Data mining for business analytics: concepts, techniques and applications in Python
Gespeichert in:
| Beteiligte Personen: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2020
|
| Schlagwörter: | |
| Abstract: | "This book supplies insightful, detailed guidance on fundamental data mining techniques. The book guides readers through the use of Python software for developing predictive models and techniques in order to describe and find patterns in data. The authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, with a focus on analytics rather than programming. The book includes discussions of Python subroutines, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Topics covered include time series, text mining, and dimension reduction. Each chapter concludes with exercises that allow readers to expand their comprehension of the presented material. Over a dozen cases that require use of the different data mining techniques are introduced, and a related Web site features over two dozen data sets, exercise solutions, PowerPoint slides, and case solutions"-- |
| Umfang: | XXIX, 574 Seiten Diagramme, Karten 27 cm |
| ISBN: | 9781119549840 1119549841 |
Internformat
MARC
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| 245 | 1 | 0 | |a Data mining for business analytics |b concepts, techniques and applications in Python |c Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel |
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2020 | |
| 300 | |a XXIX, 574 Seiten |b Diagramme, Karten |c 27 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 505 | 8 | |a Foreword / by Gareth James -- Foreword / by Ravi Bapna -- Preface to the Python edition -- Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating predictive performance -- Multiple linear regression -- k-nearest neighbors (kNN) -- The naive Bayes classifier -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Combining methods : ensembles and uplift modeling -- Association rules and collaborative filtering -- Cluster analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Social network analytics -- Text mining -- Cases | |
| 520 | 3 | |a "This book supplies insightful, detailed guidance on fundamental data mining techniques. The book guides readers through the use of Python software for developing predictive models and techniques in order to describe and find patterns in data. The authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, with a focus on analytics rather than programming. The book includes discussions of Python subroutines, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Topics covered include time series, text mining, and dimension reduction. Each chapter concludes with exercises that allow readers to expand their comprehension of the presented material. Over a dozen cases that require use of the different data mining techniques are introduced, and a related Web site features over two dozen data sets, exercise solutions, PowerPoint slides, and case solutions"-- | |
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| 650 | 0 | 7 | |a Business Intelligence |0 (DE-588)4588307-5 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Business mathematics / Computer programs | |
| 653 | 0 | |a Business / Data processing | |
| 653 | 0 | |a Data mining | |
| 653 | 0 | |a Python (Computer program language) | |
| 653 | 0 | |a Business mathematics / Computer programs | |
| 653 | 0 | |a Data mining | |
| 653 | 0 | |a Python (Computer program language) | |
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| 689 | 0 | 1 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | 2 | |a Python |g Programmiersprache |0 (DE-588)4434275-5 |D s |
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| 700 | 1 | |a Patel, Nitin R. |e Verfasser |0 (DE-588)170703592 |4 aut | |
| 776 | 0 | 8 | |i Online version |a Shmueli, Galit, 1971- |t Data mining for business analytics |d Hoboken, NJ : John Wiley & Sons, Inc., 2020 |z 9781119549857 |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-032030061 | |
Datensatz im Suchindex
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| author | Shmueli, Galit 1971- Bruce, Peter C. 1953- Gedeck, Peter Patel, Nitin R. |
| author_GND | (DE-588)137189265 (DE-588)1104275260 (DE-588)170703592 |
| author_facet | Shmueli, Galit 1971- Bruce, Peter C. 1953- Gedeck, Peter Patel, Nitin R. |
| author_role | aut aut aut aut |
| author_sort | Shmueli, Galit 1971- |
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| building | Verbundindex |
| bvnumber | BV046618290 |
| classification_rvk | QH 500 ST 530 |
| contents | Foreword / by Gareth James -- Foreword / by Ravi Bapna -- Preface to the Python edition -- Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating predictive performance -- Multiple linear regression -- k-nearest neighbors (kNN) -- The naive Bayes classifier -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Combining methods : ensembles and uplift modeling -- Association rules and collaborative filtering -- Cluster analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Social network analytics -- Text mining -- Cases |
