Ethan Mccallum
Bad Data Handbook
Paperback Engels 2012 9781449321888Levertijd ± 8 werkdagen
Gratis verzonden
Samenvatting
What is bad data Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems.
From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line Bad data is data that gets in the way. This book explains effective ways to get around it.
Among the many topics covered, you’ll discover how to:
-Test drive your data to see if it’s ready for analysis
-Work spreadsheet data into a usable form
-Handle encoding problems that lurk in text data
-Develop a successful web-scraping effort
-Use NLP tools to reveal the real sentiment of online reviews
-Address cloud computing issues that can impact your analysis effort
-Avoid policies that create data analysis roadblocks
-Take a systematic approach to data quality analysis
Specificaties
ISBN13:9781449321888
Taal:Engels
Bindwijze:paperback
Aantal pagina's:264
Uitgever:John Wiley & Sons
Verschijningsdatum:20-11-2012
Hoofdrubriek:IT-management / ICT
Lezersrecensies
Wees de eerste die een lezersrecensie schrijft!
Inhoudsopgave
About the Authors;
Preface;
Conventions Used in This Book;
Using Code Examples;
Safari® Books Online;
How to Contact Us;
Acknowledgments;
Chapter 1: Setting the Pace: What Is Bad Data?;
Chapter 2: Is It Just Me, or Does This Data Smell Funny?;
2.1 Understand the Data Structure;
2.2 Field Validation;
2.3 Value Validation;
2.4 Physical Interpretation of Simple Statistics;
2.5 Visualization;
2.6 Keyword PPC Example;
2.7 Search Referral Example;
2.8 Recommendation Analysis;
2.9 Time Series Data;
2.10 Conclusion;
Chapter 3: Data Intended for Human Consumption, Not Machine Consumption;
3.1 The Data;
3.2 The Problem: Data Formatted for Human Consumption;
3.3 The Solution: Writing Code;
3.4 Postscript;
3.5 Other Formats;
3.6 Summary;
Chapter 4: Bad Data Lurking in Plain Text;
4.1 Which Plain Text Encoding?;
4.2 Guessing Text Encoding;
4.3 Normalizing Text;
4.4 Problem: Application-Specific Characters Leaking into Plain Text;
4.5 Text Processing with Python;
4.6 Exercises;
Chapter 5: (Re)Organizing the Web’s Data;
5.1 Can You Get That?;
5.2 General Workflow Example;
5.3 The Real Difficulties;
5.4 The Dark Side;
5.5 Conclusion;
Chapter 6: Detecting Liars and the Confused in Contradictory Online Reviews;
6.1 Weotta;
6.2 Getting Reviews;
6.3 Sentiment Classification;
6.4 Polarized Language;
6.5 Corpus Creation;
6.6 Training a Classifier;
6.7 Validating the Classifier;
6.8 Designing with Data;
6.9 Lessons Learned;
6.10 Summary;
6.11 Resources;
Chapter 7: Will the Bad Data Please Stand Up?;
7.1 Example 1: Defect Reduction in Manufacturing;
7.2 Example 2: Who’s Calling?;
7.3 Example 3: When “Typical” Does Not Mean “Average”;
7.4 Lessons Learned;
7.5 Will This Be on the Test?;
Chapter 8: Blood, Sweat, and Urine;
8.1 A Very Nerdy Body Swap Comedy;
8.2 How Chemists Make Up Numbers;
Preface;
Conventions Used in This Book;
Using Code Examples;
Safari® Books Online;
How to Contact Us;
Acknowledgments;
Chapter 1: Setting the Pace: What Is Bad Data?;
Chapter 2: Is It Just Me, or Does This Data Smell Funny?;
2.1 Understand the Data Structure;
2.2 Field Validation;
2.3 Value Validation;
2.4 Physical Interpretation of Simple Statistics;
2.5 Visualization;
2.6 Keyword PPC Example;
2.7 Search Referral Example;
2.8 Recommendation Analysis;
2.9 Time Series Data;
2.10 Conclusion;
Chapter 3: Data Intended for Human Consumption, Not Machine Consumption;
3.1 The Data;
3.2 The Problem: Data Formatted for Human Consumption;
3.3 The Solution: Writing Code;
3.4 Postscript;
3.5 Other Formats;
3.6 Summary;
Chapter 4: Bad Data Lurking in Plain Text;
4.1 Which Plain Text Encoding?;
4.2 Guessing Text Encoding;
4.3 Normalizing Text;
4.4 Problem: Application-Specific Characters Leaking into Plain Text;
4.5 Text Processing with Python;
4.6 Exercises;
Chapter 5: (Re)Organizing the Web’s Data;
5.1 Can You Get That?;
5.2 General Workflow Example;
5.3 The Real Difficulties;
5.4 The Dark Side;
5.5 Conclusion;
Chapter 6: Detecting Liars and the Confused in Contradictory Online Reviews;
6.1 Weotta;
6.2 Getting Reviews;
6.3 Sentiment Classification;
6.4 Polarized Language;
