Data science for business: [what you need to know about data mining and data-analytic thinking]
(Book)
Provost, F., & Fawcett, T. (2013). Data science for business: [what you need to know about data mining and data-analytic thinking]. Sebastopol, Calif., O'Reilly.
Chicago / Turabian - Author Date Citation (style guide)Provost, Foster, 1964- and Tom. Fawcett. 2013. Data Science for Business: [what You Need to Know About Data Mining and Data-analytic Thinking]. Sebastopol, Calif., O'Reilly.
Chicago / Turabian - Humanities Citation (style guide)Provost, Foster, 1964- and Tom. Fawcett, Data Science for Business: [what You Need to Know About Data Mining and Data-analytic Thinking]. Sebastopol, Calif., O'Reilly, 2013.
MLA Citation (style guide)Provost, Foster and Tom Fawcett. Data Science for Business: [what You Need to Know About Data Mining and Data-analytic Thinking]. Sebastopol, Calif., O'Reilly, 2013.
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.
Notes
Record Information
Last Sierra Extract Time | Mar 13, 2024 04:22:55 PM |
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Last File Modification Time | Mar 13, 2024 04:23:29 PM |
Last Grouped Work Modification Time | Mar 13, 2024 04:23:11 PM |
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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 | ||
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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 | |
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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 | |
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