关联规则基本概念 CARMA算法简介 CARMA模块的基本概念 案例分析及Clementine操作步骤 购物篮分析----Tabular类型数据 网络日志分析----Transactional类型数据 值得注意的问题 CARMA算法原理（参考）

《商务智能：数据分析的管理视角 Business Intelligence, Analytics, and Data Science：A Managerial Perspective》教学资源（PPT课件，第3版）Chapter 01 An Overview of Business Intelligence, Analytics, and Decision Support

《商务智能：数据分析的管理视角 Business Intelligence, Analytics, and Data Science：A Managerial Perspective》教学资源（PPT课件，第3版）Chapter 03 Business Reporting, Visual Analytics, and Business Performance Management

《商务智能：数据分析的管理视角 Business Intelligence, Analytics, and Data Science：A Managerial Perspective》教学资源（PPT课件，第3版）Chapter 05 Text and Web Analytics

《商务智能：数据分析的管理视角 Business Intelligence, Analytics, and Data Science：A Managerial Perspective》教学资源（PPT课件，第3版）Chapter 07 Business Analytics：Emerging Trends and Future Impacts

2.1 Understand the nature of data as it relates to business intelligence (BI) and analytics 2.2 Learn the methods used to make real-world data analytics ready 2.3 Describe statistical modeling and its relationship to business analytics 2.4 Learn about descriptive and inferential statistics 2.5 Define business reporting, and understand its historical evolution

4.1 Define data mining as an enabling technology for business analytics 4.2 Understand the objectives and benefits of data mining 4.3 Become familiar with the wide range of applications of data mining 4.4 Learn the standardized data mining processes 4.5 Learn different methods and algorithms of data mining 4.6 Build awareness of the existing data mining software tools 4.7 Understand the privacy issues, pitfalls, and myths of data mining

6.1 Understand the applications of prescriptive analytics techniques in combination with reporting and predictive analytics 6.2 Understand the basic concepts of analytical decision modeling 6.3 Understand the concepts of analytical models for selected decision problems, including linear programming and simulation models for decision support 6.4 Describe how spreadsheets can be used for analytical modeling and solutions 6.5 Explain the basic concepts of optimization and when to use them 6.6 Describe how to structure a linear programming model 6.7 Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking 6.8 Understand the concepts and applications of different types of simulation 6.9 Understand potential applications of discrete event simulation

8.1 Explore some of the emerging technologies that may impact analytics, business intelligence (BI), and decision support 8.2 Describe the emerging Internet of Things (IoT) phenomenon, potential applications, and the IoT ecosystem 8.3 Describe the current and future use of cloud computing in business analytics 8.4 Describe how geospatial and location-based analytics are assisting organizations 8.5 Describe the organizational impacts of analytics applications 8.6 List and describe the major ethical and legal issues of analytics implementation 8.7 Identify key characteristics of a successful data science professional

《商务智能：数据分析的管理视角 Business Intelligence, Analytics, and Data Science：A Managerial Perspective》教学资源（教师手册，原书第4版）02 Descriptive Analytics I：Nature of Data, Statistical Modeling, and Visualization