### 计算机代写|数据库作业代写SQL代考|Binning

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• 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…)

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## 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。