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

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|What Is Data Analysis

Collecting and storing data for analysis is a very human activity. Systems to track stores of grain, taxes, and the population go back thousands of years, and the roots of statistics date back hundreds of years. Related disciplines, including statistical process control, operations research, and cybernetics, exploded in the 20th century. Many different names are used to describe the discipline of data analysis, such as business intelligence (BI), analytics, data science, and decision science, and practitioners have a range of job titles. Data analysis is also done by marketers, product managers, business analysts, and a variety of other people. In this book, I’ll use the terms data analyst and data scientist interchangeably to mean the person working with $\mathrm{SQL}$ to understand data. I will refer to the software used to build reports and dashboards as BI tools.

Data analysis in the contemporary sense was enabled by, and is intertwined with, the history of computing. Trends in both research and commercialization have shaped it,

and the story includes a who’s who of researchers and major companies, which we’ll talk about in the section on SQL. Data analysis blends the power of computing with techniques from traditional statistics. Data analysis is part data discovery, part data interpretation, and part data communication. Very often the purpose of data analysis is to improve decision making, by humans and increasingly by machines through automation.

Sound methodology is critical, but analysis is about more than just producing the right number. It’s about curiosity, asking questions, and the “why” behind the numbers. It’s about patterns and anomalies, discovering and interpreting clues about how businesses and humans behave. Sometimes analysis is done on a data set gathered to answer a specific question, as in a scientific setting or an online experiment. Analysis is also done on data that is generated as a result of doing business, as in sales of a company’s products, or that is generated for analytics purposes, such as user interaction tracking on websites and mobile apps. This data has a wide range of possible applications, from troubleshooting to planning user interface (UI) improvements, but it often arrives in a format and volume such that the data needs processing before yielding answers. Chapter 2 will cover preparing data for analysis, and Chapter 8 will discuss some of the ethical and privacy concerns with which all data practitioners should be familiar.

It’s hard to think of an industry that hasn’t been touched by data analysis: manufacturing, retail, finance, health care, education, and even government have all been changed by it. Sports teams have employed data analysis since the early years of Billy Beane’s term as general manager of the Oakland Athletics, made famous by Michael Lewis’s book Moneyball (Norton). Data analysis is used in marketing, sales, logistics, product development, user experience design, support centers, human resources, and more. The combination of techniques, applications, and computing power has led to the explosion of related fields such as data engineering and data science.

## 计算机代写|数据库作业代写SQL代考|What Is SQL

SQL is the language used to communicate with databases. The acronym stands for Structured Query Language and is pronounced either like “sequel” or by saying each letter, as in “ess cue el.” This is only the first of many controversies and inconsistencies surrounding SQL that we’ll see, but most people will know what you mean regardless of how you say it. There is some debate as to whether SQL is or isn’t a programming language. It isn’t a general purpose language in the way that $\mathrm{C}$ or Python are. SQL without a database and data in tables is just a text file. SQL can’t build a website, but it is powerful for working with data in databases. On a practical level, what matters most is that SQL can help you get the job of data analysis done.

IBM was the first to develop SQL databases, from the relational model invented by Edgar Codd in the 1960s. The relational model was a theoretical description for managing data using relationships. By creating the first databases, IBM helped to advance the theory, but it also had commercial considerations, as did Oracle, Microsoft, and every other company that has commercialized a database since. From the beginning, there has been tension between computer theory and commercial reality. SQL became an International Organization for Standards (ISO) standard in 1987 and an American National Standards Institute (ANSI) standard in 1986. Although all major databases start from these standards in their implementation of SQL, many have variations and functions that make life easier for the users of those databes. These come at the cost of making SQL more difficult to move between databases without some modifications.

SQL is used to access, manipulate, and retrieve data from objects in a database. Databases can have one or more schemas, which provide the organization and structure and contain other objects. Within a schema, the objects most commonly used in data analysis are tables, views, and functions. Tables contain fields, which hold the data. Tables may have one or more indexes; an index is a special kind of data structure that allows data to be retrieved more efficiently. Indexes are usually defined by a databe administrator. Views are essentially stored queries that can be referenced in the same way as a table. Functions allow commonly used sets of calculations or procedures to be stored and easily referenced in queries. They are usually created by a database administrator, or DBA. Figure 1-1 gives an overview of the organization of databases.

