## 计算机代写|数据库作业代写Database代考|DATABASE OPERATIONS

statistics-lab™ 为您的留学生涯保驾护航 在代写数据库Database方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库Database代写方面经验极为丰富，各种代写数据库Database相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|数据库作业代写Database代考|DATABASE OPERATIONS

Eight operations were originally defined for relational databases, and they form the core of modern database operations. The following list describes those original operations:

Selection-This selects some or all of the records in a table. For example, you might want to select only the Competitors records where Event is Pyramid Luge so that you can know who to expect for that event (and how many ambulances to have standing by).

Projection-This drops columns from a table or selection. For example, when you make your list of Pyramid Luge competitors, you may want to list only their names and not their addresses, blood types, events (which you know is Pyramid Luge anyway), or next of kin.
Union-This combines tables with similar columns and removes duplicates. For example, suppose you have another table named FormerCompetitors that contains data for people who participated in previous years’ competitions. Some of these people are competing this year and some are not. You could use the union operator to build a list of everyone in either table. (Note that the operation would remove duplicates, but for these tables you would still get the same person several times with different events.)
Intersection-This finds the records that are the same in two tables. The intersection of the FormerCompetitors and Competitors tables would list those few who competed in previous years and who survived to compete again this year (i.e., the slow learners).

Difference-This selects the records in one table that are not in a second table. For example, the difference between FormerCompetitors and Competitors would give you a list of those who competed in previous years but who are not competing this year (so you can email them and ask them what the problem is).

Cartesian Product-This creates a new table containing every record in a first table combined with every record in a second table. For example, if one table contains values $1,2,3$, and a second table contains values A, B, C, then their Cartesian product contains the values $1 / \mathrm{A}$, $1 / \mathrm{B}, 1 / \mathrm{C}, 2 / \mathrm{A}, 2 / \mathrm{B}, 2 / \mathrm{C}, 3 / \mathrm{A}, 3 / \mathrm{B}$, and $3 / \mathrm{C}$.

Join-This is similar to a Cartesian product except records in one table are paired only with those in the second table if they meet some condition. For example, you might join the Competitors records with the NextOfKin records where a Competitors record’s NextOfKin value matches the NextOfKin record’s Name value. In this example, that gives you a list of the competitors together with their corresponding next of kin data.

Divide-This operation is the opposite of the Cartesian product. It uses one table to partition the records in another table. It finds all of the field values in one table that are associated with every value in another table. For example, if the first table contains the values $1 / \mathrm{A}, 1 / \mathrm{B}$, $1 / \mathrm{C}, 2 / \mathrm{A}, 2 / \mathrm{B}, 2 / \mathrm{C}, 3 / \mathrm{A}, 3 / \mathrm{B}$, and $3 / \mathrm{C}$ and a second table contains the values $1,2,3$, then the first divided by the second gives A, B, C. (Don’t worry, I think it’s pretty weird and confusing, too, so it probably won’t be on the final exam.)

## 计算机代写|数据库作业代写Database代考|POPULAR RDBs

There are many relational database products available for you to use. All provide the same basic features, such as the ability to build tables, perform CRUD operations, carry out the eight basic relational database operations (selection, projection, union, etc.), define indexes and keys, and so forth.
They all also provide some form of SQL. SQL is a standardized language, so many queries are the same in most RDBMSs, although there are some slight differences. For example, different systems call a 4-byte integer an INT, INTEGER, NUMBER(4), or INT4. Many of these differences affect statements that modify the database (such as adding or deleting tables) rather than queries.

You can find a good SQL tutorial at www. w3schools. com/sql.
For a catalog of SQL differences on different RDBMSs, see https : / en . wikibooks . org/wiki/ SQL_Dialects_Reference.

You can find a list of around 100 RDBMSs at https://en.wikipedia.org/wiki/List_of_ relational_database_management_systems and a similar list at https://database .guide/list-of-relational-database-management-systems-rdbms.

The following list shows the most popular RDBMSs in use as of September 2022 according to the DB-Engines page, https://db-engines.com/en/ranking/relational+dbms :

1. Oracle
2. $\mathrm{MySQL}$
3. Microsoft SQL Server
4. PostgreSQL
5. IBM Db2
6. Microsoft Access
7. SQLite
9. Snowflake
10. Microsoft Azure SQL Database
I started writing a summary of each of the products, but found that they were so similar that it wasn’t worth the effort. The following paragraphs describe some of their common features and give a few details for specific products.

# 数据库代考

## 计算机代写|数据库作业代写Database代考|DATABASE OPERATIONS

join—这类似于笛卡尔积，除了一个表中的记录只有在满足某些条件时才与另一个表中的记录配对。例如，你可以将竞争者记录与NextOfKin记录连接起来，其中竞争者记录的NextOfKin值与NextOfKin记录的Name值匹配。在本例中，它为您提供了竞争对手列表及其相应的近亲数据。

## 计算机代写|数据库作业代写Database代考|POPULAR RDBs

$\ mathrm {MySQL}$

Microsoft SQL Server

PostgreSQL

IBM Db2

Microsoft Access

SQLite

Microsoft Azure SQL数据库

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 计算机代写|数据库作业代写Database代考|KEYS

statistics-lab™ 为您的留学生涯保驾护航 在代写数据库Database方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库Database代写方面经验极为丰富，各种代写数据库Database相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|数据库作业代写Database代考|KEYS

Relational database terminology includes an abundance of different flavors of keys. (They are key terms, so you could say that relational databases have a lot of key key terms.) In the loosest sense, a key is a combination of one or more columns that you use to find rows in a table. For example, a Customers table might use CustomerID to find customers. If you know a customer’s ID, then you can quickly find that customer’s record in the table. (In fact, many ID numbers, such as employee IDs, student IDs, driver’s license numbers, and so forth, were invented just to make searching in database tables easier. My library card certainly doesn’t include a 10-digit ID number for $m y$ convenience.)
The more formal relational vocabulary includes several other more precise definitions of keys.

In general, a key is a set of one or more columns in the table that have certain properties. A compound key or composite key is a key that includes more than one column. For example, you might use the combination of FirstName and LastName to look up customers.

A superkey is a set of one or more columns in a table for which no two rows can have the exact same values. For example, in the Competitors table shown in Figure 2.1, the Name, Address, and Event columns together form a superkey because no two rows have exactly the same Name, Address, and Event values. Because superkeys define fields that must be unique within a table, they are sometimes called unique keys.
Because no two rows in the table have the same values for a superkey, a superkey can uniquely identify a particular row in the table. In other words, a program could use a superkey to find any particular record.

