Isbn: 9781449321888 - bad data handbook: cleaning up the data so you can get back to work (27 resultados)

Paperback. Condición: Very Good. 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.…

Condición: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

paperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Paperback. Condición: Very Good. 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.…

paperback. Condición: Fine.

Condición: New.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: NEW. International Edition, Paperback, Brand New,ISBN and Cover image may differ but contents similar to U.S. Edition. We ship from multiple Locations including India, We ship to PO , APO and FPO adresses in U.S.A. Choose Expedited Shipping for FASTER DELIVERY.Customer Satisfaction Guaranteed.…

Condición: As New. Unread book in perfect condition.

Paperback. Condición: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: New. Illustrated. 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.…

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,550grams, ISBN:9781449321888.

Paperback or Softback. Condición: New. Bad Data Handbook: Cleaning Up the Data So You Can Get Back to Work. Book.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New. 2012. 1st Edition. Paperback. 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. Num Pages: 264 pages, Illustrations. BIC Classification: UNA. Category: (XV) Technical / Manuals. Dimension: 231 x 178 x 14. Weight in Grams: 442. . . . . .…

Condición: New. In English.

Condición: New. 2012. 1st Edition. Paperback. 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. Num Pages: 264 pages, Illustrations. BIC Classification: UNA. Category: (XV) Technical / Manuals. Dimension: 231 x 178 x 14. Weight in Grams: 442. . . . . . Books ship from the US and Ireland.…

Paperback. Condición: Brand New. 1st edition. 245 pages. 9.25x6.75x0.75 inches. In Stock.

Condición: New. pp. 264.

Condición: New. pp. 264.

Paperback. Condición: New. Illustrated. 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.…

Taschenbuch. Condición: Neu. Neuware - 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.…

Condición: New. 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, b.

Paperback. Condición: Brand New. 1st edition. 245 pages. 9.25x6.75x0.75 inches. In Stock. This item is printed on demand.