Business Analytics, 4/e (Hardocver)
暫譯: 商業分析,第4版(精裝本)
Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann
- 出版商: Cengage Learning
- 出版日期: 2020-03-10
- 定價: $1,380
- 售價: 9.8 折 $1,352
- 語言: 英文
- 頁數: 880
- ISBN: 0357131789
- ISBN-13: 9780357131787
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其他版本:
Business Analytics, 4/e (AE-Paperback)
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相關主題
商品描述
本書序言
●NEW ONLINE CHAPTER APPENDICES FOCUS ON HOW TO USE R SOFTWARE EFFECTIVELY. This edition's Chapters 1 through 9 offer online appendices detailing how to use the popular open-source software R. The appendices introduce R for descriptive statistics, data visualization, probability, regression and data mining. The authors use R Studio for an easier introduction for both you and your students. The R appendices for descriptive data mining (Ch. 4) and prescriptive data mining (Ch. 9) describe these methods using Rattle, an R package that provides a "point-and-click" graphical user interface as well native R commands.
●NEW SECTION HIGHLIGHTS LEGAL AND ETHICAL ISSUES IN THE USE OF DATA AND ANALYTICS. Chapter 1 includes a new section that addresses common legal and ethical issues related to the use of data and analytics. This new legal and ethical section discusses recent data privacy laws as well as ethical issues that both practitioners and consumers of analytics models should consider.
●NEW HOMEWORK PROBLEMS AND CASES HIGHLIGHT DATA MINING AND CUMULATIVE KNOWLEDGE. The chapters on data mining in this edition contain even more problems that do not require specialized software. This gives you the flexibility to introduce these important topics, even if you do not want students to have to learn additional software to solve the problems. This edition also introduces numerous additional cases throughout the text, including cases that integrate topics from multiple chapters to emphasize how various analytics topics interact and build upon each another.
●NEW ONLINE APPENDIX INTRODUCES HOW TO USE TABLEAU FOR DATA VISUALIZATION. This brand-new online appendix details how to maximize the features of Tableau for data visualization. The authors apply their proven, step-by-step presentation methods to clearly guide students through using this powerful software to produce useful charts for analytics.
●REVISED DATA MINING CHAPTERS OFFER CLEARER PRESENTATION OF CONCEPTS. The authors have reorganized and updated this edition's data mining chapters to ensure students thoroughly understand the presentation. The descriptive data mining chapter (Ch. 5) now appears after the probability chapter so that the data mining discussion can directly integrate notions of probability within the explanations.
本書特色
●ANALYTICS IN ACTION EFFECTIVELY DEMONSTRATE THE IMPORTANCE OF CONCEPTS IN BUSINESS TODAY. Each chapter contains an Analytics in Action feature that presents interesting examples of how professionals use business analytics in actual practice today. Engaging examples are drawn from organizations in a variety of areas, including healthcare, finance, manufacturing and marketing.
●PRACTICAL, RELEVANT PROBLEMS HELP STUDENTS MASTER CONCEPTS AND HANDS-ON SKILLS. Applications drawn from all functional business areas, including finance, marketing and operations, provide important practice at a variety of levels of difficulty. Time-saving data sets are available for most exercises and cases.
●STEP-BY-STEP INSTRUCTIONS EXPLAIN IMPORTANT ANALYTICAL STEPS. Clear instructions show students how to use a variety of leading software programs to perform the analyses discussed in the text.
●COMPLETELY INTEGRATED COVERAGE OF EXCEL DEMONSTRATES THE LATEST METHODS FOR SOLVING PRACTICAL PROBLEMS. Clear, step-by-step instructions teach students to use Excel as a tool for applying concepts in the book. The authors also include by-hand calculations to highlight specific analytical insights, when appropriate.
●ONLINE DATA FILES AND MODEL FILES SAVE TIME. All data sets used as examples and used within student exercises are provided online for convenient student download. DATAfiles are files that contain data that corresponds to examples and problems given in the text. MODELfiles contain additional modeling features that highlight the extensive use of Excel formulas or the use of other software.