A candidate key is a minimal superkey. That means if you remove any of the columns from the superkey, it won’t be a superkey anymore.
For example, you already know that Name/Address/Event is a superkey for the Competitors table. If you remove Event from the superkey, you’re left with Name/Address. This is not a superkey because everyone in the table is participating in multiple events, and therefore they have more than one record in the table with the same name and address.

If you remove Name, then Address/Event is not a superkey because Dean Daring and his roommate George Foreman share the same address and are both signed up for Pyramid Luge. (They also have the same blood type. They became friends and decided to become roommates when Dean donated blood for George after a particularly flamboyant skateboarding accident.)

Finally if you remove Address, then Name/Event is still a superkey. That means Name/Address/Event is not a candidate key because it is not minimal. However, Name/Event is a candidate key because no two rows have the same Name/Event values and you can easily see neither Name nor Event is a superkey, so the pair is minimal.

## 计算机代写|数据库作业代写Database代考|INDEXES

An index is a database structure that makes it quicker and easier to find records based on the values in one or more fields. Indexes are not the same as keys, although the two are related closely enough that many developers confuse the two and use the terms interchangeably.

For example, suppose you have a Customers table that holds customer information: name, address, phone number, Swiss bank account number, and so forth. The table also contains a CustomerId field that it uses as its primary key.

Unfortunately, customers usually don’t remember their customer IDs (I know I don’t), so you need to be able to look them up by name or phone number. If you make Name and PhoneNumber two different keys, then you can quickly locate a customer’s record in three ways: by customer ID, by name, and by phone number.

NOTE Relational databases also make it easy to look up records based on non-indexed fields, although it may take a while. If the customer only remembers their address and not their customer ID or name, you can search for the address even if it that field isn’t part of an index. It may just take a long time. Of course, if the customer cannot remember their name, then they have bigger problems.
Building and maintaining an index takes the database some extra time, so you shouldn’t make indexes gratuitously. Place indexes on the fields that you are most likely to need to search and don’t bother indexing fields like apartment number or telephone extension, which you’re unlikely to need to search.

# 数据库代考

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 计算机代写|数据库作业代写Database代考|Legal and Security Considerations

statistics-lab™ 为您的留学生涯保驾护航 在代写数据库Database方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库Database代写方面经验极为丰富，各种代写数据库Database相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|数据库作业代写Database代考|Cloud Considerations

A cloud database hosts data in the cloud so it is accessible over a network. There are two common cloud deployment models. First, you can rent space on a virtual machine and run the database there. A virtual machine (VM) is a simulation of a physical computer running on a server somewhere.

Virtual machines have the advantage that the cloud provider can move them around, possibly hosting multiple VMs on a single physical machine. Conversely, you may also be able to use multiple computers to host a single VM. Those two capabilities together make it easier to scale an application up or down as needed.

For example, suppose you write some software to schedule appointments for nail salons. Initially you serve only a few salons, so your VM uses a small fraction of one physical server. Over time, as more and more salons sign up for your service, you need more space and faster processing, so you start using more of the server. Soon your provider moves you onto faster hardware and eventually onto a small group of servers (for a price, of course).

NOT SO VIRTUAL MACHINES
Instead of renting a virtual machine, you can rent a physical machine or even buy your own and put it on your network. Then you have full use of the machine.
That approach works and has some advantages, such as giving you complete control (and commensurate responsibility) and letting you know exactly where your data is, but it isn’t really cloud computing and it doesn’t give you the same easy scaling advantages.

## 计算机代写|数据库作业代写Database代考|Legal and Security Considerations

I won’t talk too much about legal issues in this book, but you should determine whether you might encounter any of them. For example, I already mentioned data residency and data sovereignty earlier in this chapter. Some countries require that certain kinds of data reside physically within their borders, and you could be in big trouble if your data is stored in the cloud on foreign servers.
In addition to ensuring that your cloud servers have allowed physical locations, you need to ensure that your data is properly protected. For example, in the United States, HIPAA (which stands for the Health Insurance Portability and Accountability Act and is pronounced “hip-uh”) prohibits the disclosure of a patient’s sensitive medical information without their consent or knowledge. I don’t believe HIPAA requires data residency (but I’m not a lawyer, so don’t take my word for it), but some states have their own special requirements. For example, all 50 U.S. states plus Washington D.C., Puerto Rico, and the U.S. Virgin Islands have some sort of law requiring you to notify residents if their personal information is compromised in a security breach.

Obviously sensitive information like credit card numbers, bank account numbers, Social Security numbers, driver’s license numbers, website passwords, biometric data, business information, and other important items require top-notch security.

Certain other kinds of data are also considered personal and/or sensitive and may or may not be protected by law. Personally identifiable information $(P I I)$ is information that could be used to assist with identity theft and includes such items as a person’s name, mother’s maiden name, address and former addresses, phone numbers, and so on. Sensitive data may include gender identity, ethnic background, political or religious affiliation, union membership, and more.

# 数据库代考

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 计算机代写|数据库作业代写Database代考|EECS484

statistics-lab™ 为您的留学生涯保驾护航 在代写数据库Database方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库Database代写方面经验极为丰富，各种代写数据库Database相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|数据库作业代写Database代考|MOVING FROM 3 × 5 CARDS TO COMPUTERS

Let us return to our example of a merchant who maintained a customer file on $3 \times 5$ cards. As time passed, the customer base grew and the merchant desired to keep more information about customers. From a dataprocessing standpoint, we would say the enhancement techniques for storage and retrieval led to better organized cards, more fields, and perhaps better ways to store and find individual records.

Some questions arise: Were customer records kept in name-alphabetical order? Were the records stored by telephone number or record number (which might also be a customer number)? What happens if a field not on existing forms or cards were required? If data is added or changed, how much will the record formats change? Such were data-processing dilemmas of the past.

When computers began to be used for businesses, data was stored on magnetic media. The magnetic media were mostly disks and tapes. The way data was stored and retrieved on a computer started out like the 3 $\times 5$ cards, but the magnetic data was virtual. It did not physically exist where you could touch it or see it without some kind of software to load and find records. Further, a display device to see what the “3 $\times 5$ card” had on it was required. Prior to about 1975, the most common way data was fed into a computer was via punched cards. Punched card systems for handling data were in use as early as the $1930 \mathrm{~s}$; sorters were capable of scanning and arranging a pile of cards. Using punched cards to input data into computers was common in the 1960 s because it was known technology. The output or “display device” was typically a line printer.
As data was placed on a computer, software was developed to handle the data and filing techniques evolved. In the very early days of databes, the files kept on computers basically replicated the $3 \times 5$ cards. There were many problems with computers and databases in the “early days.” (Generally, early days in terms of computers and databases means roughly early-to-mid 1960s.) Some problems involved input (how the data got into the computer), output (how the data was to be displayed), and file maintenance (how the data was to be stored and kept up to date, how records were to be added and deleted, and how fields were to be added, deleted, or changed). A person using a computer for keeping track of data could buy a computer and hire programmers, computer operators, and data entry personnel.

