個案資料

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. Chapter 9 provides an introduction to data mining and its applications to predictive analysis. Data mining explores patterns and relationships in the vast amounts of data that businesses collect. The information that is extracted is used to predict future business outcomes. Several data mining methodologies and tasks are described, including data preparation, knowledge representation, pattern evaluation, and descriptive and predictive data mining. Data mining techniques can also be categorized into supervised and unsupervised learning. Key methodologies of data mining include anomalies detection, association learning, classification, clustering, sequence, prediction, and time series and forecasting. The growing application areas of data mining are discussed, including applications in machine learning. The relationship between data mining and machine learning as well as different types of machine learning are also introduced.

• Business analysis
• Predictive analytics
• Technology and analytics
• Management performance
• Decision making