统计代写|数据可视化代写data visualization代考|Data visualization as discourse

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statistics-lab™ 为您的留学生涯保驾护航 在代写数据可视化data visualization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可视化data visualization代写方面经验极为丰富,各种代写数据可视化data visualization相关的作业也就用不着说。

我们提供的数据可视化data visualization及其相关学科的代写,服务范围广, 其中包括但不限于:

  • 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
Data Visualization — People remember the feeling not the numbers | by  Silvia Li Sam | WRITE LAB
统计代写|数据可视化代写data visualization代考|Data visualization as discourse

统计代写|数据可视化代写data visualization代考|Data visualization as discourse

This book is a contribution to multidisciplinary and multifaceted academic conversation concerning the forms, uses, and roles of data visualization in society. As a collection of chapters which study the conditions under which visualizations are generated, disseminated, and thought to benefit processes of learning, development, and participation, to reuse our own phrase from above, it belongs to the large and diverse field of discourse studies. Although the individual chapters derive from a range of perspectives, the tradition of discourse studies provides a framework. The book leans on a social semiotic understanding of discourse – as the situated application of semiotic resources (such as words and images) by human agents in order to construct and share ideas about the world and to perform social action (or make things happen) (Kress, 2010; van Leeuwen, 2005). The potential meanings carried by semiotic resources are dependent on both cultural conventions and the particular situations of use, including the background and motivations of the human participants, the media used to produce and distribute the messages, and the social practice of which the semiotic material is an integrated part. Discourse studies can offer nuanced analyses of the mediated processes of communication in which data visualizations are situated and also illuminate processes of social struggle and control.

A discourse studies approach combines the micro level with the macro level. It focuses on the relations between the specific structures and forms of the semiotic artefact on the one hand, and the social, technological, and cultural contexts which form it and are formed by it, on the other (Fairclough, 2010; Chouliaraki \& Fairclough, 1999; van Leeuwen, 2005). The concept of discourse thus offers a theoretical and methodological framework for analysing data visualization in discrete social practices, like journalism, public information campaigning, or health communication. These relations between the micro and the macro, between texts and contexts, are apparent in all chapters of the book, although some focus more on the micro level, and others more on the macro level.

Discourse studies include a range of approaches, from those based on an analysis of how meanings are shaped and negotiated in specific social situations, to critical investigations of how words and images play a role in creating or opposing power structures and social inequalities. The latter approaches are often grouped under the term critical discourse studies (or CDA), which was originally theoretically and methodologically modelled by Norman Fairclough (2010). In several chapters in this book, similar critical approaches to the relationship between semiotic practices and social inequalities are used, although the authors do not necessarily all see themselves as discourse studies scholars. Rather, authors adopt such approaches from within a diverse range of disciplines, including gender studies, science and technology studies, (digital) media studies, critical cartography, design, art history, literacy studies, ICT, and the emerging field of data studies. Together, the chapters shine a spotlight on data visualization as an important instance of text-in-society.

统计代写|数据可视化代写data visualization代考|How the book is organized and targeted

The book is organized into five sections. The first, called “Framing Data Visualization’, does the work of framing the contributions in the rest of the book, drawing on a range of conceptual and theoretical resources. The three chapters in this section sketch out three significant issues with which subsequent chapters engage: epistemology, semiotics, and politics respectively. In the first chapter in this section, ‘Ways of knowing with data visualization’, Jill Walker Rettberg explores the ways of knowing that have historically been privileged by different systems for gathering and visualizing data. Giorgia Aiello then maps out how the strategies deployed in a social semiotic approach can help us to understand data visualization

in society in ‘Inventorizing, situating, transforming: Social semiotics and data visualization’. In the final chapter in this section, Torgeir Nærland maps out perspectives from which we might approach analyses of data visualization’s politics, in ‘The political significance of data visualization: Four key perspectives’.

