1、对数据的描述。（配料，指出应使用那些原料） 2、对操作的描述。（操作步骤，如何用料做成菜肴） 3、程序设计方法。 4、语言工具和环境

受弯构件受剪性能的试验研究 斜截面受剪承载力计算 构造要求 偏心受力构件的受剪承载力

•定义：保险金额，是指一个保险合同项下保险公司承担赔偿或给付保险金责任的最高限额，即投保人对保险标的的实际投保金额；同时又是保险公司收取保险费的计算基础。•确定方式：在人身保险合同中，人身的价值无法衡量，保险金额是人身保险合同双方约定的，由保险人承担的最高给付的限额或实际给付的金额

本文结合作者对该课程中一些概念和方法的理解，着重分析了这些概念的深层含义和一些方法的适用范围，包括材料强度平均值、标准值和设计值的概念、应用及其转换，混凝土弹性模量的应用，T形和I形截面受扭塑性抵抗矩的计算，以及现浇混凝土楼盖结构的计算方法等

附表 1 等截面等跨连续梁在常用荷载作用下的内力系数表 附表 2 双向板计算系数表 附表 3 风荷载特征值 附表 7 框架柱反弯点高度比

1.1 Understand the need for computerized support of managerial decision making 1.2 Recognize the evolution of such computerized support to the current state—analytics/data science 1.3 Describe the business intelligence (BI) methodology and concepts 1.4 Understand the various types of analytics, and see selected applications 1.5 Understand the analytics ecosystem to identify various key players and career opportunities

3.1 Understand the basic definitions and concepts of data warehousing 3.2 Understand data warehousing architectures 3.3 Describe the processes used in developing and managing data warehouses 3.4 Explain data warehousing operations 3.5 Explain the role of data warehouses in decision support

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

5.1 Describe text mining and understand the need for text mining 5.2 Differentiate among text analytics, text mining, and data mining 5.3 Understand the different application areas for text mining 5.4 Know the process of carrying out a text mining project 5.5 Appreciate the different methods to introduce structure to text-based data 5.6 Describe sentiment analysis 5.7 Develop familiarity with popular applications of sentiment analysis 5.8 Learn the common methods for sentiment analysis 5.9 Become familiar with speech analytics as it relates to sentiment analysis

7.1 Learn what Big Data is and how it is changing the world of analytics 7.2 Understand the motivation for and business drivers of Big Data analytics 7.3 Become familiar with the wide range of enabling technologies for Big Data analytics 7.4 Learn about Hadoop, MapReduce, and NoSQL as they relate to Big Data analytics 7.5 Compare and contrast the complementary uses of data warehousing and Big Data technologies 7.6 Become familiar with select Big Data platforms and services 7.7 Understand the need for and appreciate the capabilities of stream analytics 7.8 Learn about the applications of stream analytics