### 计算机代写|数据库作业代写SQL代考|Quantitative Versus Qualitative Data

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|Quantitative Versus Qualitative Data

Quantitative data is numeric. It measures people, things, and events. Quantitative data can include descriptors, such as customer information, product type, or device configurations, but it also comes with numeric information such as price, quantity, or visit duration. Counts, sums, average, or other numeric functions are applied to the data. Quantitative data is often machine generated these days, but it doesn’t need to be. Height, weight, and blood pressure recorded on a paper patient intake form are quantitative, as are student quiz scores typed into a spreadsheet by a teacher.

Qualitative data is usually text based and includes opinions, feelings, and descriptions that aren’t strictly quantitative. Temperature and humidity levels are quantitative, while descriptors like “hot and humid” are qualitative. The price a customer paid for a product is quantitative; whether they like or dislike it is qualitative. Survey feedback, customer support inquiries, and social media posts are qualitative. There are whole professions that deal with qualitative data. In a data analysis context, we usually try to quantify the qualitative. One technique for this is to extract keywords or phrases and count their occurrences. We’ll look at this in more detail when we delve into text analysis in Chapter 5. Another technique is sentiment analysis, in which the structure of language is used to interpret the meaning of the words used, in addition to their frequency. Sentences or other bodies of text can be scored for their level of positivity or negativity, and then counts or averages are used to derive insights that would be hard to summarize otherwise. There have been exciting advances in the field of natural language processing, or NLP, though much of this work is done with tools such as Python.

## 计算机代写|数据库作业代写SQL代考|First-, Second-, and Third-Party Data

First-party data is collected by the organization itself. This can be done through server logs, databases that keep track of transactions and customer information, or other systems that are built and controlled by the organization and generate data of interest for analysis. Since the systems were created in-house, finding the people who built them and learning about how the data is generated is usually possible. Data analysts may also be able to influence or have control over how certain pieces of data are created and stored, particularly when bugs are responsible for poor data quality.

Second-party data comes from vendors that provide a service or perform a business function on the organization’s behalf. These are often software as a service (SaaS) products; common examples are CRM, email and marketing automation tools, ecommerce-enabling software, and web and mobile interaction trackers. The data is similar to first-party data since it is about the organization itself, created by its employees and customers. However, both the code that generates and stores the data and the data model are controlled externally, and the data analyst typically has little influence over these aspects. Second-party data is increasingly imported into an organization’s data warehouse for analysis. This can be accomplished with custom code or ETL connectors, or with SaaS vendors that offer data integration.

Third-party data may be purchased or obtained from free sources such as those published by governments. Unless the data has been collected specifically on behalf of the organization, data teams usually have little control over the format, frequency, and data quality. This data often lacks the granularity of first- and second-party data. For example, most third-party sources do not have user-level data, and instead data might be joined with first-party data at the postal code or city level, or at a higher level. Third-party data can have unique and useful information, however, such as aggregate spending patterns, demographics, and market trends that would be very expensive or impossible to collect otherwise.

## 计算机代写|数据库作业代写SQL代考|Sparse Data

Sparse data occurs when there is a small amount of information within a larger set of empty or unimportant information. Sparse data might show up as many nulls and only a few values in a particular column. Null, different from a value of 0 , is the absence of data; that will be covered later in the section on data cleaning. Sparse data can occur when events are rare, such as software errors or purchases of products in the long tail of a product catalog. It can also occur in the early days of a feature or product launch, when only testers or beta customers have access. JSON is one approach that has been developed to deal with sparse data from a writing and storage perspective, as it stores only the data that is present and omits the rest. This is in contrast to a row-store database, which has to hold memory for a field even if there is no value in it.

Sparse data can be problematic for analysis. When events are rare, trends aren’t necessarily meaningful, and correlations are hard to distinguish from chance fluctuations. It’s worth profiling your data, as discussed later in this chapter, to understand if and where your data is sparse. Some options are to group infrequent events or items into categories that are more common, exclude the sparse data or time period from

the analysis entirely, or show descriptive statistics along with cautionary explanations that the trends are not necessarily meaningful.

There are a number of different types of data and a variety of ways that data is described, many of which are overlapping or not mutually exclusive. Familiarity with these types is useful not only in writing good SQL but also for deciding how to analyze the data in appropriate ways. You may not always know the data types in advance, which is why data profiling is so critical. Before we get to that, and to our first code examples, I’ll give a brief review of SQL query structure.

## 有限元方法代写

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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。