The second section of the book, ‘Living and Working with Data Visualization’, includes chapters which reflect on diverse experiences of and with data visualization in private and professional settings. In Chapter 5 , ‘Rain on your radar: Engaging with weather data visualizations as part of everyday routines’, Eef Masson and Karin van Es explore uses and evaluations of uses of weather data visualizations in everyday life. This is followed by a chapter by Salla-Maaria Laaksonen and Juho Pääkkönen, which shifts the focus to working environments, and explores the uses of data visualizations in social media analytics companies, their role in knowledge claims, and the mechanisms by which they achieve credibility. The chapter is called ‘Between automation and interpretation: Using data visualization in social media analytics companies’. Chapter 7 , ‘Accessibility of data visualizations: An overview of European statistics institutes’, by Mikael Snaprud and Andrea Velazquez, uses multiple approaches to assess the extent to which dataviz shared by National Statistics Institutes (NSIs) are accessible to people with disabilities, and the extent of preparedness for compliance with new EU legislation on web accessibility of NSIs, which are both important characteristics of democratic societies. This is followed by a chapter which explores how data visualizations are evaluated, and whether approaches to evaluation which account for the sociocultural contexts of and influences on dataviz might be possible. This chapter, by Arran Ridley and Christopher Birchall, is called ‘Evaluating data visualization: Broadening the measures of success.’ The subsequent chapter, ‘Approaching data visualizations as interfaces: An empirical demonstration of how data are imag(in)ed’, by Daniela van Geenen and Maranke Wieringa focuses on the case of a specific data visualization produced by the authors, to show how visualization practices allow for interfacing with data and that a particular visualization provides only one perspective on data. In Chapter 10 , ‘Visualizing data: A lived experience’, Jill Simpson draws on her own experience of producing a small-data hand-drawn visualization to explore questions of subjectivity, authenticity, and honesty in data visualization. This section ends with a chapter by Helen Kennedy, Wibke Weber, and Martin Engebretsen called ‘Data visualization and transparency in the news’, which explores the relationship between data visualization and the emerging journalistic norm of transparency.

统计代写|数据可视化代写data visualization代考|Jill Walker Rettberg

Data visualizations combine numeric data with visual representation, and these modes allow them to express certain kinds of knowledge more easily than others. This chapter uses examples of historical data visualizations in order to examine what ways of knowing they privilege. What is the difference between the spatial organization of tools in prehistoric homes and a photograph or bar chart showing information about the same tools, in terms of the kinds of knowledge they enable? How do the systems for gathering and visualizing data during the $18^{\text {th }}$ and $19^{\text {th }}$ centuries shape our understanding of the world? How do data visualizations make us feel that they are objective? How do they shape our ideas of what is possible?
Keywords: Dataism; God trick; Desire for numbers; Correlation and causation; The sublime; Epistemology of data visualization

5 Rules of Engagement When it Comes to Data Visualization - Codemotion
统计代写|数据可视化代写data visualization代考|Data visualization as discourse


统计代写|数据可视化代写data visualization代考|Data visualization as discourse


话语研究方法将微观层面与宏观层面相结合。它一方面关注符号学人工制品的特定结构和形式之间的关系,另一方面关注形成它和由它形成的社会、技术和文化背景(Fairclough,2010;Chouliaraki \ & Fairclough ,1999 年;范列文,2005 年)。因此,话语的概念为分析离散社会实践中的数据可视化提供了理论和方法框架,如新闻、公共信息运动或健康传播。这些微观与宏观之间、文本与语境之间的关系在本书的所有章节中都很明显,尽管有些章节更侧重于微观层面,而另一些则侧重于宏观层面。

