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- 530pages
- 19 heures de lecture
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These days it seems like everyone is collecting data. But all of that data is just raw information -- to make that information meaningful, it has to be organized, filtered, and analyzed. Anyone can apply data analysis tools and get results, but without the right approach those results may be useless. Author Philipp Janert teaches you how to think about how to effectively approach data analysis problems, and how to extract all of the available information from your data. Janert covers univariate data, data in multiple dimensions, time series data, graphical techniques, data mining, machine learning, and many other topics. He also reveals how seat-of-the-pants knowledge can lead you to the best approach right from the start, and how to assess results to determine if they're meaningful.
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Data Analysis with Open Source Tools, Philipp K. Janert
- Langue
- Année de publication
- 2010
- product-detail.submit-box.info.binding
- (souple),
- État du livre
- Abîmé
- Prix
- 10,55 €
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- Titre
- Data Analysis with Open Source Tools
- Sous-titre
- A Hands-On Guide for Programmers and Data Scientists
- Langue
- Anglais
- Auteurs
- Philipp K. Janert
- Éditeur
- O'Reilly Media
- Publié
- 2010
- Format
- souple
- Pages
- 530
- ISBN10
- 0596802358
- ISBN13
- 9780596802356
- Séries
- Mots clés
- Nonfiction, Manuels et guides, Informatique & Internet, Science, Économie, États-Unis, Mathématiques, Technologie, Intelligence Artificielle, Base de données, Analyse de données, Open Source
- Description
- These days it seems like everyone is collecting data. But all of that data is just raw information -- to make that information meaningful, it has to be organized, filtered, and analyzed. Anyone can apply data analysis tools and get results, but without the right approach those results may be useless. Author Philipp Janert teaches you how to think about how to effectively approach data analysis problems, and how to extract all of the available information from your data. Janert covers univariate data, data in multiple dimensions, time series data, graphical techniques, data mining, machine learning, and many other topics. He also reveals how seat-of-the-pants knowledge can lead you to the best approach right from the start, and how to assess results to determine if they're meaningful.