6.5 Corpus Creation;
6.6 Training a Classifier;
6.7 Validating the Classifier;
6.8 Designing with Data;
6.9 Lessons Learned;
6.10 Summary;
6.11 Resources;
Chapter 7: Will the Bad Data Please Stand Up?;
7.1 Example 1: Defect Reduction in Manufacturing;
7.2 Example 2: Who’s Calling?;
7.3 Example 3: When “Typical” Does Not Mean “Average”;
7.4 Lessons Learned;
7.5 Will This Be on the Test?;
Chapter 8: Blood, Sweat, and Urine;
8.1 A Very Nerdy Body Swap Comedy;
8.2 How Chemists Make Up Numbers;
8.3 All Your Database Are Belong to Us;
8.4 Check, Please;
8.5 Live Fast, Die Young, and Leave a Good-Looking Corpse Code Repository;
8.6 Rehab for Chemists (and Other Spreadsheet Abusers);
8.7 tl;dr;
Chapter 9: When Data and Reality Don’t Match;
9.1 Whose Ticker Is It Anyway?;
9.2 Splits, Dividends, and Rescaling;
9.3 Bad Reality;
9.4 Conclusion;
Chapter 10: Subtle Sources of Bias and Error;
10.1 Imputation Bias: General Issues;
10.2 Reporting Errors: General Issues;
10.3 Other Sources of Bias;
10.4 Conclusions;
10.5 References ;
Chapter 11: Don’t Let the Perfect Be the Enemy of the Good: Is Bad Data Really Bad?;
11.1 But First, Let’s Reflect on Graduate School …;
11.2 Moving On to the Professional World;
11.3 Moving into Government Work;
11.4 Government Data Is Very Real;
11.5 Service Call Data as an Applied Example;
11.6 Moving Forward;
11.7 Lessons Learned and Looking Ahead;
Chapter 12: When Databases Attack: A Guide for When to Stick to Files;
12.1 History;
12.2 Consider Files as Your Datastore;
12.3 File Concepts;
12.4 A Web Framework Backed by Files;
12.5 Reflections;
Chapter 13: Crouching Table, Hidden Network;
13.1 A Relational Cost Allocations Model;
13.2 The Delicate Sound of a Combinatorial Explosion…;
13.3 The Hidden Network Emerges;
13.4 Storing the Graph;
13.5 Navigating the Graph with Gremlin;
13.6 Finding Value in Network Properties;
13.7 Think in Terms of Multiple Data Models and Use the Right Tool for the Job;
13.8 Acknowledgments;
Chapter 14: Myths of Cloud Computing;
14.1 Introduction to the Cloud;
14.2 What Is “The Cloud”?;
14.3 The Cloud and Big Data;
14.4 Introducing Fred;
14.5 At First Everything Is Great;
14.6 They Put 100% of Their Infrastructure in the Cloud;
14.7 As Things Grow, They Scale Easily at First;
14.8 Then Things Start Having Trouble;
14.9 They Need to Improve Performance;
14.10 Higher IO Becomes Critical;
14.11 A Major Regional Outage Causes Massive Downtime;
14.12 Higher IO Comes with a Cost;
14.13 Data Sizes Increase;
14.14 Geo Redundancy Becomes a Priority;
14.15 Horizontal Scale Isn’t as Easy as They Hoped;
14.16 Costs Increase Dramatically;
14.17 Fred’s Follies;
14.18 Myth 1: Cloud Is a Great Solution for All Infrastructure Components;
14.19 Myth 2: Cloud Will Save Us Money;
14.20 Myth 3: Cloud IO Performance Can Be Improved to Acceptable Levels Through Software RAID;
14.21 Myth 4: Cloud Computing Makes Horizontal Scaling Easy;
14.22 Conclusion and Recommendations;
Chapter 15: The Dark Side of Data Science;
15.1 Avoid These Pitfalls;
15.2 Know Nothing About Thy Data;
15.3 Thou Shalt Provide Your Data Scientists with a Single Tool for All Tasks;
15.4 Thou Shalt Analyze for Analysis’ Sake Only;
15.5 Thou Shalt Compartmentalize Learnings;
15.6 Thou Shalt Expect Omnipotence from Data Scientists;
15.7 Final Thoughts;
Chapter 16: How to Feed and Care for Your Machine-Learning Experts;
16.1 Define the Problem;
16.2 Fake It Before You Make It;
16.3 Create a Training Set;
16.4 Pick the Features;
16.5 Encode the Data;
16.6 Split Into Training, Test, and Solution Sets;
16.7 Describe the Problem;
16.8 Respond to Questions;
16.9 Integrate the Solutions;
16.10 Conclusion;
Chapter 17: Data Traceability;
17.1 Why?;
17.2 Personal Experience;
17.3 Immutability: Borrowing an Idea from Functional Programming;
17.4 An Example;
17.5 Conclusion;
Chapter 18: Social Media: Erasable Ink?;
18.1 Social Media: Whose Data Is This Anyway?;
18.2 Control;
18.3 Commercial Resyndication;
18.4 Expectations Around Communication and Expression;
18.5 Technical Implications of New End User Expectations;
18.6 What Does the Industry Do?;
18.7 What Should End Users Do?;
18.8 How Do We Work Together?;
