### 计算机代写|数据库作业代写SQL代考|SQL Versus R or Python

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 Versus R or Python

While SQL is a popular language for data analysis, it isn’t the only choice. $R$ and Python are among the most popular of the other languages used for data analysis. $R$ is a statistical and graphing language, while Python is a general-purpose programming language that has strengths in working with data. Both are open source, can be installed on a laptop, and have active communities developing packages, or extensions, that tackle various data manipulation and analysis tasks. Choosing between $R$ and Python is beyond the scope of this book, but there are many discussions online about the relative advantages of each. Here I will consider them together as codinglanguage alternatives to $\mathrm{SQL}$.

One major difference between SQL and other coding languages is where the code runs and, therefore, how much computing power is available. $\mathrm{SQL}$ always runs on a database server, taking advantage of all its computing resources. For doing analysis, $R$ and Python are usually run locally on your machine, so computing resources are capped by whatever is available locally. There are, of course, lots of exceptions: databases can run on laptops, and R and Python can be run on servers with more resources. When you are performing anything other than the simplest analysis on large data sets, pushing work onto a database server with more resources is a good option. Since databases are usually set up to continually receive new data, SQL is also a good choice when a report or dashboard needs to update periodically.

A second difference is in how data is stored and organized. Relational databes always organize data into rows and columns within tables, so SQL assumes this structure for every query. $\mathrm{R}$ and Python have a wider variety of ways to store data, including variables, lists, and dictionaries, among other options. These provide more flexibility, but at the cost of a steeper learning curve. To facilitate data analysis, $R$ has data frames, which are similar to database tables and organize data into rows and columns. The pandas package makes DataFrames available in Python. Even when other options are available, the table structure remains valuable for analysis.

Looping is another major difference between SQL and most other computer programming languages. A loop is an instruction or a set of instructions that repeats until a specified condition is met. SQL aggregations implicitly loop over the set of data, without any additional code. We will see later how the lack of ability to loop over fields can result in lengthy SQL statements when pivoting or unpivoting data. While deeper discussion is beyond the scope of this book, some vendors have created extensions to SQL, such as PL/SQL in Oracle and T-SQL in Microsoft SQL Server, that allow functionality such as looping.

## 计算机代写|数据库作业代写SQL代考|SQL as Part of the Data Analysis Workflow

Now that I’ve explained what SQL is, discussed some of its benefits, and compared it to other languages, we’ll turn to a discussion of where SQL fits in the data analysis process. Analysis work always starts with a question, which may be about how many new customers have been acquired, how sales are trending, or why some users stick around for a long time while others try a service and never return. Once the question is framed, we consider where the data originated, where the data is stored, the analysis plan, and how the results will be presented to the audience. Figure 1-2 shows the steps in the process. Queries and analysis are the focus of this book, though I will discuss the other steps briefly in order to put the queries and analysis stage into a broader context.

First, data is generated by source systems, a term that includes any human or machine process that generates data of interest. Data can be generated by people by hand, such as when someone fills out a form or takes notes during a doctor’s visit. Data can also be machine generated, such as when an application database records a purchase, an event-streaming system records a website click, or a marketing management tool records an email open. Source systems can generate many different types and formats of data, and Chapter 2 will discuss them, and how the type of source may impact the analysis, in more detail.

The second step is moving the data and storing it in a database for analysis. I will use the terms data warehouse, which is a database that consolidates data from across an organization into a central repository, and data store, which refers to any type of data storage system that can be queried. Other terms you might come across are data mart, which is typically a subset of a data warehouse, or a more narrowly focused data warehouse; and data lake, a term that can mean either that data resides in a file storage system or that it is stored in a database but without the degree of data transformation that is common in data warehouses. Data warehouses range from small and simple to huge and expensive. A database running on a laptop will be sufficient for you to follow along with the examples in this book. What matters is having the data you need to perform an analysis together in one place.

## 计算机代写|数据库作业代写SQL代考|Database Types and How to Work with Them

If you’re working with SQL, you’ll be working with databases. There is a range of database types-open source to proprietary, row-store to column-store. There are onpremises databases and cloud databases, as well as hybrid databases, where an organization runs the database software on a cloud vendor’s infrastructure. There are also a number of data stores that aren’t databases at all but can be queried with SQL.

Databases are not all created equal; each database type has its strengths and weaknesses when it comes to analysis work. Unlike tools used in other parts of the analysis workflow, you may not have much say in which database technology is used in your organization. Knowing the ins and outs of the database you have will help you work more efficiently and take advantage of any special SQL functions it offers. Familiarity with other types of databases will help you if you find yourself working on a project to build or migrate to a new data warehouse. You may want to install a database on your laptop for personal, small-scale projects, or get an instance of a cloud warehouse for similar reasons.

Databases and data stores have been a dynamic area of technology development since they were introduced. A few trends since the turn of the 21 st century have driven the technology in ways that are really exciting for data practitioners today. First, data volumes have increased incredibly with the internet, mobile devices, and the Internet of Things (IoT). In 2020 IDC predicted that the amount of data stored globally will grow to 175 zettabytes by 2025 . This scale of data is hard to even think about, and not all of it will be stored in databases for analysis. It’s not uncommon for companies to have data in the scale of terabytes and petabytes these days, a scale that would have been impossible to process with the technology of the 1990 s and earlier. Second, decreases in data storage and computing costs, along with the advent of the cloud,

have made it cheaper and easier for organizations to collect and store these massive amounts of data. Computer memory has gotten cheaper, meaning that large amounts of data can be loaded into memory, calculations performed, and results returned, all without reading and writing to disk, greatly increasing the speed. Third, distributed compuling has alluwed the breaking up of wurkluads acruss many machines. This allows a large and tunable amount of computing to be pointed to complex data tasks.
Databases and data stores have combined these technological trends in a number of different ways in order to optimize for particular types of tasks. There are two broad categories of databases that are relevant for analysis work: row-store and columnstore. In the next section I’ll introduce them, discuss what makes them similar to and different from each other, and talk about what all of this means as far as doing analysis with data stored in them. Finally, I’ll introduce some additional types of data infrastructure beyond databases that you may encounter.

## 计算机代写|数据库作业代写SQL代考|SQL Versus R or Python

SQL 和其他编码语言之间的一个主要区别是代码运行的位置，因此，有多少计算能力可用。小号问大号始终在数据库服务器上运行，利用其所有计算资源。为了进行分析，R和 Python 通常在您的机器上本地运行，因此计算资源受本地可用资源的限制。当然，也有很多例外：数据库可以在笔记本电脑上运行，R 和 Python 可以在资源更多的服务器上运行。当您对大型数据集执行最简单的分析以外的任何操作时，将工作推送到具有更多资源的数据库服务器上是一个不错的选择。由于数据库通常设置为不断接收新数据，因此当报表或仪表板需要定期更新时，SQL 也是一个不错的选择。

## 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。