualization." —Ben Shneiderman, University of Maryland"Colin Ware is the perfect person to write this book, with a long history of prominent contributions to the visual interaction with machines and to information visualization directly. It goes a long way towards joining science to the practical design of information visualization systems." —from the foreword by Stuart Card, PARCMost designers know that yellow text presented against a blue background reads clearly and easily, but how many can explain why, and what really are the best ways to help others and ourselves clearly see key patterns in a bunch of data? When we use software, access a web site, or view graphics, our understanding is greatly enhanced or impeded by the way information is presented. By explaining in detail how we think visually, this book provides guidance on how to construct effective interactive information displays.This book combines a strictly scientific approach to human perception with a practical concern for the rules governing the effective visual presentation of information. Surveying the research of leading psychologists and neurophysiologists, author Colin Ware isolates key principles at work in vision and perception, and from them derives specific and effective visualization techniques suitable for a wide range of scenarios. Information Visualization offers practical guidelines that can be applied by anyone, and covers all facets of visual perception: color, organization, space perception, motion, and texture.* Major revision of this classic work, with a new chapter on visual thinking, new sections on face perception and flow visualization, an appendix on how to evaluate visualizations,and a greatly expanded chapter on color and color sequences. *New to this edition is the full-color treatment throughout, to better display over 400 illustrations.*From a leading researcher in the field of human perception who has brought together, in a single resource, all current scientific insight into the question of data visualization.

On Being a Data Skeptic


Cathy O'Neil - 2013
    Data is nuanced, and "a really excellent skeptic puts the term 'science' into 'data science.'" The big data revolution shouldn't be dismissed as hype, but current data science tools and models shouldn't be hailed as the end-all-be-all, either."

Information Graphics


Sandra Rendgen - 2011
    Considering this complex variety of data floating around us, sometimes the best — or even only — way to communicate is visually. This unique book presents a fascinating historical perspective on the subject, highlighting the work of the masters of the profession who have created a number of breakthroughs that have changed the way we communicate. Information Graphics has been conceived and designed not just for designers or graphics professionals, but for anyone interested in the history and practice of communicating visually. The in-depth introductory section, illustrated with over 60 images (each accompanied by an explanatory caption), features essays by Sandra Rendgen, Paolo Ciuccarelli, Richard Saul Wurman, and Simon Rogers; looking back all the way to primitive cave paintings as a means of communication, this introductory section gives readers an excellent overview of the subject. The second part of the book is entirely dedicated to contemporary works by the current most renowned professionals, presenting 200 graphics projects, with over 400 examples — each with a fact sheet and an explanation of methods and objectives — divided into chapters by the subjects Location, Time, Category, and Hierarchy.Features:200 projects and over 400 examples of contemporary information graphics from all over the world—ranging from journalism to art, government, education, business and much more Historical essays about the development of information graphics since its beginnings Exclusive poster (673 x 475 mm / 26.5 x 18.7 in) by Nigel Homes, who during his 20 years as graphics director for TIME revolutionized the way the magazine used information graphics

The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling


Ralph Kimball - 1996
    Here is a complete library of dimensional modeling techniques-- the most comprehensive collection ever written. Greatly expanded to cover both basic and advanced techniques for optimizing data warehouse design, this second edition to Ralph Kimball's classic guide is more than sixty percent updated.The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including:* Retail sales and e-commerce* Inventory management* Procurement* Order management* Customer relationship management (CRM)* Human resources management* Accounting* Financial services* Telecommunications and utilities* Education* Transportation* Health care and insuranceBy the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts.This book is also available as part of the Kimball's Data Warehouse Toolkit Classics Box Set (ISBN: 9780470479575) with the following 3 books:The Data Warehouse Toolkit, 2nd Edition (9780471200246)The Data Warehouse Lifecycle Toolkit, 2nd Edition (9780470149775)The Data Warehouse ETL Toolkit (9780764567575)

Beautiful Data: The Stories Behind Elegant Data Solutions (Theory In Practice, #31)


Toby Segaran - 2009
    Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video.With Beautiful Data, you will: Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web Learn how to visualize trends in urban crime, using maps and data mashups Discover the challenges of designing a data processing system that works within the constraints of space travel Learn how crowdsourcing and transparency have combined to advance the state of drug research Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data Learn about the massive infrastructure required to create, capture, and process DNA data That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:Nathan Yau Jonathan Follett and Matt Holm J.M. Hughes Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava Jeff Hammerbacher Jason Dykes and Jo Wood Jeff Jonas and Lisa Sokol Jud Valeski Alon Halevy and Jayant Madhavan Aaron Koblin with Valdean Klump Michal Migurski Jeff Heer Coco Krumme Peter Norvig Matt Wood and Ben Blackburne Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen Lukas Biewald and Brendan O'Connor Hadley Wickham, Deborah Swayne, and David Poole Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza Toby Segaran

