Bad Data Handbook. Cleaning Up The Data So You Can Get Back To Work
![Język publikacji: angielski Język publikacji: angielski](https://static01.helion.com.pl/global/flagi/1.png)
- Autor:
- Q. Ethan McCallum
![Bad Data Handbook. Cleaning Up The Data So You Can Get Back To Work Q. Ethan McCallum - okładka ebooka](https://static01.helion.com.pl/global/okladki/326x466/e_2gtg.png)
![Bad Data Handbook. Cleaning Up The Data So You Can Get Back To Work Q. Ethan McCallum - tył okładki ebooka](https://static01.helion.com.pl/global/okladki-tyl/326x466/e_2gtg.png)
- Ocena:
- Bądź pierwszym, który oceni tę książkę
- Stron:
- 264
- Dostępne formaty:
-
ePubMobi
Opis ebooka: Bad Data Handbook. Cleaning Up The Data So You Can Get Back To Work
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
Wybrane bestsellery
-
You're sitting on a pile of interesting data. How do you transform that into money? It's easy to focus on the contents of the data itself, and to succumb to the (rather unimaginative) idea of simply collecting and reselling it in raw form. While that's certainly profitable right now, you'd do wel...
-
It’s tough to argue with R as a high-quality, cross-platform, open source statistical software product—unless you’re in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on u...(72.24 zł najniższa cena z 30 dni)
76.42 zł
89.90 zł(-15%) -
Managing multiple Red Hat-based systems can be easy--with the right tools. The yum package manager and the Kickstart installation utility are full of power and potential for automatic installation, customization, and updates. Here's what you need to know to take control of your systems.(38.17 zł najniższa cena z 30 dni)
38.17 zł
44.90 zł(-15%) -
This book will guide you through the fundamental and advanced features of the Snowpark framework in Python. You’ll learn how to use Snowpark for implementing workloads in the fields of data engineering, data science, and data applications.
The Ultimate Guide to Snowpark. Design and deploy Snowpark with Python for efficient data workloads The Ultimate Guide to Snowpark. Design and deploy Snowpark with Python for efficient data workloads
-
This book provides a highly focused view of real business outcomes powered by data governance, that resonate with non-data executives such as CFOs and CEOs. You’ll also find useful insights into how to implement data governance initiatives.
Data Governance Handbook. A practical approach to building trust in data Data Governance Handbook. A practical approach to building trust in data
-
This book shows you how to use Apache Spark, Delta Lake, and Databricks to build data pipelines, manage and transform data, optimize performance, and more. Additionally, you’ll implement DataOps and DevOps practices, and orchestrate data workflows.
Data Engineering with Databricks Cookbook. Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake Data Engineering with Databricks Cookbook. Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake
-
Ця книжка познайомить вас з особливостями Jav...
Head First. Програмування на JavaScript. Head First. Програмування на JavaScript Head First. Програмування на JavaScript. Head First. Програмування на JavaScript
(84.16 zł najniższa cena z 30 dni)84.16 zł
103.90 zł(-19%) -
«Патерни проєктування» 2014 ваша книжка, якщо C...(84.16 zł najniższa cena z 30 dni)
84.16 zł
103.90 zł(-19%) -
This practical guide to implementing DeFi in your projects guides you through building full-stack DeFi solutions with popular tools and teaches you how to leverage blockchain technologies to manage crypto assets.