## 计算机代写|数据库作业代写Database代考|DATABASE MODELS

We now take a look back at database models as they were before the relational database was practical. The look back shows why the “old systems” are considered obsolete and why the relational model is the de facto standard in databases today. The old systems were classified as two main database models: hierarchical and network. These two models were the backbone of database software before the 1980s. Although these legacy systems might be considered “old fashioned,” there are some systems still in use today dependent on these models.

In this section, we present some versions of the hierarchical model for several reasons:
(a) To illustrate how older models were constructed from file systems
(b) To show why these file-based databases became outdated when relational databases became practical
(c) To see the evolution of file-based systems
The file systems discussed below are actual ways some database systems were written prior to the availability of relational database. The point here is to illustrate the good and bad points of older database systems and to show why relational database was and is such an improvement in database design and use.

In hierarchical database models, all data are arranged in a top-down fashion in which some records have one or more “dependent” or “child” records, and each child record is tied to one and only one “parent.” The parent-child relationship is not meant to infer a human familial relationship. The terms parent and child are historical and are meant to conjure up a picture of one type of data as dependent on another. Another terminology for the parent-child relationship is owner and objects owned, but parent-child terminology is more common. As is illustrated here, the “child” records will be sports played by a “parent” person.

We begin with an example of a hierarchical file situation. Suppose you have a database of people who play a sport at some location. Suppose we have a person, Brenda, who plays tennis at city courts and who plays golf at the municipal links. The person, Brenda, would be at the top of the hierarchy, and the sport location would be in the second tier. Usually, the connection between the layers in the hierarchy is a parent-child relationship. Each parent-person may be related to many child sport locations, but each sport location (each child record) is tied back to the one person (one parent record) who plays that particular sport. A way to store this hierarchical databe could be to have two files, one file for person, one file for sport locations. For the two-file model to make sense (i.e., to have the files “related” and hence be a database), there would have to be pointers or references of some kind from one file to the other.

# 数据库代考

## 计算机代写|数据库作业代写Database代考|DATABASE MODELS

(a) 说明旧模型是如何从文件系统构建的
(b) 说明为什么当关系数据库变得实用时这些基于文件的数据库变得过时
(c)查看基于文件的系统的演变

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 计算机代写|数据库作业代写Database代考|CS6400

statistics-lab™ 为您的留学生涯保驾护航 在代写数据库Database方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库Database代写方面经验极为丰富，各种代写数据库Database相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|数据库作业代写Database代考|ENTITY-RELATIONSHIP DIAGRAMS

This text concentrates on steps 1 through 3 of the software life cycle for databases. A database is a collection of related data. The concept of related data means a database stores information about one enterprise: a business, an organization, a grouping of related people or processes. For example, a database might contain data about Acme Plumbing and involve customers and service calls. A different database might be about the members and activities of a church group in town. It would be inappropriate to have data about the church group and Acme Plumbing in the same database because the two organizations are not related. Again, a database is a collection of related data. To keep a database about each of the above entities is fine, but not in the same database.

Database systems are often modeled using an entity-relationship (ER) diagram as the blueprint from which the actual database is created; the finalized blueprint is the output of the design phase. The ER diagram is an analyst’s tool to diagram the data to be stored in a database system. Phase 1 , the requirements phase, can be quite frustrating as the analyst has to elicit needs and wants from the user. The user may or may not be “computer savvy” and may or may not know the capabilities of a software system. The analyst often has a difficult time deciphering a user’s needs and wants to create a specification that (a) makes sense to both parties (user and analyst) and (b) allows the analyst to design efficiently.

In the real world, the user and the analyst may each be committees of professionals, but users (or user groups) must convey their ideas to an analyst (or team of analysts). Users must express what they want and what they think they need; analysts must elicit these wants and needs, document them, and create a plan to realize the user’s requirements.

User descriptions may seem vague and unstructured. Typically, users are successful at a business. They know the business; they understand the business model. The computer person is typically ignorant of the business but understands the computer end of the problem. To the computeroriented person, the user’s description of the business is as new to the analyst as the computer jargon is to the user. We present a methodology designed to make the analyst’s language precise so the user is comfortable with the to-be-designed database but still provides the analyst with a tool to facilitate mapping directly into the database.

In brief, next we review the early steps in the SE life cycle as it applies to database design.

## 计算机代写|数据库作业代写Database代考|FILES, RECORDS, AND DATA ITEMS

Data must be stored in an orderly fashion in a file of some kind to be useful. Suppose there were no computers-think back to a time when all files were paper documents for a business to keep track of its customers and products. A doctor’s office kept track of patients. A sports team kept statistics on its players. In these cases, data was recorded on paper and likely kept in a filing cabinet. The files with data in them could be referred to as a “database.” A database is most simply a repository of data about some specific entity. A customer file might be as plain and minimal as a list of people who did business with a merchant. There are two aspects to filing: storage and retrieval. Some method of storing data to facilitate retrieval is most desirable.

In a file of customer records, the whole file might be called the customer file, whereas the individual customer’s information is kept in a customer record. Files consist of records. More than likely, more information than a list of just customer’s names would be recorded. At the very least, a customer’s name, address, and phone number could constitute a customer record. Each of these components of the record is called a data item or field. The customer file contains customer records consisting of fields of data.

Table $2.1$ presents an example of some data (you can imagine each line as a $3 \times 5$ card, with the three cards [three records] making up a file).
This file contains three records with one record for each customer. The records each consist of four fields: record number, name, address, and city. As more customers are added, their data will be recorded on a new $3 \times 5$ card (a new record) and placed in the customer file. Several interesting questions and observations arise for the merchant keeping this information:

1. The merchant may well want to add information, such as a telephone number, in the future. Would you add a phone number to all $3 \times 5$ cards, or would the adding be done “as necessary”? If it were done “as necessary,” then some customers would have telephone numbers, and some would not. If a customer had no phone number on the record, then the phone number for that customer would be “null.” (We use the term “null” to mean “unknown.”)
2. How will the file be organized? Imagine not three customers, but 300 or 3,000 . Would the $3 \times 5$ cards be put in alphabetical order? Perhaps, but what happens if you get another A. McDonald or S.

