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Clean Data
Clean Data
Knygos.lt klubas Knygos.lt nariams
61,66 €
-30%
Įprastai
88,09 €
  • Išsiųsime per 12–18 d.d.
Is much of your time spent doing tedious tasks such as cleaning dirty data, accounting for lost data, and preparing data to be used by others? If so, then having the right tools makes a critical difference, and will be a great investment as you grow your data science expertise.The book starts by highlighting the importance of data cleaning in data science, and will show you how to reap rewards from reforming your cleaning process. Next, you will cement your knowledge of the basic concepts that…
  • Leidėjas:
  • Metai: 2015
  • Puslapiai: 272
  • ISBN-10: 1785284010
  • ISBN-13: 9781785284014
  • Formatas: 19.1 x 23.5 x 1.5 cm, minkšti viršeliai
  • Kalba: Anglų

Clean Data (el. knyga) (skaityta knyga) | Megan Squire | knygos.lt

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Is much of your time spent doing tedious tasks such as cleaning dirty data, accounting for lost data, and preparing data to be used by others? If so, then having the right tools makes a critical difference, and will be a great investment as you grow your data science expertise.

The book starts by highlighting the importance of data cleaning in data science, and will show you how to reap rewards from reforming your cleaning process. Next, you will cement your knowledge of the basic concepts that the rest of the book relies on: file formats, data types, and character encodings. You will also learn how to extract and clean data stored in RDBMS, web files, and PDF documents, through practical examples.

At the end of the book, you will be given a chance to tackle a couple of real-world projects.

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  • Autorius: Megan Squire
  • Leidėjas:
  • Metai: 2015
  • Puslapiai: 272
  • ISBN-10: 1785284010
  • ISBN-13: 9781785284014
  • Formatas: 19.1 x 23.5 x 1.5 cm, minkšti viršeliai
  • Kalba: Anglų

Is much of your time spent doing tedious tasks such as cleaning dirty data, accounting for lost data, and preparing data to be used by others? If so, then having the right tools makes a critical difference, and will be a great investment as you grow your data science expertise.

The book starts by highlighting the importance of data cleaning in data science, and will show you how to reap rewards from reforming your cleaning process. Next, you will cement your knowledge of the basic concepts that the rest of the book relies on: file formats, data types, and character encodings. You will also learn how to extract and clean data stored in RDBMS, web files, and PDF documents, through practical examples.

At the end of the book, you will be given a chance to tackle a couple of real-world projects.

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