Building Full Stack DeFi Applications. A practical guide to creating your own decentralized finance projects on blockchain Building Full Stack DeFi Applications. A practical guide to creating your own decentralized finance projects on blockchain
Ebooka "Bad Data Handbook. Cleaning Up The Data So You Can Get Back To Work" przeczytasz na:
-
czytnikach Inkbook, Kindle, Pocketbook, Onyx Boox i innych
-
systemach Windows, MacOS i innych
-
systemach Windows, Android, iOS, HarmonyOS
-
na dowolnych urządzeniach i aplikacjach obsługujących formaty: PDF, EPub, Mobi
Masz pytania? Zajrzyj do zakładki Pomoc »
Audiobooka "Bad Data Handbook. Cleaning Up The Data So You Can Get Back To Work" posłuchasz:
-
w aplikacji Ebookpoint na Android, iOS, HarmonyOs
-
na systemach Windows, MacOS i innych
-
na dowolnych urządzeniach i aplikacjach obsługujących format MP3 (pliki spakowane w ZIP)
Masz pytania? Zajrzyj do zakładki Pomoc »
Kurs Video "Bad Data Handbook. Cleaning Up The Data So You Can Get Back To Work" zobaczysz:
-
w aplikacjach Ebookpoint i Videopoint na Android, iOS, HarmonyOs
-
na systemach Windows, MacOS i innych z dostępem do najnowszej wersji Twojej przeglądarki internetowej
Szczegóły ebooka
- ISBN Ebooka:
- 978-14-493-2497-1, 9781449324971
- Data wydania ebooka:
-
2012-11-07
Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
- Język publikacji:
- angielski
- Rozmiar pliku ePub:
- 4.0MB
- Rozmiar pliku Mobi:
- 9.0MB
Spis treści ebooka
- Bad Data Handbook
- About the Authors
- Preface
- Conventions Used in This Book
- Using Code Examples
- Safari Books Online
- How to Contact Us
- Acknowledgments
- 1. Setting the Pace: What Is Bad Data?
- 2. Is It Just Me, or Does This Data Smell Funny?
- Understand the Data Structure
- Field Validation
- Value Validation
- Physical Interpretation of Simple Statistics
- Visualization
- Keyword PPC Example
- Search Referral Example
- Recommendation Analysis
- Time Series Data
- Conclusion
- 3. Data Intended for Human Consumption, Not Machine Consumption
- The Data
- The Problem: Data Formatted for Human Consumption
- The Arrangement of Data
- Data Spread Across Multiple Files
- The Solution: Writing Code
- Reading Data from an Awkward Format
- Reading Data Spread Across Several Files
- Postscript
- Other Formats
- Summary
- 4. Bad Data Lurking in Plain Text
- Which Plain Text Encoding?
- Guessing Text Encoding
- Normalizing Text
- Problem: Application-Specific Characters Leaking into Plain Text
- Text Processing with Python
- Exercises
- 5. (Re)Organizing the Webs Data
- Can You Get That?
- General Workflow Example
- robots.txt
- Identifying the Data Organization Pattern
- Store Offline Version for Parsing
- Scrape the Information Off the Page
- The Real Difficulties
- Download the Raw Content If Possible
- Forms, Dialog Boxes, and New Windows
- Flash
- The Dark Side
- Conclusion
- 6. Detecting Liars and the Confused in Contradictory Online Reviews
- Weotta
- Getting Reviews
- Sentiment Classification
- Polarized Language
- Corpus Creation
- Training a Classifier
- Validating the Classifier
- Designing with Data
- Lessons Learned
- Summary
- Resources
- 7. Will the Bad Data Please Stand Up?
- Example 1: Defect Reduction in Manufacturing
- Example 2: Whos Calling?
- Example 3: When Typical Does Not Mean Average
- Lessons Learned
- Will This Be on the Test?
- 8. Blood, Sweat, and Urine
- A Very Nerdy Body Swap Comedy
- How Chemists Make Up Numbers
- All Your Database Are Belong to Us
- Check, Please
- Live Fast, Die Young, and Leave a Good-Looking Corpse Code Repository
- Rehab for Chemists (and Other Spreadsheet Abusers)
- tl;dr
- 9. When Data and Reality Dont Match
- Whose Ticker Is It Anyway?
- Splits, Dividends, and Rescaling
- Bad Reality
- Conclusion
- 10. Subtle Sources of Bias and Error
- Imputation Bias: General Issues
- Reporting Errors: General Issues
- Other Sources of Bias
- Topcoding/Bottomcoding
- Seam Bias
- Proxy Reporting
- Sample Selection
- Conclusions
- References
- 11. Dont Let the Perfect Be the Enemy of the Good: Is Bad Data Really Bad?
- But First, Lets Reflect on Graduate School
- Moving On to the Professional World
- Moving into Government Work
- Government Data Is Very Real
- Service Call Data as an Applied Example
- Moving Forward
- Lessons Learned and Looking Ahead
- 12. When Databases Attack: A Guide for When to Stick to Files
- History
- Building My Toolset
- The Roadblock: My Datastore
- History
- Consider Files as Your Datastore
- Files Are Simple!
