個案資料
了解個案內容與學習目標，選擇授課教師及使用課程。
Descriptive versus Predictive Analytics
內容大綱
Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter starts with a discussion about what predictive analytics is and how it differs from descriptive analytics. While descriptive analytics is the process of exploring and explaining data to reveal trends, predictive analytics is about predicting future business outcomes. It uses several models that can be categorized into logic-driven models and data-driven models. A number of tools and statistical models are introduced, which are considered prerequisites to predictive analytics. Descriptions and application areas of these prerequisite tools - including probability concepts, probability distributions, and correlation analysis - are given. The understanding of these prerequisite tools is critical in understanding and applying predictive analytics models.