1.确定a与a,使下列各无穷小量或无穷大量等价于(~)ax:

一、基本概念 1.设函数f定义在无穷区间[a,+∞)上,且在任何有限区间[a,u]上可积,如果存在极限

4.1导数定义和某些初等函数的导数 1.定义设y=f(x)在(a,b)上定义,x∈(a,b),若极限

16.1 D/A转换器概述 D/A转换器输入的是数字量，经转换后输出的是模拟量。 有关D/A转换器的技术性能指标很多，例如绝对精度、相对精 度、线性度、输出电压范围、温度系数、输入数字代码种类 （二进制或BCD码）等。 1) 分辩率 分辨率是D/A转换器对输入量变化敏感程度的描述，与输 入数字量的位数有关。如果数字量的位数为n，则D/A转换器 的分辨率为2-n。这就意味着数/模转换器能对满刻度的2-n输入 量作出反应

定理假设函数x)在区间[a,b]上连续,函数x=()满 足条件:(1)o(a)=a,以B)=b;(2)∞(1)在[a,(或B,a)上具 有连续导数,且其值域不越出[a,b],则有

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

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

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

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

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