Data Analysis with Open Source Tools. A hands-on guide for programmers and data scientists
Par :Formats :
Définitivement indisponible
Cet article ne peut plus être commandé sur notre site (ouvrage épuisé ou plus commercialisé). Il se peut néanmoins que l'éditeur imprime une nouvelle édition de cet ouvrage à l'avenir. Nous vous invitons donc à revenir périodiquement sur notre site.
Disponible dans votre compte client Decitre ou Furet du Nord dès validation de votre commande. Le format Multi-format est :
- Pour les liseuses autres que Vivlio, vous devez utiliser le logiciel Adobe Digital Edition. Non compatible avec la lecture sur les liseuses Kindle, Remarkable et Sony
, qui est-ce ?Notre partenaire de plateforme de lecture numérique où vous retrouverez l'ensemble de vos ebooks gratuitement
Pour en savoir plus sur nos ebooks, consultez notre aide en ligne ici
C'est si simple ! Lisez votre ebook avec l'app Vivlio sur votre tablette, mobile ou ordinateur :
- Nombre de pages536
- FormatMulti-format
- ISBN978-1-4493-9488-2
- EAN9781449394882
- Date de parution11/11/2010
- Protection num.NC
- Infos supplémentairesMulti-format incluant PDF sans p...
- ÉditeurO'Reilly Media
Résumé
Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.
Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter.
Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. - Use graphics to describe data with one, two, or dozens of variables - Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments - Mine data with computationally intensive methods such as simulation and clustering - Make your conclusions understandable through reports, dashboards, and other metrics programs - Understand financial calculations, including the time-value of money - Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations - Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E.
Driscoll, CEO/Founder, Dataspora
Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. - Use graphics to describe data with one, two, or dozens of variables - Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments - Mine data with computationally intensive methods such as simulation and clustering - Make your conclusions understandable through reports, dashboards, and other metrics programs - Understand financial calculations, including the time-value of money - Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations - Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E.
Driscoll, CEO/Founder, Dataspora
Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.
Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter.
Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. - Use graphics to describe data with one, two, or dozens of variables - Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments - Mine data with computationally intensive methods such as simulation and clustering - Make your conclusions understandable through reports, dashboards, and other metrics programs - Understand financial calculations, including the time-value of money - Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations - Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E.
Driscoll, CEO/Founder, Dataspora
Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. - Use graphics to describe data with one, two, or dozens of variables - Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments - Mine data with computationally intensive methods such as simulation and clustering - Make your conclusions understandable through reports, dashboards, and other metrics programs - Understand financial calculations, including the time-value of money - Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations - Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E.
Driscoll, CEO/Founder, Dataspora