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die


Eric Siegel - 2013
    Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: -What type of mortgage risk Chase Bank predicted before the recession. -Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. -Why early retirement decreases life expectancy and vegetarians miss fewer flights. -Five reasons why organizations predict death, including one health insurance company. -How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. -How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! -How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. -How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. -What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

Facts are Sacred: The power of data


Simon Rogers - 2011
    

Analyzing the Analyzers


Harlan Harris - 2013
    

Better Presentations: A Guide for Scholars, Researchers, and Wonks


Jonathan Schwabish - 2016
    Most of us approach this task by converting a written document into slides, but the result is often a text-heavy presentation saddled with bullet points, stock images, and graphs too complex for an audience to decipher--much less understand. Presenting is fundamentally different from writing, and with only a little more time, a little more effort, and a little more planning, you can communicate your work with force and clarity.Designed for presenters of scholarly or data-intensive content, "Better Presentations "details essential strategies for developing clear, sophisticated, and visually captivating presentations. Following three core principles--visualize, unify, and focus--"Better Presentations" describes how to visualize data effectively, find and use images appropriately, choose sensible fonts and colors, edit text for powerful delivery, and restructure a written argument for maximum engagement and persuasion. With a range of clear examples for what to do (and what not to do), the practical package offered in" Better Presentations" shares the best techniques to display work and the best tactics for winning over audiences. It pushes presenters past the frustration and intimidation of the process to more effective, memorable, and persuasive presentations.

Think Stats


Allen B. Downey - 2011
    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations


Isabel Meirelles - 2013
    Design for Information critically examines other design solutions —current and historic— helping you gain a larger understanding of how to solve specific problems. This book is designed to help you foster the development of a repertoire of existing methods and concepts to help you overcome design problems. Learn the ins and outs of data visualization with this informative book that provides you with a series of current visualization case studies. The visualizations discussed are analyzed for their design principles and methods, giving you valuable critical and analytical tools to further develop your design process. The case study format of this book is perfect for discussing  the histories, theories and best practices in the field through real-world, effective visualizations. The selection represents a fraction of effective visualizations that we encounter in this burgeoning field, allowing you the opportunity to extend your study to other solutions in your specific field(s) of practice. This book is also helpful to students in other disciplines who are involved with visualizing information, such as those in the digital humanities and most of the sciences.

The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios


Steve Wexler - 2017
    It's great to have theory and evidenced-based research at your disposal, but what will you do when somebody asks you to make your dashboard 'cooler' by adding packed bubbles and donut charts?The expert authors have a combined 30-plus years of hands-on experience helping people in hundreds of organizations build effective visualizations. They have fought many 'best practices' battles and having endured bring an uncommon empathy to help you, the reader of this book, survive and thrive in the data visualization world.A well-designed dashboard can point out risks, opportunities, and more; but common challenges and misconceptions can make your dashboard useless at best, and misleading at worst. The Big Book of Dashboards gives you the tools, guidance, and models you need to produce great dashboards that inform, enlighten, and engage.

Data Visualization: A Practical Introduction


Kieran Healy - 2018
    It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way.Data Visualization builds the reader's expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective "small multiple" plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible.Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings.Provides hands-on instruction using R and ggplot2Shows how the "tidyverse" of data analysis tools makes working with R easier and more consistentIncludes a library of data sets, code, and functions

Data Analytics Made Accessible


Anil Maheshwari - 2014
    It is a conversational book that feels easy and informative. This short and lucid book covers everything important, with concrete examples, and invites the reader to join this field. The chapters in the book are organized for a typical one-semester course. The book contains case-lets from real-world stories at the beginning of every chapter. There is a running case study across the chapters as exercises. This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms. Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others are attracted to the idea of discovering new insights and ideas from data. This book can also be gainfully used by executives, managers, analysts, professors, doctors, accountants, and other professionals to learn how to make sense of the data coming their way. This is a lucid flowing book that one can finish in one sitting, or can return to it again and again for insights and techniques. Table of Contents Chapter 1: Wholeness of Business Intelligence and Data Mining Chapter 2: Business Intelligence Concepts & Applications Chapter 3: Data Warehousing Chapter 4: Data Mining Chapter 5: Decision Trees Chapter 6: Regression Models Chapter 7: Artificial Neural Networks Chapter 8: Cluster Analysis Chapter 9: Association Rule Mining Chapter 10: Text Mining Chapter 11: Web Mining Chapter 12: Big Data Chapter 13: Data Modeling Primer Appendix: Data Mining Tutorial using Weka