Getting in Front on Data

Getting in Front on Data

Author: Thomas C. Redman

Publisher:

Published: 2016

Total Pages: 0

ISBN-13: 9781634621267

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This book lays out the roles everyone, up and down the organization chart, can and must play to ensure that data is up to the demands of its use, in day-in, day-out work, decision-making, planning, and analytics. By now, everyone knows that bad data extorts an enormous toll, adding huge (though often hidden) costs, and making it more difficult to make good decisions and leverage advanced analyses. While the problems are pervasive and insidious, they are also solvable! As Tom Redman, "the Data Doc," explains in Getting in Front on Data, the secret lies in getting the right people in the right roles to "get in front" of the management and social issues that lead to bad data in the first place. Everyone should see himself or herself in this book. We are all both data customers and data creators--after all, we use data created by others and create data used by others. And all of us must step up to these roles. As data customers, we must clarify our most important needs and communicate them to data creators. As data creators, we must strive to meet those needs by finding and eliminating the root causes of error. Getting in Front on Data proposes new roles for data professionals as: embedded data managers, in helping data customers and creators complete their work, DQ team leads, in connecting customers and creators, pulling the entire program together, and training people on their new roles, data maestros, in providing deep expertise on the really tough problems, chief data architects, in establishing common data definitions, and technologists, in increasing scale and decreasing unit cost. Getting in Front on Data introduces a new role, the data provocateur, the motive force in attacking data quality properly! This book urges everyone to unleash their inner provocateur. Finally, it crystallizes what senior leaders must do if their entire organizations are to enjoy the benefits of high-quality data!


Book Synopsis Getting in Front on Data by : Thomas C. Redman

Download or read book Getting in Front on Data written by Thomas C. Redman and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book lays out the roles everyone, up and down the organization chart, can and must play to ensure that data is up to the demands of its use, in day-in, day-out work, decision-making, planning, and analytics. By now, everyone knows that bad data extorts an enormous toll, adding huge (though often hidden) costs, and making it more difficult to make good decisions and leverage advanced analyses. While the problems are pervasive and insidious, they are also solvable! As Tom Redman, "the Data Doc," explains in Getting in Front on Data, the secret lies in getting the right people in the right roles to "get in front" of the management and social issues that lead to bad data in the first place. Everyone should see himself or herself in this book. We are all both data customers and data creators--after all, we use data created by others and create data used by others. And all of us must step up to these roles. As data customers, we must clarify our most important needs and communicate them to data creators. As data creators, we must strive to meet those needs by finding and eliminating the root causes of error. Getting in Front on Data proposes new roles for data professionals as: embedded data managers, in helping data customers and creators complete their work, DQ team leads, in connecting customers and creators, pulling the entire program together, and training people on their new roles, data maestros, in providing deep expertise on the really tough problems, chief data architects, in establishing common data definitions, and technologists, in increasing scale and decreasing unit cost. Getting in Front on Data introduces a new role, the data provocateur, the motive force in attacking data quality properly! This book urges everyone to unleash their inner provocateur. Finally, it crystallizes what senior leaders must do if their entire organizations are to enjoy the benefits of high-quality data!


Data Smart

Data Smart

Author: John W. Foreman

Publisher: John Wiley & Sons

Published: 2013-10-31

Total Pages: 432

ISBN-13: 1118839862

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Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.


Book Synopsis Data Smart by : John W. Foreman

Download or read book Data Smart written by John W. Foreman and published by John Wiley & Sons. This book was released on 2013-10-31 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.


R for Data Science

R for Data Science

Author: Hadley Wickham

Publisher: "O'Reilly Media, Inc."