Chapter 19: Data Quality Analysis Demystified: Knowing When Your Data Is Good Enough;
19.1 Framework Introduction: The Four Cs of Data Quality Analysis;
19.2 Complete;
19.3 Coherent;
19.4 Correct;
19.5 aCcountable;
19.6 Conclusion;
Colophon;
8.4 Check, Please;
8.5 Live Fast, Die Young, and Leave a Good-Looking Corpse Code Repository;
8.6 Rehab for Chemists (and Other Spreadsheet Abusers);
8.7 tl;dr;
Chapter 9: When Data and Reality Don’t Match;
9.1 Whose Ticker Is It Anyway?;
9.2 Splits, Dividends, and Rescaling;
9.3 Bad Reality;
9.4 Conclusion;
Chapter 10: Subtle Sources of Bias and Error;
10.1 Imputation Bias: General Issues;
10.2 Reporting Errors: General Issues;
10.3 Other Sources of Bias;
10.4 Conclusions;
10.5 References ;
Chapter 11: Don’t Let the Perfect Be the Enemy of the Good: Is Bad Data Really Bad?;
11.1 But First, Let’s Reflect on Graduate School …;
11.2 Moving On to the Professional World;
11.3 Moving into Government Work;
11.4 Government Data Is Very Real;
11.5 Service Call Data as an Applied Example;
11.6 Moving Forward;
11.7 Lessons Learned and Looking Ahead;
Chapter 12: When Databases Attack: A Guide for When to Stick to Files;
12.1 History;
12.2 Consider Files as Your Datastore;
12.3 File Concepts;
12.4 A Web Framework Backed by Files;
12.5 Reflections;
Chapter 13: Crouching Table, Hidden Network;
13.1 A Relational Cost Allocations Model;
13.2 The Delicate Sound of a Combinatorial Explosion…;
13.3 The Hidden Network Emerges;
13.4 Storing the Graph;
13.5 Navigating the Graph with Gremlin;
13.6 Finding Value in Network Properties;
13.7 Think in Terms of Multiple Data Models and Use the Right Tool for the Job;
13.8 Acknowledgments;
Chapter 14: Myths of Cloud Computing;
14.1 Introduction to the Cloud;
14.2 What Is “The Cloud”?;
14.3 The Cloud and Big Data;
14.4 Introducing Fred;
14.5 At First Everything Is Great;
14.6 They Put 100% of Their Infrastructure in the Cloud;
14.7 As Things Grow, They Scale Easily at First;
14.8 Then Things Start Having Trouble;
14.9 They Need to Improve Performance;
14.10 Higher IO Becomes Critical;
14.11 A Major Regional Outage Causes Massive Downtime;
14.12 Higher IO Comes with a Cost;
14.13 Data Sizes Increase;
14.14 Geo Redundancy Becomes a Priority;
14.15 Horizontal Scale Isn’t as Easy as They Hoped;
14.16 Costs Increase Dramatically;
14.17 Fred’s Follies;
14.18 Myth 1: Cloud Is a Great Solution for All Infrastructure Components;
14.19 Myth 2: Cloud Will Save Us Money;
14.20 Myth 3: Cloud IO Performance Can Be Improved to Acceptable Levels Through Software RAID;
14.21 Myth 4: Cloud Computing Makes Horizontal Scaling Easy;
14.22 Conclusion and Recommendations;
Chapter 15: The Dark Side of Data Science;
15.1 Avoid These Pitfalls;
15.2 Know Nothing About Thy Data;
15.3 Thou Shalt Provide Your Data Scientists with a Single Tool for All Tasks;
15.4 Thou Shalt Analyze for Analysis’ Sake Only;
15.5 Thou Shalt Compartmentalize Learnings;
15.6 Thou Shalt Expect Omnipotence from Data Scientists;
15.7 Final Thoughts;
Chapter 16: How to Feed and Care for Your Machine-Learning Experts;
16.1 Define the Problem;
16.2 Fake It Before You Make It;
16.3 Create a Training Set;
16.4 Pick the Features;
16.5 Encode the Data;
16.6 Split Into Training, Test, and Solution Sets;
16.7 Describe the Problem;
16.8 Respond to Questions;
16.9 Integrate the Solutions;
16.10 Conclusion;
Chapter 17: Data Traceability;
17.1 Why?;
17.2 Personal Experience;
17.3 Immutability: Borrowing an Idea from Functional Programming;
17.4 An Example;
17.5 Conclusion;
Chapter 18: Social Media: Erasable Ink?;
18.1 Social Media: Whose Data Is This Anyway?;
18.2 Control;
18.3 Commercial Resyndication;
18.4 Expectations Around Communication and Expression;
18.5 Technical Implications of New End User Expectations;
18.6 What Does the Industry Do?;
18.7 What Should End Users Do?;
18.8 How Do We Work Together?;
Chapter 19: Data Quality Analysis Demystified: Knowing When Your Data Is Good Enough;
19.1 Framework Introduction: The Four Cs of Data Quality Analysis;
19.2 Complete;
19.3 Coherent;
19.4 Correct;
19.5 aCcountable;
19.6 Conclusion;
Colophon;
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