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

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。

• 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

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

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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。