# 数据库代考

## 计算机代写|数据库作业代写Database代考|FILES, RECORDS, AND DATA ITEMS

1. 商家将来可能很想添加信息，例如电话号码。你能给所有人加个电话号码吗3×5卡片，还是“根据需要”添加？如果它是“必要时”完成的，那么一些客户会有电话号码，而另一些则没有。如果客户在记录中没有电话号码，则该客户的电话号码将为“空”。（我们使用术语“空”来表示“未知”。）
2. 文件将如何组织？想象一下，不是三个客户，而是 300 或 3,000 个客户。将3×5卡片按字母顺序排列？也许吧，但如果你得到另一个 A. McDonald 或 S. 会发生什么？

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 计算机代写|数据库作业代写Database代考|CMU15-445

statistics-lab™ 为您的留学生涯保驾护航 在代写数据库Database方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库Database代写方面经验极为丰富，各种代写数据库Database相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|数据库作业代写Database代考|BUILDING A DATABASE

How do we construct a database? Suppose you were asked to put together a database of items one keeps in a pantry. How would you go about doing this? You might grab a piece of paper and begin listing items you see. When you are done, you should have a database of items in the pantry. Simple enough-you have a collection of related data. But take this a step further-Is this a good database? Was your approach to database construction a good methodology? The answer to these questions depends in part on why and how you constructed the list and who will use the list and for what. Also, will whoever uses the database be able to find a fact easily? If you are more methodical, you might first ask yourself how best to construct this database before you grab the paper and begin a list of items. A bit of pre-thinking will save time in the long run because you plan how the list is to be used and by whom.

When dealing with software and computer-related activity like databases, there exists a science of “how to” called software engineering (SE). SE is a process of specifying systems and writing software. To design a good database, we will use some ideas from SE.

In this chapter, we present a brief description of $S E$ as it pertains to planning our database. After this background/overview of SE, we explore database models and in particular the relational database model. While there are historically many kinds of database models, most of the databes in use today use a model known as “relational database.” Our focus in this book is to put forward a methodology based on SE to design a sound relational database.

## 计算机代写|数据库作业代写Database代考|WHAT IS THE SOFTWARE ENGINEERING PROCESS

The term software engineering refers to a process of specifying, designing, writing, delivering, maintaining, and finally retiring software. Software engineers often refer to the “life cycle” of software; software has a beginning and an ending. There are many excellent references on the topic of SE. Some are referenced at the end of this chapter.

Some authors use the term software engineering synonymously with “systems analysis and design,” but the underlying point is that any information system requires some process to develop it correctly. SE spans a wide range of information system tasks. The task we are primarily interested in here is specifying and designing a database. “Specifying a database” means documenting what the datahase is supposed to contain and how to go about the overall design task itself.

A basic idea in SE is to build software correctly; a series of steps or phases is required to progress through a “life cycle.” These steps ensure that a process of thinking precedes action-thinking through “what is needed” precedes “what software is written.” Further, the “thinking before action” necessitates that all parties involved in software development understand and communicate with one another. A common version of presenting the “thinking before acting” scenario may be called a “waterfall” model; the software development process is supposed to flow in a directional way without retracing. Like a waterfall, once a decision point is passed, it is at best difficult to back up and revisit it.

# 数据库代考

## 计算机代写|数据库作业代写Database代考|WHAT IS THE SOFTWARE ENGINEERING PROCESS

SE 的一个基本思想是正确地构建软件；需要一系列步骤或阶段才能通过“生命周期”。这些步骤确保了思考先于行动的过程——通过“需要什么”先于“编写什么软件”来思考。此外，“三思而后行”要求参与软件开发的各方相互理解和沟通。呈现“三思而后行”场景的常见版本可称为“瀑布”模型；软件开发过程应该在没有回溯的情况下以定向方式流动。就像瀑布一样，一旦通过了一个决策点，最多就很难回过头来重新审视它。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 计算机代写|数据库作业代写SQL代考|Time Series Analysis

statistics-lab™ 为您的留学生涯保驾护航 在代写数据库SQL方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库SQL代写方面经验极为丰富，各种代写数据库SQL相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|数据库作业代写SQL代考|Date, Datetime, and Time Manipulations

Dates and times come in a wide variety of formats, depending on the data source. We often need or want to transform the raw data format for our output, or to perform calculations to arrive at new dates or parts of dates. For example, the data set might contain transaction timestamps, but the goal of the analysis is to trend monthly sales. At other times, we might want to know how many days or months have elapsed since a particular event. Fortunately, SQL has powerful functions and formatting capabilities that can transform just about any raw input to almost any output we might need for analysis.

In this section, I’ll show you how to convert between time zones, and then I’ll go into depth on formatting dates and datetimes. Next, I’ll explore date math and time manipulations, including those that make use of intervals. An interval is a data type that holds a span of time, such as a number of months, days, or hours. Although data can he stored in a datahase table as an interval type, in practice I rapely see this done, sn I will talk ahout intervals alnngside the date and time finctions that you can use them with. Last, I’ll discuss some special considerations when joining or otherwise combining data from different sources.

## 计算机代写|数据库作业代写SQL代考|Time Zone Conversions

Understanding the standard time zone used in a data set can prevent misunderstandings and mistakes further into the analysis process. Time zones split the world into north-south regions that observe the same time. Time zones allow different parts of the world to have similar clock times for daytime and nighttime-so, for example, the sun is overhead at 12 p.m. wherever you are in the world. The zones follow irregular boundaries that are as much political as geographic ones. Most are one hour apart, but some are offset only 30 or 45 minutes, and so there are more than 30 time zones spanning the globe. Many countries that are distant from the equator observe daylight savings time for parts of the year as well, but there are exceptions, such as in the

United States and Australia, where some states observe daylight savings time and others do not. Each time zone has a standard abbreviation, such as PST for Pacific Standard Time and PDT for Pacific Daylight Time.

Many databases are set to Coordinated Universal Time (UTC), the global standard used to regulate clocks, and record events in this time zone. It replaced Greenwich Mean Time (GMT), which you might still see if your data comes from an older database. UTC does not have daylight savings time, so it stays consistent all year long. This turns out to be quite useful for analysis. I remember one time a panicked product manager asked me to figure out why sales on a particular Sunday dropped so much compared to the prior Sunday. I spent hours writing queries and investigating possible causes before eventually figuring out that our data was recorded in Pacific Time (PT). Daylight savings started early Sunday morning, the database clock moved ahead 1 hour, and the day had only 23 hours instead of 24 , and thus sales appeared to drop. Half a year later we had a corresponding 25 -hour day, when sales appeared unusually high.