- Files Work with Everything
- Files Can Contain Any Data Type
- Data Corruption Is Local
- They Have Great Tooling
- Theres No Install Tax
- File Concepts
- Encoding
- Text Files
- Binary Data
- Memory-Mapped Files
- File Formats
- Delimiters
- A Web Framework Backed by Files
- Motivation
- Implementation
- Reflections
- 13. Crouching Table, Hidden Network
- A Relational Cost Allocations Model
- The Delicate Sound of a Combinatorial Explosion
- The Hidden Network Emerges
- Storing the Graph
- Navigating the Graph with Gremlin
- Finding Value in Network Properties
- Think in Terms of Multiple Data Models and Use the Right Tool for the Job
- Acknowledgments
- 14. Myths of Cloud Computing
- Introduction to the Cloud
- What Is The Cloud?
- The Cloud and Big Data
- Introducing Fred
- At First Everything Is Great
- They Put 100% of Their Infrastructure in the Cloud
- As Things Grow, They Scale Easily at First
- Then Things Start Having Trouble
- They Need to Improve Performance
- Higher IO Becomes Critical
- A Major Regional Outage Causes Massive Downtime
- Higher IO Comes with a Cost
- Data Sizes Increase
- Geo Redundancy Becomes a Priority
- Horizontal Scale Isnt as Easy as They Hoped
- Costs Increase Dramatically
- Freds Follies
- Myth 1: Cloud Is a Great Solution for All Infrastructure Components
- How This Myth Relates to Freds Story
- Myth 2: Cloud Will Save Us Money
- How This Myth Relates to Freds Story
- Myth 3: Cloud IO Performance Can Be Improved to Acceptable Levels Through Software RAID
- How This Myth Relates to Freds Story
- Myth 4: Cloud Computing Makes Horizontal Scaling Easy
- How This Myth Relates to Freds Story
- Conclusion and Recommendations
- 15. The Dark Side of Data Science
- Avoid These Pitfalls
- Know Nothing About Thy Data
- Be Inconsistent in Cleaning and Organizing the Data
- Assume Data Is Correct and Complete
- Spillover of Time-Bound Data
- Thou Shalt Provide Your Data Scientists with a Single Tool for All Tasks
- Using a Production Environment for Ad-Hoc Analysis
- The Ideal Data Science Environment
- Thou Shalt Analyze for Analysis Sake Only
- Thou Shalt Compartmentalize Learnings
- Thou Shalt Expect Omnipotence from Data Scientists
- Where Do Data Scientists Live Within the Organization?
- Final Thoughts
- 16. How to Feed and Care for Your Machine-Learning Experts
- Define the Problem
- Fake It Before You Make It
- Create a Training Set
- Pick the Features
- Encode the Data
- Split Into Training, Test, and Solution Sets
- Describe the Problem
- Respond to Questions
- Integrate the Solutions
- Conclusion
- 17. Data Traceability
- Why?
- Personal Experience
- Snapshotting
- Saving the Source
- Weighting Sources
- Backing Out Data
- Separating Phases (and Keeping them Pure)
- Identifying the Root Cause
- Finding Areas for Improvement
- Immutability: Borrowing an Idea from Functional Programming
- An Example
- Crawlers
- Change
- Clustering
- Popularity
- Conclusion
- 18. Social Media: Erasable Ink?
- Social Media: Whose Data Is This Anyway?
- Control
- Commercial Resyndication
- Expectations Around Communication and Expression
- Technical Implications of New End User Expectations
- What Does the Industry Do?
- Validation API
- Update Notification API
- What Should End Users Do?
- How Do We Work Together?
- 19. Data Quality Analysis Demystified: Knowing When Your Data Is Good Enough
- Framework Introduction: The Four Cs of Data Quality Analysis
- Complete
- Coherent
- Correct
- aCcountable
- Conclusion
- Index
- About the Author
- Colophon
- Copyright
O'Reilly Media - inne książki
-
Python is an excellent way to get started in programming, and this clear, concise guide walks you through Python a step at a time—beginning with basic programming concepts before moving on to functions, data structures, and object-oriented design. This revised third edition reflects the gro...(143.65 zł najniższa cena z 30 dni)
160.65 zł
189.00 zł(-15%) -
Developers with the ability to operate, troubleshoot, and monitor applications in Kubernetes are in high demand today. To meet this need, the Cloud Native Computing Foundation created a certification exam to establish a developer's credibility and value in the job market for work in a Kubernetes ...
