Empirical Modeling and Data Analysis for Engineers and Applied Scientists English | 25 July 2016 | ISBN: 3319327674 | 264 Pages This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and “applied science” is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as “Statistics for Engineers and Scientists” without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models – predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes.

很棒的学术数据分析 学术数据分析的资源清单，包括数据集，论文和有关文献计量学，引文分析的代码以及其他学术共享资源。 可在线访问 目录 隶属关系 高度度量和尺寸 工具 发布数据集和分析的用户界面 收集开放获取文件的工具 研究论文分类工具 可视化 语言处理和信息提取 引文和元数据提取 出版地点 期刊 专题会议 工作坊 暑期学校 协会与社区 会费 用markdown-toc生成的目录 数据集 出版与引文 Arnet矿工 微软学术图 打开学术图-MAG + AMiner OpenAIRE研究图-在此处了解更多信息 语义学者语料库 CiteSeer 考研 用于引用字符串解析的CORA数据集 人文和多语言引文字符串解析Flux-CiM和ICONIP ，有关详细信息，请参见Neural ParsCit论文 社会科学对英语和德语引文的引文字符串解析数据-与Grobid和Cermine的比较 Cro

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该软件包提供了一些不错的实用程序，用于创建和加载对拓扑数据分析有用的数据集。 当前，我们提供具有特定拓扑特征的各种综合数据集。 设置 安装就像 pip install tadasets 用法 形状构造函数在功能接口中公开，每个函数都返回一个numpy数组，其中包含在对象上采样的数据。 可用的对象包括 圆环面 d球 瑞士卷 无限符号 通过提供ambient参数，可以将每个形状嵌入任意的环境尺寸中。 此外，可以通过noise参数将noise添加到形状中。 import tadasets torus = tadasets.torus(n=2000, c=2, a=1, ambient=200, noise=0.2) swiss_roll = tadasets.swiss_roll(n=2000, r=4, ambient=10, noise=1.2) dsphere = tadasets.

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