## 计算机代写|数据库作业代写SQL代考|Date and Timestamp Format Conversions

Dates and timestamps are key to time series analysis. Due to the wide variety of ways in which dates and times can be represented in source data, it is almost inevitable that you will need to convert date formats at some point. In this section, I’ll cover several of the most common conversions and how to accomplish them with SQL: changing the data type, extracting parts of a date or timestamp, and creating a date or timestamp from parts. I’ll begin by introducing some handy functions that return the current date and/or time.

Returning the current date or time is a common analysis task-for cxample, to include a timestamp for the result sel or to use in dale math, covered in the nexi section. The current date and time are referred to as system time, and while returning them is easy to do with SQL, there are some syntax differences between databases.

To return the current date, some databases have a current_date function, with no parentheses:
SELECT current_date;
There is a wider variety of functions to return the current date and time. Check your database’s documentation or just experiment by typing into a SQL window to see whether a function returns a value or an error. The functions with parentheses do not take arguments, but it is important to include the parentheses:
current_timestamp
localtimestamp
get_date()
now()
Finally, there are functions to return only the timestamp portion of the current system time. Again, consult documentation or experiment to figure out which function(s) to use with your database:
current_time
localtime
timeofday()
SQL has a number of functions for changing the format of dates and times. To reduce the granularity of a timestamp, use the date_trunc function. The first argument is a text value indicating the time period level to which to truncate the timestamp in the second argument. The result is a timestamp value:
date_trunc (text, timestamp)
SELECT date_trunc(‘month’ , ‘2020-10-04 12:33:35’ : : timestamp);
date_trunc (text, timestamp)
SELECT date_trunc(‘month’ ,’2020-10-04 12:33:35′: : timestamp);
date_trunc
$\cdots 2020-10-0100: 00: 00$
date_trunc
2020-10-01 00:00:00

## 计算机代写|数据库作业代写SQL代考|Date and Timestamp Format Conversions

SELECT current_date;

current_timestamp
localtimestamp
get_date()
now()

current_time
localtime
timeofday()
SQL 有许多用于更改日期和时间格式的函数。要减少时间戳的粒度，请使用 date_trunc 函数。第一个参数是一个文本值，指示要将第二个参数中的时间戳截断到的时间段级别。结果是一个时间戳值：
date_trunc (text, timestamp)
SELECT date_trunc(‘month’ , ‘2020-10-04 12:33:35’ : : timestamp);
date_trunc(文本，时间戳)
SELECT date_trunc(‘month’ ,’2020-10-04 12:33:35′: : timestamp);

⋯2020−10−0100:00:00
date_trunc
2020-10-01 00:00:00

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 计算机代写|数据库作业代写SQL代考|Detecting Duplicates

statistics-lab™ 为您的留学生涯保驾护航 在代写数据库SQL方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库SQL代写方面经验极为丰富，各种代写数据库SQL相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|数据库作业代写SQL代考|Detecting Duplicates

A duplicate is when you have two (or more) rows with the same information. Duplicates can exist for any number of reasons. A mistake might have been made during data entry, if there is some manual step. A tracking call might have fired twice. A processing step might have run multiple times. You might have created it accidentally with a hidden many-to-many JOIN. However they come to be, duplicates can really throw a wrench in your analysis. I can recall times early in my career when I thought I had a great finding, only to have a product manager point out that my sales figure was twice the actual sales. It’s embarrassing, it erodes trust, and it requires rework and sometimes painstaking reviews of the code to find the problem. I’ve learned to check for duplicates as I go.

Fortunately, it’s relatively easy to find duplicates in our data. One way is to inspect a sample, with all columns ordered:
SELECT column_a, column_b, column_c…
FROM table
SELECT column_a, column_b, column_c.
FROM table
ORDER BY $1,2,3 \ldots$
;
ORDER BY $1,2,3 \ldots$
;

This will reveal whether the data is full of duplicates, for example, when looking at a brand-new data set, when you suspect that a process is generating duplicates, or after a possible Cartesian JOIN. If there are only a few duplicates, they might not show up in the sample. And scrolling through data to try to spot duplicates is taxing on your eyes and brain. A more systematic way to find duplicates is to SELECT the columns and then count the rows (this might look familiar from the discussion of histograms!):
SELECT count() FROM ( SELECT column_a, column_b, column_c… , count() as records
FROM….
GROUP BY $1,2,3 \ldots$
) a
SELECT count() FROM ( SELECT column_a, column_b, column_c… , count $^{}$ ) as records FROM… GROUP BY $1,2,3 \ldots$ ) a WHERE records > 1 ; WHERE records > 1 ; This will tell you whether there are any cases of duplicates. If the query returns 0 , you’re good to go. For more detail, you can list out the number of records $(2,3,4$, etc.): SELECT records, count $()$
FROM
(
SELECT column_a, column_b, column_c…, count(*) as records
FROM….
GROUP BY $1,2,3 \ldots$
) a
WHERE records > 1
GROUP BY 1
;

## 计算机代写|数据库作业代写SQL代考|Deduplication with GROUP BY and DISTINCT

Duplicates happen, and they’re not always a result of bad data. For example, imagine we want to find a list of all the customers who have successfully completed a transaction so we can send them a coupon for their next order. We might JOIN the custom ers table to the transactions table, which would restrict the records returned to only those customers that appear in the transactions table:
SELECT a.customer_id, a.customer_name, a.customer_email
FROM customers a
JOIN transactions b on a.customer_id = b.customer_id
;
This will return a row for each customer for each transaction, however, and there are hopefully at least a few customers who have transacted more than once. We have accidentally created duplicates, not because there is any underlying data quality problem but because we haven’t taken care to avoid duplication in the results. Fortunately, there are several ways to avoid this with SQL. One way to remove duplicates is to use the keyword DISTINCT:
SELECT distinct a.customer_id, a.customer_name, a.customer_email
FROM customers a
JoIN transactions b on a.customer_id = b.customer_id
SELECT distinct a.customer_id, a.customer_name, a.customer_email
FROM customers a
JOIN transactions b on a.customer_id = b.customer_id
;
;
Another option is to use a GROUP BY, which, although typically seen in connection with an aggregation, will also deduplicate in the same way as DISTINCT. I remember the first time I saw a colleague use GROUP BY without an aggregation dedupe-I