Certified Kubernetes Application Developer (CKAD) Study Guide. 2nd Edition Certified Kubernetes Application Developer (CKAD) Study Guide. 2nd Edition
(177.65 zł najniższa cena z 30 dni)186.15 zł
219.00 zł(-15%) -
The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity i...(194.65 zł najniższa cena z 30 dni)
211.65 zł
249.00 zł(-15%) -
How do some organizations maintain 24-7 internet-scale operations? How can organizations integrate security while continuously deploying new features? How do organizations increase security within their DevOps processes?This practical guide helps you answer those questions and more. Author Steve ...(169.14 zł najniższa cena z 30 dni)
177.65 zł
209.00 zł(-15%) -
Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, ...(228.65 zł najniższa cena z 30 dni)
254.15 zł
299.00 zł(-15%) -
Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry great...(245.65 zł najniższa cena z 30 dni)
254.15 zł
299.00 zł(-15%) -
Filled with tips, tricks, and techniques, this easy-to-use book is the perfect resource for intermediate to advanced users of Excel. You'll find complete recipes for more than a dozen topics covering formulas, PivotTables, charts, Power Query, and more. Each recipe poses a particular problem and ...(203.15 zł najniższa cena z 30 dni)
211.65 zł
249.00 zł(-15%) -
Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool—a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of prio...(211.65 zł najniższa cena z 30 dni)
220.15 zł
259.00 zł(-15%) -
In today's data-driven world, understanding statistical models is crucial for effective analysis and decision making. Whether you're a beginner or an experienced user, this book equips you with the foundational knowledge to grasp and implement statistical models within Tableau. Gain the confidenc...(186.15 zł najniższa cena z 30 dni)
186.15 zł
219.00 zł(-15%) -
If you haven't modernized your data cleaning and reporting processes in Microsoft Excel, you're missing out on big productivity gains. And if you're looking to conduct rigorous data analysis, more can be done in Excel than you think. This practical book serves as an introduction to the modern Exc...(186.15 zł najniższa cena z 30 dni)
186.15 zł
219.00 zł(-15%)
Dzieki opcji "Druk na żądanie" do sprzedaży wracają tytuły Grupy Helion, które cieszyły sie dużym zainteresowaniem, a których nakład został wyprzedany.
Dla naszych Czytelników wydrukowaliśmy dodatkową pulę egzemplarzy w technice druku cyfrowego.
Co powinieneś wiedzieć o usłudze "Druk na żądanie":
- usługa obejmuje tylko widoczną poniżej listę tytułów, którą na bieżąco aktualizujemy;
- cena książki może być wyższa od początkowej ceny detalicznej, co jest spowodowane kosztami druku cyfrowego (wyższymi niż koszty tradycyjnego druku offsetowego). Obowiązująca cena jest zawsze podawana na stronie WWW książki;
- zawartość książki wraz z dodatkami (płyta CD, DVD) odpowiada jej pierwotnemu wydaniu i jest w pełni komplementarna;
- usługa nie obejmuje książek w kolorze.
Masz pytanie o konkretny tytuł? Napisz do nas: sklep[at]helion.pl.
Książka, którą chcesz zamówić pochodzi z końcówki nakładu. Oznacza to, że mogą się pojawić drobne defekty (otarcia, rysy, zagięcia).
Co powinieneś wiedzieć o usłudze "Końcówka nakładu":
- usługa obejmuje tylko książki oznaczone tagiem "Końcówka nakładu";
- wady o których mowa powyżej nie podlegają reklamacji;
Masz pytanie o konkretny tytuł? Napisz do nas: sklep[at]helion.pl.
Książka drukowana
![Loader](https://static01.helion.com.pl/ebookpoint/img/ajax-loader.gif)
![ajax-loader](https://static01.helion.com.pl/ebookpoint/img/ajax-loader.gif)
Oceny i opinie klientów: Bad Data Handbook. Cleaning Up The Data So You Can Get Back To Work Q. Ethan McCallum (0)
Weryfikacja opinii następuję na podstawie historii zamówień na koncie Użytkownika umieszczającego opinię. Użytkownik mógł otrzymać punkty za opublikowanie opinii uprawniające do uzyskania rabatu w ramach Programu Punktowego.