话语研究包括一系列方法,从基于对特定社会情境中意义如何形成和协商的分析,到对文字和图像如何在创造或反对权力结构和社会不平等中发挥作用的批判性调查。后一种方法通常归入批判性话语研究(或 CDA)一词,最初是由 Norman Fairclough(2010)在理论和方法上建模的。在本书的几个章节中,对符号学实践与社会不平等之间的关系使用了类似的批判方法,尽管作者不一定都将自己视为话语研究学者。相反,作者从不同的学科范围内采用这些方法,包括性别研究、科学和技术研究、(数字)媒体研究、批判制图学、设计、艺术史、读写能力研究、信息通信技术和新兴的数据研究领域。这些章节一起突出了数据可视化作为社会文本的一个重要实例。

统计代写|数据可视化代写data visualization代考|How the book is organized and targeted

本书分为五个部分。第一个称为“框架数据可视化”,它利用一系列概念和理论资源来构建本书其余部分的贡献。本节的三章分别勾勒出随后各章涉及的三个重要问题:认识论、符号学和政治学。在本节的第一章“数据可视化的认知方式”中,Jill Walker Rettberg 探讨了历史上被不同系统用于收集和可视化数据的认知方式。Giorgia Aiello 然后绘制出在社会符号学方法中部署的策略如何帮助我们理解数据可视化

在社会中的“盘点、定位、转换:社会符号学和数据可视化”。在本节的最后一章中,Torgeir Nærland 在“数据可视化的政治意义:四个关键观点”中描绘了我们可以用来分析数据可视化政治的观点。

本书的第二部分“与数据可视化一起生活和工作”,包括反映私人和专业环境中数据可视化和数据可视化的各种经验的章节。在第 5 章,“雷达上的雨:将天气数据可视化作为日常生活的一部分”,Eef Masson 和 Karin van Es 探讨了天气数据可视化在日常生活中的用途和评估。紧随其后的是 Salla-Maaria Laaksonen 和 Juho Pääkkönen 的一章,将重点转移到工作环境,并探讨了数据可视化在社交媒体分析公司中的使用、它们在知识声明中的作用以及它们获得可信度的机制. 这一章被称为“自动化和解释之间:在社交媒体分析公司中使用数据可视化”。第七章 ,由 Mikael Snaprud 和 Andrea Velazquez 撰写的“数据可视化的可访问性:欧洲统计机构概述”使用多种方法来评估国家统计局 (NSI) 共享的数据可视化对残疾人的访问程度,以及准备遵守关于 NSI 的网络可访问性的新欧盟立法,这都是民主社会的重要特征。接下来的一章探讨了如何评估数据可视化,以及考虑到 dataviz 的社会文化背景和影响的评估方法是否可行。本章由 Arran Ridley 和 Christopher Birchall 撰写,名为“评估数据可视化:扩大成功的衡量标准”。下一章,Daniela van Geenen 和 Maranke Wieringa 所著的“将数据可视化作为接口:如何对数据进行想象(in)的实证演示”侧重于作者制作的特定数据可视化案例,以展示可视化实践如何实现接口与数据,并且特定的可视化仅提供数据的一个视角。在第 10 章“可视化数据:亲身体验”中,Jill Simpson 借鉴了她自己制作小数据手绘可视化的经验,探讨了数据可视化中的主观性、真实性和诚实性等问题。本节以 Helen Kennedy、Wibke Weber 和 Martin Engebretsen 撰写的名为“新闻中的数据可视化和透明度”的章节结束,该章节探讨了数据可视化与新兴的新闻透明度规范之间的关系。

统计代写|数据可视化代写data visualization代考|Jill Walker Rettberg

数据可视化将数字数据与视觉表示相结合,这些模式使它们能够比其他模式更容易地表达某些类型的知识。本章使用历史数据可视化的示例来检查了解它们的特权的方式。史前住宅中工具的空间组织与显示相同工具信息的照片或条形图之间的区别是什么?收集和可视化数据的系统如何在18th 和19th 几个世纪塑造了我们对世界的理解?数据可视化如何让我们觉得它们是客观的?它们如何塑造我们对可能性的想法?
关键词:数据主义;神把戏;对数字的渴望;相关性和因果关系;崇高; 数据可视化的认识论

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术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。



有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。





随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


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。



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