| ctrlnum | (OCoLC)1153986809 (DE-599)BVBBV046618290 |
| discipline | Informatik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV046618290 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T18:56:26Z |
| institution | BVB |
| isbn | 9781119549840 1119549841 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032030061 |
| oclc_num | 1153986809 |
| open_access_boolean | |
| owner | DE-29T DE-Aug4 DE-706 DE-1043 DE-739 |
| owner_facet | DE-29T DE-Aug4 DE-706 DE-1043 DE-739 |
| physical | XXIX, 574 Seiten Diagramme, Karten 27 cm |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Wiley |
| record_format | marc |
| spelling | Shmueli, Galit 1971- Verfasser (DE-588)137189265 aut Data mining for business analytics concepts, techniques and applications in Python Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel Hoboken, NJ Wiley 2020 XXIX, 574 Seiten Diagramme, Karten 27 cm txt rdacontent n rdamedia nc rdacarrier Foreword / by Gareth James -- Foreword / by Ravi Bapna -- Preface to the Python edition -- Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating predictive performance -- Multiple linear regression -- k-nearest neighbors (kNN) -- The naive Bayes classifier -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Combining methods : ensembles and uplift modeling -- Association rules and collaborative filtering -- Cluster analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Social network analytics -- Text mining -- Cases "This book supplies insightful, detailed guidance on fundamental data mining techniques. The book guides readers through the use of Python software for developing predictive models and techniques in order to describe and find patterns in data. The authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, with a focus on analytics rather than programming. The book includes discussions of Python subroutines, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Topics covered include time series, text mining, and dimension reduction. Each chapter concludes with exercises that allow readers to expand their comprehension of the presented material. Over a dozen cases that require use of the different data mining techniques are introduced, and a related Web site features over two dozen data sets, exercise solutions, PowerPoint slides, and case solutions"-- Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Business Intelligence (DE-588)4588307-5 gnd rswk-swf Business mathematics / Computer programs Business / Data processing Data mining Python (Computer program language) Business Intelligence (DE-588)4588307-5 s Data Mining (DE-588)4428654-5 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Bruce, Peter C. 1953- Verfasser (DE-588)1104275260 aut Gedeck, Peter Verfasser aut Patel, Nitin R. Verfasser (DE-588)170703592 aut Online version Shmueli, Galit, 1971- Data mining for business analytics Hoboken, NJ : John Wiley & Sons, Inc., 2020 9781119549857 |
| spellingShingle | Shmueli, Galit 1971- Bruce, Peter C. 1953- Gedeck, Peter Patel, Nitin R. Data mining for business analytics concepts, techniques and applications in Python Foreword / by Gareth James -- Foreword / by Ravi Bapna -- Preface to the Python edition -- Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating predictive performance -- Multiple linear regression -- k-nearest neighbors (kNN) -- The naive Bayes classifier -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Combining methods : ensembles and uplift modeling -- Association rules and collaborative filtering -- Cluster analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Social network analytics -- Text mining -- Cases Python Programmiersprache (DE-588)4434275-5 gnd Data Mining (DE-588)4428654-5 gnd Business Intelligence (DE-588)4588307-5 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4428654-5 (DE-588)4588307-5 |
| title | Data mining for business analytics concepts, techniques and applications in Python |
| title_auth | Data mining for business analytics concepts, techniques and applications in Python |
| title_exact_search | Data mining for business analytics concepts, techniques and applications in Python |
| title_full | Data mining for business analytics concepts, techniques and applications in Python Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel |
| title_fullStr | Data mining for business analytics concepts, techniques and applications in Python Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel |
| title_full_unstemmed | Data mining for business analytics concepts, techniques and applications in Python Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel |
| title_short | Data mining for business analytics |
| title_sort | data mining for business analytics concepts techniques and applications in python |
| title_sub | concepts, techniques and applications in Python |
| topic | Python Programmiersprache (DE-588)4434275-5 gnd Data Mining (DE-588)4428654-5 gnd Business Intelligence (DE-588)4588307-5 gnd |
| topic_facet | Python Programmiersprache Data Mining Business Intelligence |
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