全真模拟试题(一) 一、单项选择题(在每小题的4个备选答案中,选出正确的答案,并将其号码填在题干的括号内。每小题2分,共24分) 1.若某线性表中最常用的操作是取第1个元素和找第i个元素的前趋元素,则采用 ()存储方式最节省时间。 ①单链表 ②双链表 ③单向循环 ④顺序表

第一部分选择题(共30分) 一、单项选择题(本大题共15小题,每小题2,共30分)。在每小题列出的四个选项中只有一个是符合题 目要求的,请将其代码填在题后的括号内。错选或未选均无分

一、单项选择题(本大题共15小题,每小题2分共30分)在每小题列出的四个选项中只有一个是符合题目要求的,请将其代码填在题后的括号内。错选或未选均无分 1、若结点的存储地址与其关键字之间存在的某种映射关系,则称这种存储结构为() A顺序存储结构 B链式存储结构 C索引存储结构 D散列存储结构

一、单项选择题(本大题共15小题,每小题2分,共30分)在每小题列出的四个选项中只有一个是符合题目要求的,请将其代码填在题后的括号内。错选或未选均无分 1.下列各式中,按增长率由小至大的顺序正确排列的是() .,n!, 2\, B.,2\, nlogn,2100 C.2n, logn, nlogn, n3/2 D.2100, logn, 2n

一、选择题 1.某内排序方法的稳定性是指()。 A.该排序算法不允许有相同的关键字记录 B.该排序字记录 C.平均时间为0( log)的排序方法 D.以上都不对

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

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

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