學門類別
哈佛
- General Management
- Marketing
- Entrepreneurship
- International Business
- Accounting
- Finance
- Operations Management
- Strategy
- Human Resource Management
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- Business Ethics
- Organizational Behavior
- Information Technology
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- Business & Government Relations
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- Economics
- Teaching & the Case Method
最新個案
- A practical guide to SEC ï¬nancial reporting and disclosures for successful regulatory crowdfunding
- Quality shareholders versus transient investors: The alarming case of product recalls
- The Health Equity Accelerator at Boston Medical Center
- Monosha Biotech: Growth Challenges of a Social Enterprise Brand
- Assessing the Value of Unifying and De-duplicating Customer Data, Spreadsheet Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise, Data Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise
- Board Director Dilemmas: The Tradeoffs of Board Selection
- Barbie: Reviving a Cultural Icon at Mattel (Abridged)
- Happiness Capital: A Hundred-Year-Old Family Business's Quest to Create Happiness
Advanced Business Analysis
內容大綱
Business Intelligence: Making Decisions through Data Analytics is a six-chapter book written by Dr. Jerzy Surma, an assistant professor at Warsaw School of Economics, and published by Business Expert Press in January of 2011. Written for managers, business consultants, and undergraduate and postgraduate students in business administration, the book explores the use of business intelligence (BI), including data warehousing and data analytics, to support managerial decision-making. The author starts by examining data model infrastructure, then data preparation, followed by data analysis, integration, knowledge discovery, and finally the actual use of discovered knowledge. All examples are based on recent achievements in BI. Finally, the book outlines an overview of a methodology that takes into account the complexity of developing applications in an integrated BI environment. In Chapter 4, the author examines data exploration and the business applications of advanced analyses. The four classic tasks of data mining are discussed at length: classification, cluster analysis, estimation, and mining association rules. Limitations of data exploration methods are analyzed in detail, and standard business applications are reviewed