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Data science for business [what you need to know about data mining and data-analytic thinking] /
Provides an introduction to the fundamental principles of data science, walking the reader through the "data-analytic thinking" necessary for extracting useful knowledge and business value from collected data.
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Book |
| Language: | English |
| Published: |
Sebastopol, Calif. :
O'Reilly,
2013.
|
| Edition: | 1st ed. |
| Physical Description: |
xxi, 386 pages : illustrations ; 24 cm. |
| Subjects: | |
| Online Access: | Additional Information at Google Books |
MARC
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| 003 | OCoLC | ||
| 005 | 20180522110838.0 | ||
| 008 | 130524s2013 caua b 001 0 eng | ||
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| 100 | 1 | |a Provost, Foster, |d 1964- |0 https://id.loc.gov/authorities/names/n00016761. | |
| 245 | 1 | 0 | |a Data science for business : |b [what you need to know about data mining and data-analytic thinking] / |c Foster Provost and Tom Fawcett. |
| 246 | 1 | 4 | |a What you need to know about data mining and data-analytic thinking. |
| 250 | |a 1st ed. | ||
| 264 | 1 | |a Sebastopol, Calif. : |b O'Reilly, |c 2013. | |
| 300 | |a xxi, 386 pages : |b illustrations ; |c 24 cm. | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a unmediated |b n |2 rdamedia. | ||
| 338 | |a volume |b nc |2 rdacarrier. | ||
| 500 | |a Subtitle from cover. | ||
| 504 | |a Includes bibliographical references (pages 361-368) and index. | ||
| 505 | 0 | |a Introduction : data-analytic thinking -- Business problems and data science solutions -- Introduction to predictive modeling : from correlation to supervised segmentation -- Fitting a model to data -- Overfitting and its avoidance -- Similarity, neighbors, and clusters -- Decision analytic thinking I : what is a good model? -- Visualizing model performance -- Evidence and probabilities -- Representing and mining text -- Decision analytic thinking II : toward analytical engineering -- Other data science tasks and techniques -- Data science and business strategy -- Conclusion. | |
| 520 | |a Provides an introduction to the fundamental principles of data science, walking the reader through the "data-analytic thinking" necessary for extracting useful knowledge and business value from collected data. | ||
| 650 | 0 | |a Data mining. |0 https://id.loc.gov/authorities/subjects/sh97002073. | |
| 650 | 0 | |a Big data. |0 https://id.loc.gov/authorities/subjects/sh2012003227. | |
| 650 | 0 | |a Information science. |0 https://id.loc.gov/authorities/subjects/sh85066150. | |
| 650 | 0 | |a Business |x Data processing. |0 https://id.loc.gov/authorities/subjects/sh85018264. | |
| 650 | 7 | |a Sciences de l'information. |2 eclas. | |
| 650 | 7 | |a Big data. |2 fast |0 (OCoLC)fst01892965. | |
| 650 | 7 | |a Business |x Data processing. |2 fast |0 (OCoLC)fst00842293. | |
| 650 | 7 | |a Data mining. |2 fast |0 (OCoLC)fst00887946. | |
| 650 | 7 | |a Information science. |2 fast |0 (OCoLC)fst00972640. | |
| 650 | 7 | |a Data Mining. |2 gnd |0 (DE-588)4428654-5. | |
| 650 | 7 | |a Massendaten. |2 gnd |0 (DE-588)4802620-7. | |
| 650 | 7 | |a Business Intelligence. |2 gnd |0 (DE-588)4588307-5. | |
| 650 | 4 | |a Massendaten. |0 (DE-588)4802620-7. | |
| 650 | 0 | |a Business intelligence. |0 https://id.loc.gov/authorities/subjects/sh85018300. | |
| 650 | 4 | |a Sciences de l'information. | |
| 650 | 2 | |a Data Mining. |0 https://id.nlm.nih.gov/mesh/D057225. | |
| 650 | 2 | |a Information Science. |0 https://id.nlm.nih.gov/mesh/D007254. | |
| 650 | 2 | |a Commerce. |0 https://id.nlm.nih.gov/mesh/D003132. | |
| 650 | 2 | |a Electronic Data Processing. |0 https://id.nlm.nih.gov/mesh/D001330. | |
| 700 | 1 | |a Fawcett, Tom. |0 https://id.loc.gov/authorities/names/n2004016206. | |
| 856 | 4 | 2 | |z Additional Information at Google Books |u http://books.google.com/books?isbn=1449361323 |
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