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Bad Data Handbook by McCallum, Q.
by McCallum, Q. | PB | Good
US $4.79
ApproximatelyS$ 6.18
Condition:
“Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, ”... Read moreabout condition
Good
A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages.
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eBay item number:145686908240
Item specifics
- Condition
- Good
- Seller Notes
- Binding
- Paperback
- Weight
- 1 lbs
- Product Group
- Book
- IsTextBook
- No
- ISBN
- 9781449321888
- Subject Area
- Computers
- Publication Name
- Bad Data Handbook : Cleaning Up the Data So You Can Get Back to Work
- Publisher
- O'reilly Media, Incorporated
- Item Length
- 9.1 in
- Subject
- Data Modeling & Design, Databases / Data Warehousing, Data Processing
- Publication Year
- 2012
- Type
- Handbook
- Format
- Trade Paperback
- Language
- English
- Item Height
- 0.5 in
- Item Weight
- 16.5 Oz
- Item Width
- 7 in
- Number of Pages
- 264 Pages
About this product
Product Identifiers
Publisher
O'reilly Media, Incorporated
ISBN-10
1449321887
ISBN-13
9781449321888
eBay Product ID (ePID)
117267262
Product Key Features
Number of Pages
264 Pages
Language
English
Publication Name
Bad Data Handbook : Cleaning Up the Data So You Can Get Back to Work
Subject
Data Modeling & Design, Databases / Data Warehousing, Data Processing
Publication Year
2012
Type
Handbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Height
0.5 in
Item Weight
16.5 Oz
Item Length
9.1 in
Item Width
7 in
Additional Product Features
Intended Audience
Scholarly & Professional
Illustrated
Yes
Edition Description
Handbook (Instructor's)
Synopsis
Welcome to data science's dirty secret: real-world data is messy. Data scientists must spend a good deal of time playing software developer, writing code to clean up data before they can actually do anything constructive with it. It's a necessary evil, but you can still make the most of it. This practical book walks you through several real-world examples to demonstrate the theory and practice behind working with and cleaning up dirty data. No one tool solves all of the problems well. Wise data scientists learn many tools and learn where each one shines. To that end, this book takes a polyglot approach: most examples will involve R and Python, but expect the occasional smattering of Groovy and sed/awk fun., 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, Welcome to data science's dirty secret: real-world data is messy. Data scientists must spend a good deal of time playing software developer, writing code to clean up data before they can actually do anything constructive with it. This is a necessary evil, but we can still make the most of it.
LC Classification Number
QA76.76.D34
Item description from the seller
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