Published: 2016-12-12

Total Pages: 521

ISBN-13: 1491910364

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Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results


Book Synopsis R for Data Science by : Hadley Wickham

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results


Compilation of Public Opinion Data on Tolls and Road Pricing

Compilation of Public Opinion Data on Tolls and Road Pricing

Author: Johanna Zmud

Publisher: Transportation Research Board

Published: 2008

Total Pages: 65

ISBN-13: 0309098009

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Book Synopsis Compilation of Public Opinion Data on Tolls and Road Pricing by : Johanna Zmud

Download or read book Compilation of Public Opinion Data on Tolls and Road Pricing written by Johanna Zmud and published by Transportation Research Board. This book was released on 2008 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt:


Protocols for Collecting and Using Traffic Data in Bridge Design

Protocols for Collecting and Using Traffic Data in Bridge Design

Author: Bala Sivakumar

Publisher: Transportation Research Board

Published: 2011

Total Pages: 125

ISBN-13: 0309155479

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TRB's National Cooperative Highway Research Program (NCHRP) Report 683: Protocols for Collecting and Using Traffic Data in Bridge Design explores a set of protocols and methodologies for using available recent truck traffic data to develop and calibrate vehicular loads for superstructure design, fatigue design, deck design, and design for overload permits. The protocols are geared to address the collection, processing, and use of national weigh-in-motion (WIM) data. The report also gives practical examples of implementing these protocols with recent national WIM data drawn from states/sites around the country with different traffic exposures, load spectra, and truck configurations. The material in this report will be of immediate interest to bridge engineers. This report replaces NCHRP Web-Only Document 135: Protocols for Collecting and Using Traffic Data in Bridge Design. Appendices A through F for NCHRP Report 683 are available only online.


Book Synopsis Protocols for Collecting and Using Traffic Data in Bridge Design by : Bala Sivakumar

Download or read book Protocols for Collecting and Using Traffic Data in Bridge Design written by Bala Sivakumar and published by Transportation Research Board. This book was released on 2011 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRB's National Cooperative Highway Research Program (NCHRP) Report 683: Protocols for Collecting and Using Traffic Data in Bridge Design explores a set of protocols and methodologies for using available recent truck traffic data to develop and calibrate vehicular loads for superstructure design, fatigue design, deck design, and design for overload permits. The protocols are geared to address the collection, processing, and use of national weigh-in-motion (WIM) data. The report also gives practical examples of implementing these protocols with recent national WIM data drawn from states/sites around the country with different traffic exposures, load spectra, and truck configurations. The material in this report will be of immediate interest to bridge engineers. This report replaces NCHRP Web-Only Document 135: Protocols for Collecting and Using Traffic Data in Bridge Design. Appendices A through F for NCHRP Report 683 are available only online.


Leveraging ITS Data for Transit Market Research

Leveraging ITS Data for Transit Market Research

Author: James G. Strathman

Publisher: Transportation Research Board

Published: 2008

Total Pages: 92

ISBN-13: 0309099420

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TRB¿s Transit Cooperative Research Program (TCRP) Report 126: Leveraging ITS Data for Transit Market Research: A Practitioner¿s Guidebook examines intelligent transportation systems (ITS) and Transit ITS technologies currently in use, explores their potential to provide market research data, and presents methods for collecting and analyzing these data. The guidebook also highlights three case studies that illustrate how ITS data have been used to improve market research practices.


Book Synopsis Leveraging ITS Data for Transit Market Research by : James G. Strathman

Download or read book Leveraging ITS Data for Transit Market Research written by James G. Strathman and published by Transportation Research Board. This book was released on 2008 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRB¿s Transit Cooperative Research Program (TCRP) Report 126: Leveraging ITS Data for Transit Market Research: A Practitioner¿s Guidebook examines intelligent transportation systems (ITS) and Transit ITS technologies currently in use, explores their potential to provide market research data, and presents methods for collecting and analyzing these data. The guidebook also highlights three case studies that illustrate how ITS data have been used to improve market research practices.


Thinking with Data

Thinking with Data

Author: Max Shron

Publisher: "O'Reilly Media, Inc."