didn’t even realize it was possible. I find it somewhat less intuitive than DISTINCT, but the result is the samc:
SELECT a.customer_id, a.customer_name, a.customer_email
FROM customers a
JOIN transactions b on a.customer_id = b.customer_id
GROUP BY $1,2,3$
;
Another useful technique is to perform an aggregation that returns one row per entity. Although technically not deduping, it has a similar effect. For example, if we have a number of transactions by the same customer and need to return one record per customer, we could find the min (first) and/or the max (most recent) transac tion_date:
SELECT customer_id
,min(transaction_date) as first_transaction_date
, max(transaction_date) as last_transaction_date
, count $()$ as total_orders FROM table GROUP BY customer_id SELECT customer_id ,min(transaction_date) as first_transaction_date ,max(transaction_date) as last_transaction_date , count $\left(^{}\right.$ ) as total_orders
FROM table
GROUP BY customer_id
;
uplicate data, or data that contains multiple records per entity even if they techni-
;
Duplicate data, or data that contains multiple records per entity even if they technically are not duplicates, is one of the most common reasons for incorrect query results. You can suspect duplicates as the cause if all of a sudden the number of customers or total sales returned by a query is many times greater than what you were expecting. Fortunately, there are several techniques that can be applied to prevent this from occurring.
Another common problem is missing data, which we’ll turn to next.

## 计算机代写|数据库作业代写SQL代考|Cleaning Data with CASE Transformations

CASE statements can be used to perform a variety of cleaning, enrichment, and summarization tasks. Sometimes the data exists and is accurate, but it would be more useful for analysis if values were standardized or grouped into categories. The structure of CASE statements was presented earlier in this chapter, in the section on binning.
Nonstandard values occur for a variety of reasons. Values might come from different systems with slightly different lists of choices, system code might have changed,

options might have been presented to the customer in different languages, or the customer might have been able to fill out the value rather than pick from a list.

Imagine a field containing information about the gender of a person. Values indicating a female person exist as “F” “female”, and “femme.” We can standardize the values like this:
CASE when gender $=$ ‘ $F$ ‘ then ‘Female’
when gender = ‘female’ then ‘Female’
when qender = ‘femme’ then ‘Female’
else gender
end as gender_cleaned
CASE statements can also be used to add categorization or enrichment that does not exist in the original data. As an example, many organizations use a Net Promoter Score, or NPS, to monitor customer sentiment. NPS surveys ask respondents to rate, on a scale of 0 to 10 , how likely they are to recommend a company or product to a friend or colleague. Scores of 0 to 6 are considered detractors, 7 and 8 are passive, and 9 and 10 are promoters. The final score is calculated by subtracting the percentage of detractors from the percentage of promoters. Survey result data sets usually include optional free text comments and are sometimes enriched with information the organization knows about the person surveyed. Given a data set of NPS survey responses, the first step is to group the responses into the categories of detractor, passive, and promoter:
SELECT response_id
, likelihood
, case when llkelthood $<=6$ then ‘Detractor’
when likelihood $<=8$ then ‘Passive’
else ‘Promoter’
SELECT response_id
, Likelihood
,case when Llkelthood $<=6$ then ‘Detractor’
when likelihood $<=8$ then ‘Passive’
else ‘Promoter’
end as response_type
FRoM nps_responses
;
end as response_type
FROM nps_responses
;

## 计算机代写|数据库作业代写SQL代考|Detecting Duplicates

SELECT column_a、column_b、column_c…
FROM table
SELECT column_a、column_b、column_c。
FROM 表
ORDER BY1,2,3…
;

;

SELECT count() FROM ( SELECT column_a, column_b, column_c… , count() as records
FROM ….

) 一个
SELECT count() FROM ( SELECT column_a, column_b, column_c… , count) 作为记录来自… GROUP BY1,2,3…) a WHERE 记录 > 1 ；WHERE 记录 > 1 ; 这将告诉您是否存在重复的情况。如果查询返回 0 ，您就可以开始了。有关更多详细信息，您可以列出记录数(2,3,4等）：SELECT 记录、计数()
FROM
(
SELECT column_a, column_b, column_c…, count(*) 作为记录
FROM….
GROUP BY1,2,3…
) a
WHERE 记录 > 1
GROUP BY 1

## 计算机代写|数据库作业代写SQL代考|Deduplication with GROUP BY and DISTINCT

SELECT a.customer_id, a.customer_name, a.customer_email
FROM customers a
JOIN transactions b on a。 customer_id = b.customer_id
;

SELECT distinct a.customer_id, a.customer_name, a.customer_email
FROM customers
a 在 a.customer_id = b.customer_id 上加入交易 b
SELECT distinct a.customer_id, a.customer_name, a .customer_email
FROM customers a
JOIN transactions b on a.customer_id = b.customer_id
;
;

SELECT a.customer_id, a.customer_name, a.customer_email
FROM customers a
JOIN transactions b on a.customer_id = b.customer_id
GROUP BY1,2,3
;

SELECT customer_id
,min(transaction_date)作为 first_transaction_date
， max(transaction_date) 作为 last_transaction_date
， count()as total_orders FROM table GROUP BY customer_id SELECT customer_id ,min(transaction_date) as first_transaction_date ,max(transaction_date) as last_transaction_date , count() 作为 total_orders
FROM table
GROUP BY customer_id

## 计算机代写|数据库作业代写SQL代考|Cleaning Data with CASE Transformations

CASE 语句可用于执行各种清理、扩充和汇总任务。有时数据存在并且是准确的，但如果将值标准化或分组到类别中，它将对分析更有用。CASE 语句的结构在本章前面的分箱一节中介绍过。

CASE when gender= ‘ F’ 然后 ‘Female’

CASE 语句还可用于添加原始数据中不存在的分类或丰富。例如，许多组织使用净推荐值或 NPS 来监控客户情绪。NPS 调查要求受访者以 0 到 10 的等级对他们向朋友或同事推荐公司或产品的可能性进行评分。0 到 6 分被认为是批评者，7 和 8 分是被动的，9 和 10 是推动者。最终得分是通过从推荐者的百分比中减去批评者的百分比来计算的。调查结果数据集通常包括可选的自由文本评论，有时还包含组织了解的有关被调查人的信息。给定一组 NPS 调查响应的数据集，第一步是将响应分为批评者、被动者和促进者类别：
SELECT response_id
, 可能性
, case when llkelthood<=6然后是“贬低者”的

SELECT response_id
，可能性
，Llkelthood 时的情况<=6然后是“贬低者”的

FRoM nps_responses 结尾

FROM nps_responses 结尾

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 计算机代写|数据库作业代写SQL代考|Binning

statistics-lab™ 为您的留学生涯保驾护航 在代写数据库SQL方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库SQL代写方面经验极为丰富，各种代写数据库SQL相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|数据库作业代写SQL代考|Binning

Binning is useful when working with continuous values. Rather than the number of observations or records for each value being counted, ranges of values are grouped together, and these groups are called bins or buckets. The number of records that fall into each interval is then counted. Bins can be variable in size or have a fixed size, depending on whether your goal is to group the data into bins that have particular meaning for the organization, are roughly equal width, or contain roughly equal numbers of records. Bins can be created with CASE statements, rounding, and logarithms.

