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Data science for dummies /
This is the perfect starting point for IT professionals and students interested in making sense of their organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and softwar...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley and Sons, Inc.,
[2015]
|
| Colección: | --For dummies.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Pierson, Lillian, |e author. | |
| 245 | 1 | 0 | |a Data science for dummies / |c by Lillian Pierson ; foreword by Jake Porway. |
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley and Sons, Inc., |c [2015] | |
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| 505 | 0 | |a Part I, Getting started with data science: Wrapping your head around data science -- Exploring data engineering pipelines and infrastructure -- Applying data science to business and industry -- Part II, Using data science to extract meaning from your data: Introducing probability and statistics -- Clustering and classification -- Clustering and classification with nearest neighbor algorithms -- Mathematical modeling in data science -- Modeling spatial data with statistics -- Part III, Creating data visualizations that clearly communicate: Following the principles of data visualization design -- Using d3.js for data visualization -- Web-based applications for visualization design -- Exploring best practices in dashboard design -- Making maps from spatial data -- Part IV, Computing for data science: Using python for data science -- Using open source R for data science -- Using SQL in data science -- Software applications for data science -- Part V, Applying domain expertise to solve real-world problems: Using data science in journalism -- Delving into environmental data science -- Data science for driving growth in e-commerce -- Using data science to describe and predict criminal activity -- Part VI, The part of tens: Ten phenomenal resources for open data -- Ten (or so) free data science tools and applications. | |
| 520 | |a This is the perfect starting point for IT professionals and students interested in making sense of their organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data. This book provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis; details different data visualization techniques that can be used to showcase and summarize your data; explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques; includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark. -- |c Edited summary from book. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Information technology. | |
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| 650 | 0 | |a Databases. | |
| 650 | 0 | |a Data mining. | |
| 650 | 2 | |a Information Storage and Retrieval | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Technologie de l'information. | |
| 650 | 6 | |a Recherche de l'information. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
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| 650 | 7 | |a information retrieval. |2 aat | |
| 650 | 7 | |a REFERENCE |x Questions & Answers. |2 bisacsh | |
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| 650 | 7 | |a Databases |2 fast | |
| 650 | 7 | |a Information retrieval |2 fast | |
| 650 | 7 | |a Information technology |2 fast | |
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