主讲内容：Data Pump基础知识、使用Data Pump导出表空间、数据库、数据表等； 一．认识Data Pump工具 二．准备工作 三．导出数据 四．导入数据

• Basic database terminology and concepts • Security requirements for databases • Implementing access controls in databases • Protecting sensitive data • Data mining and big data

To develop a subclass from a superclass through inheritance (§8.2). To invoke the superclass’s constructors and methods using the super keyword (§8.3). To override methods in the subclass (§8.4). To explore the useful methods (equals(Object), hashCode(), toString(), finalize(), clone(), and getClass()) in the Object class (§8.5, §8.11 Optional). To comprehend polymorphism, dynamic binding, and generic programming (§8.6). To describe casting and explain why explicit downcasting is necessary (§8.7). To understand the effect of hiding data fields and static methods (§8.8 Optional). To restrict access to data and methods using the protected visibility modifier (§8.9). To declare constants, unmodifiable methods, and nonextendable class using the final modifier (§8.10). To initialize data using initialization blocks and distinguish between instance initialization and static initialization blocks (§8.12 Optioanl)

Introduction Examines a de facto standard for external data representation and presentation as well as a set of library procedures used to perform data conversion Describes the general motivations for using an external data representation and the details of one particular implementation

1. to know the transmission media of data signals, the structure of full-duplex data transmission system and its peration and the coding of data signals 2. to understand the main idea and

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

Time series vs Cross sectional e Time series data has a temporal ordering unlike cross-section data Will need to alter some of our assumptions to take into account that we no longer have a random sample of individuals Instead. we have one realization of a stochastic(i.e. random) process Economics 20- Prof anderson

Why study econometrics? Rare in economics(and many other areas without labs! ) to have experimental data Need to use nonexperimental. or observational data to make inferences eImportant to be able to apply economic theory to real world data Economics 20- Prof. Anderson

Research methods Qualitative Vs quantitative Understanding the relationship between objectives(research question) and variables is critical Information≠Data Information=data analysis Planning in advance is a must To include how data will be analyzed

一、集中趋势(Central Tendency) 1, What is the most typical value? The Average: typical value for quantitative data The Weighted Average: Adjusting for importance The Median: typical value for quantitative and ordinal data The Mode: typical value even for nominal data 2, What percentile is it? Extremes, Quartiles, and Box Plots The Cumulative distribution function displays the percentiles