A CASE statement allows for conditional logic to be evaluated. These statements are very flexible, and we will come back to them throughout the book, applying them to data profiling, cleaning, text analysis, and more. The basic structure of a CASE statement is:
case when condition1 then return_value_1
when condition2 then return_value_2
else return_value_default
end
The WHEN condition can be an equality, inequality, or other logical condition. The THEN return value can be a constant, an expression, or a field in the table. Any number of conditions can be included, but the statement will stop executing and return the result the first time a condition evaluates to TRUE. ELSE tells the database what to use as a default value if no matches are found and can also be a constant or field. ELSE is optional, and if it is not included, any nonmatches will return null. CASE statements can also be nested so that the return value is another CASE statement.

## 计算机代写|数据库作业代写SQL代考|n-Tiles

You’re probably familiar with the median, or middle value, of a data set. This is the 50th percentile value. Half of the values are larger than the median, and the other half are smaller. With quartiles, we fill in the 25 th and 75 th percentile values. A quarter of the values are smaller and three quarters are larger for the 25 th percentile; three quarters are smaller and one quarter are larger at the 75 th percentile. Deciles break the data set into 10 equal parts. Making this concept generic, $n$-tiles allow us to calculate any percentile of the data set: 27 th percentile, $50.5$ th percentile, and so on.

Many databases have a median function built in but rely on more generic n-tile functions for the rest. These functions are window functions, computing across a range of rows to return a value for a single row. They take an argument that specifies the number of bins to split the data into and, optionally, a PARTITION BY and/or an ORDER BY clause:
ntile(num_bins) over (partition by… order by…)
As an example, imagine we had 12 transactions with order_amounts of $\$ 19.99, \$9.99$, $\$ 59.99, \$11.99, \$ 23.49, \$55.98, \$ 12.99, \$99.99, \$ 14.99, \$34.99, \$ 4.99$, and$\$89.99$. Performing an ntile calculation with 10 bins sorts each order_amount and assigns a bin from 1 to 10 :

This can be used to bin records in practice by first calculating the ntile of each row in a subquery and then wrapping it in an outer query that uses min and max to find the upper and lower boundaries of the value range:
SELECT ntile
,min(order_amount) as lower_bound
, max(order_amount) as upper_bound
, count(order_id) as orders
FROM
SELECT customer_id, order_id, order_amount
SELECT ntile
, min(order_amount) as lower_bound
, max(order_amount) as upper_bound
, count(order_id) as orders
FROM
( SELECT customer_id, order_id, order_amount
,ntile(10) over_(order by order_amount) as ntile
FROM orders a
GROUP BY 1
;
, ntile(10) over (order by order_amount) as ntile
FROM orders
) $a$
GROUP BY 1
;
A related function is percent_rank. Instead of returning the bins that the data falls into, percent_rank returns the percentile. It takes no argument but requires parentheses and optionally takes a PARTITIONBY and/or an ORDER BY clause:
percent_rank() over (partition by… order by…)

## 计算机代写|数据库作业代写SQL代考|Profiling: Data Quality

Data quality is absolutely critical when it comes to creating good analysis. Although this may seem obvious, it has been one of the hardest lessons I’ve learned in my years of working with data. It’s easy to get overly focused on the mechanics of processing

the data, finding clever query techniques and just the right visualization, only to have stakeholders ignore all of that and point out the one data inconsistency. Ensuring data quality can be one of the hardest and most frustrating parts of analysis. The saying “garbage in, garbage out” captures only part of the problem. Good ingredients in plus incorrect assumptions can also lead to garbage out.

Comparing data against ground truth, or what is otherwise known to be true, is ideal though not always possible. For example, if you are working with a replica of a production database, you could compare the row counts in each system to verify that all rows arrived in the replica database. In other cases, you might know the dollar value and count of sales in a particular month and thus can query for this information in the database to make sure the sum of sales and count of records match. Often the difference between your query results and the expected value comes down to whether you applied the correct filters, such as excluding cancelled orders or test accounts; how you handled nulls and spelling anomalies; and whether you set up correct JOIN conditions between tables.

Profiling is a way to uncover data quality issues early on, before they negatively impact results and conclusions drawn from the data. Profiling reveals nulls, categorical codings that need to be deciphered, fields with multiple values that need to be parsed, and unusual datetime formats. Profiling can also uncover gaps and step changes in the data that have resulted from tracking changes or outages. Data is rarely perfect, and it’s often only through its use in analysis that data quality issues are uncuvered.

## 计算机代写|数据库作业代写SQL代考|Binning

CASE 语句允许评估条件逻辑。这些语句非常灵活，我们将在本书中反复讨论它们，将它们应用于数据分析、清理、文本分析等。CASE 语句的基本结构是：
case when condition1 then return_value_1
when condition2 then return_value_2
else return_value_default
end
WHEN 条件可以是等式、不等式或其他逻辑条件。THEN 返回值可以是常量、表达式或表中的字段。可以包含任意数量的条件，但语句将停止执行并在条件第一次评估为 TRUE 时返回结果。如果没有找到匹配项，ELSE 告诉数据库使用什么作为默认值，也可以是常量或字段。ELSE 是可选的，如果不包括在内，任何不匹配项都将返回 null。CASE 语句也可以嵌套，以便返回值是另一个 CASE 语句。

## 计算机代写|数据库作业代写SQL代考|n-Tiles

ntile(num_bins) over (partition by… order by…)

SELECT ntile
,min( order_amount) as lower_bound
, max(order_amount) as upper_bound
, count(order_id) as orders
FROM
SELECT customer_id, order_id, order_amount
SELECT ntile
, min(order_amount) as lower_bound
, max(order_amount) as upper_bound
, count(order_id) as orders
FROM
( SELECT customer_id, order_id, order_amount
,ntile(10) over_(order by order_amount) as ntile
FROM orders a
GROUP BY 1
;
, ntile(10) over (order by order_amount) as ntile
FROM orders
)一个

percent_rank() over (partition by… order by…)

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 计算机代写|数据库作业代写SQL代考|SQL Query Structure

statistics-lab™ 为您的留学生涯保驾护航 在代写数据库SQL方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库SQL代写方面经验极为丰富，各种代写数据库SQL相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|数据库作业代写SQL代考|SQL Query Structure

SQL queries have common clauses and syntax, although these can be combined in a nearly infinite number of ways to achieve analysis goals. This book assumes you have some prior knowledge of SQL, but I’ll review the basics here so that we have a common foundation for the code examples to come.

