Data mining and predictive analytics for business decisions: a case study approach
With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using m...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Herndon
Mercury Learning and Information
[2023]
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| Schlagworte: | |
| Online-Zugang: | DE-1043 DE-1046 DE-858 DE-859 DE-860 DE-91 DE-706 DE-739 URL des Erstveröffentlichers |
| Zusammenfassung: | With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis. Using the CRISP-DM data mining standard, the early chapters cover conducting the preparatory steps in data mining: translating business information needs into framed analytical questions and data preparation. The Jamovi and the JASP interfaces are used with R and the Orange3 data mining interface with Python. Where appropriate, Voyant and other open-source programs are used for text analytics. The techniques covered in this book range from basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics. Includes companion files with case study files, solution spreadsheets, data sets and charts, etc. from the book. FEATURES:Covers basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analyticsUses R, Python, Jamovi and JASP interfaces, and the Orange3 data mining interfaceIncludes companion files with the case study files from the book, solution spreadsheets, data sets, etc |
| Beschreibung: | 1 Online-Ressource (272 Seiten) |
| ISBN: | 9781683926740 |
| DOI: | 10.1515/9781683926740 |
Internformat
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Datensatz im Suchindex
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| spelling | Fortino, Andres Verfasser aut Data mining and predictive analytics for business decisions a case study approach Andres Fortino Herndon Mercury Learning and Information [2023] © 2023 1 Online-Ressource (272 Seiten) txt rdacontent c rdamedia cr rdacarrier With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis. Using the CRISP-DM data mining standard, the early chapters cover conducting the preparatory steps in data mining: translating business information needs into framed analytical questions and data preparation. The Jamovi and the JASP interfaces are used with R and the Orange3 data mining interface with Python. Where appropriate, Voyant and other open-source programs are used for text analytics. The techniques covered in this book range from basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics. Includes companion files with case study files, solution spreadsheets, data sets and charts, etc. from the book. FEATURES:Covers basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analyticsUses R, Python, Jamovi and JASP interfaces, and the Orange3 data mining interfaceIncludes companion files with the case study files from the book, solution spreadsheets, data sets, etc Data COMPUTERS / Database Management / Data Mining bisacsh Erscheint auch als Druck-Ausgabe 9781683926757 https://doi.org/10.1515/9781683926740?locatt=mode:legacy Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Fortino, Andres Data mining and predictive analytics for business decisions a case study approach Data COMPUTERS / Database Management / Data Mining bisacsh |
| title | Data mining and predictive analytics for business decisions a case study approach |
| title_auth | Data mining and predictive analytics for business decisions a case study approach |
| title_exact_search | Data mining and predictive analytics for business decisions a case study approach |
| title_exact_search_txtP | Data Mining and Predictive Analytics for Business Decisions A Case Study Approach |
| title_full | Data mining and predictive analytics for business decisions a case study approach Andres Fortino |
| title_fullStr | Data mining and predictive analytics for business decisions a case study approach Andres Fortino |
| title_full_unstemmed | Data mining and predictive analytics for business decisions a case study approach Andres Fortino |
| title_short | Data mining and predictive analytics for business decisions |
| title_sort | data mining and predictive analytics for business decisions a case study approach |
| title_sub | a case study approach |
| topic | Data COMPUTERS / Database Management / Data Mining bisacsh |
| topic_facet | Data COMPUTERS / Database Management / Data Mining |
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