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

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

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

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

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

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

差分放大器简介 简单差分放大器 基本差分对放大器 大信号共模特性 大信号差分特性 小信号差分特性 小信号共模特性 MOS管做负载的基本差分对放大器 差分放大器的应用－Gilbert单元

11.1 LED显示器 11.2 LED显示器和接口 11.3 数码管动态显示的硬件电路设计 11.4 LED静态显示及实验 11.5 74LS164静态显示电路源程序 11.6 LED显示驱动器MAX7221 11.7 LED动态显示及实验 11.8 实时时钟程序的分析

第二十章 创造教育 创造教育概述 我国创造教育的发展 创造教育的原则和目标 创造教育的实施 创造教育的教学模式 第21章 创造型人才 创造型人才的涵义和特征 创造型人才的客观主观因素 创造型人才队伍建设 第22章 创造的个性心理品质（上） 理想和品德 勇气和意志 自信心和好奇心 抓住机遇和惜时间 竞争意识和合作精神 第23章 创造的个性心理品质（下） 信息观和名利观 观察力和记忆力 思考力和借鉴力 迷恋性和严谨性 独立性和综合性

5.1 放大电路的组成及工作原理 5.2 图解分析法 5.3 计算分析法 5.4 放大电路的三种接法 5.5 阻容耦合放大电路 5.6 场效应管放大电路 5.7 多级放大电路 5.8 放大器的通频带