The SELECT clause determines the columns that will be returned by the query. One column will be returned for each expression within the SELECT clause, and expressions are separated by commas. An expression can be a field from the table, an aggregation such as a sum, or any number of calculations, such as CASE statements, type conversions, and various functions that will be discussed later in this chapter and throughout the book.

The FROM clause determines the tables from which the expressions in the SELECT clause are derived. A “table” can be a database table, a view (a type of saved query that otherwise functions like a table), or a subquery. A subquery is itself a query, wrapped in parentheses, and the result is treated like any other table by the query that references it. A query can reference multiple tables in the FROM clause, though they must use one of the JOIN types along with a condition that specifies how the tables relate. The JOIN condition usually specifies an squality between ficlds in cach table, such as orders.customer_id = customers.customer_id. JOIN conditions can include multiple fields and can also specify inequalities or ranges of values, such as ranges of dates. We’ll see a variety of JOIN conditions that achieve specific analysis goals throughout the book. An INNER JOIN returns all records that match in both tables. A LEFT JOIN returns all records from the first table, but only those records from the second table that match. A RIGHT JOIN returns all records from the second table, but only those records from the first table that match. A FULL OUTER JOIN returns all records from both tables. A Cartesian JOIN can result when each record in the first table matches more than one record in the second table. Cartesian JOINs should generally be avoided, though there are some specific use cases, such as generating data to fill in a time series, in which we will use them intentionally. Finally, tables in the FROM clause can be aliased, or given a shorter name of one or more letters that can be referenced in other clauses in the query. Aliases save query writers from having to type out long table names repeatedly, and they make queries easier to read.

## 计算机代写|数据库作业代写SQL代考|Profiling: Distributions

Profiling is the first thing I do when I start working with any new data set. I look at how the data is arranged into schemas and tables. I look at the table names to get familiar with the topics covered, such as customers, orders, or visits. I check out the column names in a few tables and start to construct a mental model of how the tables relate to one another. For example, the tables might include an order_detail table with line-item breakouts that relate to the order table via an order_id, while the order table relates to the customer table via a customer_id. If there is a data dictionary, I review that and compare it to the data I see in a sample of rows.

The tables generally represent the operations of an organization, or some subset of the operations, so I think about what domain or domains are covered, such as ecommerce, marketing, or product interactions. Working with data is easier when we have knowledge of how the data was generated. Profiling can provide clues about this, or about what questions to ask of the source, or of people inside or outside the organization responsible for the collection or generation of the data. Even when you collect the data yourself, profiling is useful.

Another detail I check for is how history is represented, if at all. Data sets that are replicas of production databases may not contain previous values for customer addresses or order statuses, for example, whereas a well-constructed data warehouse may have daily snapshots of changing data fields.

Profiling data is related to the concept of exploratory data analysis, or EDA, named by John Tukey. In his book of that name, ${ }^{1}$ Tukey describes how to analyze data sets by computing various summaries and visualizing the results. He includes techniques for looking at distributions of data, including stem-and-leaf plots, box plots, and histograms.

After checking a few samples of data, I start looking at distributions. Distributions allow me to understand the range of values that exist in the data and how often they occur, whether there are nulls, and whether negative values exist alongside positive ones. Distributions can be created with continuous or categorical data and are also called frequencies. In this section, we’ll look at how to create histograms, how binning can help us understand the distribution of continuous values, and how to use n-tiles to get more precise about distributions.

## 计算机代写|数据库作业代写SQL代考|Histograms and Frequencies

One of the best ways to get to know a data set, and to know particular fields within the data set, is to check the frequency of values in each field. Frequency checks are also useful whenever you have a question about whether certain values are possible or if you spot an unexpected value and want to know how commonly it occurs. Frequency checks can be done on any data type, including strings, numerics, dates, and booleans. Frequency queries are a great way to detect sparse data as well.

The query is straightforward. The number of rows can be found with count(* ), and the profiled field is in the GROUP BY. For example, we can check the frequency of each type of fruit in a fictional fruit_inventory table:

A frequency plot is a way to visualize the number of times something occurs in the data set. The field being profiled is usually plotted on the $x$-axis, with the count of observations on the $y$-axis. Figure 2-1 shows an example of plotting the frequency of fruit from our query. Frequency graphs can also be drawn horizontally, which accommodates long value names well. Notice that this is categorical data without any inherent order.

## 计算机代写|数据库作业代写SQL代考|SQL Query Structure

SQL 查询具有通用的子句和语法，尽管它们可以以几乎无限的方式组合以实现分析目标。本书假设您有一些 SQL 的先验知识，但我将在这里回顾基础知识，以便我们为后面的代码示例有一个共同的基础。

SELECT 子句确定查询将返回的列。SELECT 子句中的每个表达式都将返回一列，表达式用逗号分隔。表达式可以是表中的字段、聚合（如求和）或任意数量的计算（如 CASE 语句、类型转换和将在本章后面和整本书中讨论的各种函数）。

FROM 子句确定派生 SELECT 子句中的表达式的表。“表”可以是数据库表、视图（一种保存的查询类型，其功能类似于表）或子查询。子查询本身就是一个查询，用括号括起来，结果被引用它的查询与任何其他表一样对待。一个查询可以在 FROM 子句中引用多个表，但它们必须使用一种 JOIN 类型以及一个指定表如何关联的条件。JOIN 条件通常指定 cach 表中 ficld 之间的 squality，例如 orders.customer_id = customers.customer_id。JOIN 条件可以包括多个字段，还可以指定不等式或值范围，例如日期范围。我们将在整本书中看到各种实现特定分析目标的 JOIN 条件。INNER JOIN 返回两个表中匹配的所有记录。LEFT JOIN 返回第一个表中的所有记录，但仅返回第二个表中匹配的那些记录。RIGHT JOIN 返回第二个表中的所有记录，但仅返回第一个表中匹配的那些记录。FULL OUTER JOIN 返回两个表中的所有记录。当第一个表中的每条记录与第二个表中的多个记录匹配时，可能会导致笛卡尔连接。通常应该避免笛卡尔 JOIN，尽管有一些特定的用例，例如生成数据以填充时间序列，我们将在其中有意使用它们。最后，FROM 子句中的表可以别名，或给出一个或多个字母的较短名称，可以在查询的其他子句中引用。别名使查询编写者不必重复输入长表名，并且它们使查询更易于阅读。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。