商品描述(中文翻譯)
本書序言
● 新增的線上章節附錄專注於如何有效使用 R 軟體。本版的第 1 到第 9 章提供了線上附錄,詳細說明如何使用流行的開源軟體 R。這些附錄介紹了 R 在描述性統計、資料視覺化、機率、迴歸和資料探勘中的應用。作者使用 R Studio 以便於您和您的學生入門。描述性資料探勘(第 4 章)和規範性資料探勘(第 9 章)的 R 附錄使用 Rattle 這個 R 套件來描述這些方法,Rattle 提供了一個「點擊即用」的圖形使用者介面以及原生的 R 命令。
● 新增的部分強調使用資料和分析中的法律與倫理問題。第 1 章包含一個新部分,討論與資料和分析使用相關的常見法律和倫理問題。這個新的法律與倫理部分討論了最近的資料隱私法以及從業者和分析模型消費者應考慮的倫理問題。
● 新增的作業問題和案例強調資料探勘和累積知識。本版中有關資料探勘的章節包含更多不需要專業軟體的問題。這使您能夠靈活地介紹這些重要主題,即使您不希望學生學習額外的軟體來解決問題。本版還在文本中引入了許多額外的案例,包括整合多個章節主題的案例,以強調各種分析主題之間的互動和相互建構。
● 新增的線上附錄介紹如何使用 Tableau 進行資料視覺化。這個全新的線上附錄詳細說明如何最大化 Tableau 在資料視覺化中的功能。作者應用他們經過驗證的逐步呈現方法,清楚地指導學生使用這個強大的軟體來製作有用的分析圖表。
● 修訂的資料探勘章節提供更清晰的概念呈現。作者已重新組織並更新本版的資料探勘章節,以確保學生徹底理解呈現內容。描述性資料探勘章節(第 5 章)現在位於機率章節之後,以便資料探勘的討論能直接整合機率的概念於解釋中。
本書特色
● 實際案例有效展示當今商業中概念的重要性。每章都包含一個「實際案例」特點,呈現專業人士如何在當今實踐中使用商業分析的有趣範例。引人入勝的範例來自於各種領域的組織,包括醫療保健、金融、製造和行銷。
● 實用且相關的問題幫助學生掌握概念和實作技能。來自所有功能性商業領域的應用,包括金融、行銷和運營,提供了不同難度層級的重要練習。大多數練習和案例都有省時的資料集可用。
● 逐步指導解釋重要的分析步驟。清晰的指導顯示學生如何使用各種領先的軟體程序來執行文本中討論的分析。
● 完全整合的 Excel 覆蓋展示解決實際問題的最新方法。清晰的逐步指導教導學生如何使用 Excel 作為應用書中概念的工具。作者還在適當的情況下包含手動計算,以突顯特定的分析見解。
● 線上資料檔案和模型檔案節省時間。所有用作範例和學生練習的資料集均提供線上下載,方便學生使用。DATAfiles 是包含與文本中給出的範例和問題相對應的資料的檔案。MODELfiles 包含額外的建模功能,突顯 Excel 公式的廣泛使用或其他軟體的使用。
目錄大綱
1. Introduction.
2. Descriptive Statistics.
3. Data Visualization.
4. Probability: An Introduction to Modeling Uncertainty.
5. Descriptive Data Mining.
6. Statistical Inference.
7. Linear Regression.
8. Time Series Analysis and Forecasting.
9. Predictive Data Mining.
10. Spreadsheet Models.
11. Monte Carlo Simulation.
12. Linear Optimization Models.
13. Integer Linear Optimization Models.
14. Nonlinear Optimization Models.
15. Decision Analysis.
Appendix A: Basics of Excel.
Appendix B: Database Basics with Microsoft Access.
Appendix C: Solutions to Even-Numbered Questions (MindTap Reader).
目錄大綱(中文翻譯)
1. Introduction.
2. Descriptive Statistics.
3. Data Visualization.
4. Probability: An Introduction to Modeling Uncertainty.
5. Descriptive Data Mining.
6. Statistical Inference.
7. Linear Regression.
8. Time Series Analysis and Forecasting.
9. Predictive Data Mining.
10. Spreadsheet Models.
11. Monte Carlo Simulation.
12. Linear Optimization Models.
13. Integer Linear Optimization Models.
14. Nonlinear Optimization Models.
15. Decision Analysis.
Appendix A: Basics of Excel.
Appendix B: Database Basics with Microsoft Access.
Appendix C: Solutions to Even-Numbered Questions (MindTap Reader).