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

大学化学实验总体概况 第一章 学生实验守则 第二章 实验的程序与要求 第三章 大学化学实验的常用仪器 第四章 大学化学实验的基本操作 第五章 大学化学实验中的数据处理 第六章 实验 实验一 溶液的配制和酸碱滴定(选做) 实验二 化学反应速率与化学平衡(选做) 实验三 醋酸解离度和解离常数的测定——pH法 实验四 电解质溶液 实验五 氧化还原反应与电化学 实验六 主族元素 实验七 设计实验——混凝土溶蚀规律实验

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

1. 汇编语言基本概念 2. 汇编语言指令分类 3. 数据与转移地址的寻址方式 4.8086的六类指令 5. 总结

第1章 定量分析化学概论 第2章 酸碱平衡和酸碱滴定法 第3章 络合滴定法 第4章 氧化还原滴定法 第5章 重量分析法和沉淀滴定法 第6章 吸光光度法 第7章 分析化学中的数据处理 第8章 分析化学中常用的分离和富集方法 第9章 复杂物质的分析示例

搞研究的人离不开文献，可是很多院校未能购卖国内外商业数据库，如 PUBMED、ElseVier 等，因而检索国外全文文献很复杂。就是一些中文的要是没有给银子，也会难得到原文，方 便的得到全文往往成为少数学校的专利。从网络上积累了一些资料，跟据自己平时的积累进 行了一些修改，写了这个文章，结果发表在南大 BBS 上很是得到欢迎，所以决心写的好一些