## 计算机代写|数据库作业代写SQL代考|Benefits of SQL

There are many good reasons to use SQL for data analysis, from computing power to its ubiquity in data analysis tools and its flexibility.

Perhaps the best reason to use SQL is that much of the world’s data is already in databases. It’s likely your own organization has one or more databases. Even if data is not already in a database, loading it into one can be worthwhile in order to take advantage of the storage and computing advantages, especially when compared to alternatives such as spreadsheets. Computing power has exploded in recent years, and data warehouses and data infrastructure have evolved to take advantage of it. Some newer cloud databases allow massive amounts of data to be queried in memory, speeding things up further. The days of waiting minutes or hours for query results to return may be over, though analysts may just write more complex queries in response.

SQL is the de facto standard for interacting with databases and retrieving data from them. A wide range of popular software connects to databases with SQL, from spreadsheets to BI and visualization tools and coding languages such as Python and $\mathrm{R}$ (discussed in the next section). Due to the computing resources available, performing as much data manipulation and aggregation as possible in the database often has advantages downstream. We’ll discuss strategies for building complex data sets for downstream tools in depth in Chapter 8 .

The basic SQL building blocks can be combined in an endless number of ways. Starting with a relatively small number of building blocks-the syntax -SQL can accomplish a wide array of tasks. SQL can be developed iteratively, and it’s easy to review the results as you go. It may not be a full-fledged programming language, but it can do a lot, from transforming data to performing complex calculations and answering questions.

Last, SQL is relatively easy to learn, with a finite amount of syntax. You can learn the basic keywords and structure quickly and then hone your craft over time working with varied data sets. Applications of SQL are virtually infinite, when you take into account the range of data sets in the world and the possible questions that can be asked of data. SQL is taught in many universities, and many people pick up some skills on the job. Even employees who don’t already have SQL skills can be trained, and the learning curve may be easier than that for other programming languages. This makes storing data for analysis in relational databases a logical choice for organizations.

## 计算机代写|数据库作业代写SQL代考|What Is SQL

SQL 是用于与数据库通信的语言。该首字母缩略词代表结构化查询语言，发音类似于“sequel”，或者发音为“ess cue el”中的每个字母。这只是我们将看到的围绕 SQL 的许多争议和不一致中的第一个，但无论您怎么说，大多数人都会知道您的意思。关于 SQL 是不是一种编程语言存在一些争论。它不是一种通用语言C或 Python 是。没有数据库和表中数据的 SQL 只是一个文本文件。SQL 不能建立网站，但它在处理数据库中的数据方面非常强大。在实践层面上，最重要的是 SQL 可以帮助您完成数据分析工作。

IBM 是第一个从 Edgar Codd 在 1960 年代发明的关系模型开发 SQL 数据库的公司。关系模型是使用关系管理数据的理论描述。通过创建第一个数据库，IBM 帮助推进了这一理论，但它也有商业考虑，甲骨文、微软和其他所有将数据库商业化的公司也是如此。从一开始，计算机理论与商业现实之间就存在紧张关系。SQL 于 1987 年成为国际标准组织 (ISO) 标准，并于 1986 年成为美国国家标准协会 (ANSI) 标准。虽然所有主要数据库在实施 SQL 时都从这些标准开始，但许多数据库都有变体和功能，使生活更轻松这些数据库的用户。

SQL 用于从数据库中的对象访问、操作和检索数据。数据库可以有一个或多个模式，这些模式提供组织和结构并包含其他对象。在模式中，数据分析中最常用的对象是表、视图和函数。表包含保存数据的字段。表可能有一个或多个索引；索引是一种特殊的数据结构，可以更有效地检索数据。索引通常由数据库管理员定义。视图本质上是存储的查询，可以以与表相同的方式引用。函数允许存储常用的计算或过程集并在查询中轻松引用。它们通常由数据库管理员或 DBA 创建。图 1-1 概述了数据库的组织结构。

## 计算机代写|数据库作业代写SQL代考|Benefits of SQL

SQL 是与数据库交互并从中检索数据的事实标准。广泛的流行软件使用 SQL 连接到数据库，从电子表格到 BI 和可视化工具和编码语言，如 Python 和R（在下一节中讨论）。由于可用的计算资源，在数据库中执行尽可能多的数据操作和聚合通常具有下游优势。我们将在第 8 章深入讨论为下游工具构建复杂数据集的策略。

## 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。