Published: 2014-01-20

Total Pages: 105

ISBN-13: 1491949775

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Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills. Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You’ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved. Learn a framework for scoping data projects Understand how to pin down the details of an idea, receive feedback, and begin prototyping Use the tools of arguments to ask good questions, build projects in stages, and communicate results Explore data-specific patterns of reasoning and learn how to build more useful arguments Delve into causal reasoning and learn how it permeates data work Put everything together, using extended examples to see the method of full problem thinking in action


Book Synopsis Thinking with Data by : Max Shron

Download or read book Thinking with Data written by Max Shron and published by "O'Reilly Media, Inc.". This book was released on 2014-01-20 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills. Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You’ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved. Learn a framework for scoping data projects Understand how to pin down the details of an idea, receive feedback, and begin prototyping Use the tools of arguments to ask good questions, build projects in stages, and communicate results Explore data-specific patterns of reasoning and learn how to build more useful arguments Delve into causal reasoning and learn how it permeates data work Put everything together, using extended examples to see the method of full problem thinking in action


Doing Data Science

Doing Data Science

Author: Cathy O'Neil

Publisher: "O'Reilly Media, Inc."

Published: 2013-10-09

Total Pages: 408

ISBN-13: 144936389X

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Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.


Book Synopsis Doing Data Science by : Cathy O'Neil

Download or read book Doing Data Science written by Cathy O'Neil and published by "O'Reilly Media, Inc.". This book was released on 2013-10-09 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.


The Data Journalism Handbook

The Data Journalism Handbook

Author: Jonathan Gray

Publisher: "O'Reilly Media, Inc."

Published: 2012-07-12

Total Pages: 243

ISBN-13: 1449330029

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When you combine the sheer scale and range of digital information now available with a journalist’s "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With The Data Journalism Handbook, you’ll explore the potential, limits, and applied uses of this new and fascinating field. This valuable handbook has attracted scores of contributors since the European Journalism Centre and the Open Knowledge Foundation launched the project at MozFest 2011. Through a collection of tips and techniques from leading journalists, professors, software developers, and data analysts, you’ll learn how data can be either the source of data journalism or a tool with which the story is told—or both. Examine the use of data journalism at the BBC, the Chicago Tribune, the Guardian, and other news organizations Explore in-depth case studies on elections, riots, school performance, and corruption Learn how to find data from the Web, through freedom of information laws, and by "crowd sourcing" Extract information from raw data with tips for working with numbers and statistics and using data visualization Deliver data through infographics, news apps, open data platforms, and download links


Book Synopsis The Data Journalism Handbook by : Jonathan Gray

Download or read book The Data Journalism Handbook written by Jonathan Gray and published by "O'Reilly Media, Inc.". This book was released on 2012-07-12 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: When you combine the sheer scale and range of digital information now available with a journalist’s "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With The Data Journalism Handbook, you’ll explore the potential, limits, and applied uses of this new and fascinating field. This valuable handbook has attracted scores of contributors since the European Journalism Centre and the Open Knowledge Foundation launched the project at MozFest 2011. Through a collection of tips and techniques from leading journalists, professors, software developers, and data analysts, you’ll learn how data can be either the source of data journalism or a tool with which the story is told—or both. Examine the use of data journalism at the BBC, the Chicago Tribune, the Guardian, and other news organizations Explore in-depth case studies on elections, riots, school performance, and corruption Learn how to find data from the Web, through freedom of information laws, and by "crowd sourcing" Extract information from raw data with tips for working with numbers and statistics and using data visualization Deliver data through infographics, news apps, open data platforms, and download links


Data Driven

Data Driven

Author: Thomas C. Redman

Publisher: Harvard Business Press

Published: 2008-09-22

Total Pages: 257

ISBN-13: 1422163644

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Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that.


Book Synopsis Data Driven by : Thomas C. Redman

Download or read book Data Driven written by Thomas C. Redman and published by Harvard Business Press. This book was released on 2008-09-22 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that.