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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-storytelling
课程评论：没有评论
课程名称：数据故事讲述 概述：本课程将探讨在处理复杂数据集时涉及的更高级概念。通过研究视觉元素与数据理解之间的联系，我们将审视这些概念如何通过数据故事讲述相互作用。在回顾如何避免错误可视化和数据错误表示的关键要点后，您将继续在Tableau中进行S&P 500股票行业的多变量描述性分析。 课程大纲： 第一部分：多变量可视化方法 描述：对于大多数复杂数据集，多变量理解是进行复杂分析所必需的。数据的交互视觉展示是多变量分析的关键组成部分，使在复杂数据集中发现高维关系变得更加直观。此模块将展示用于表达比较的各种图表类型和可视化方法，同时您将有机会在Tableau中实践相关性分析。 第二部分：统计关系和分布 描述：您认为互动在普通观众理解复杂数据集方面有多重要？在此模块中，我们将探讨数据的交互式展示如何使复杂数据集中的高维关系更加直观，并辩论可视化中的互动对较大观众是帮助还是障碍。 第三部分：用数据讲故事 描述：在向观众传达数据时，故事讲述为何重要？您应该在可视化中实施哪些故事讲述原则？在此模块中，我们将探讨故事讲述的各个方面以及如何构建您的故事，以有效地向关键利益相关者传达正确的见解。 第四部分：伦理、欺骗与谬论 描述：每当我们创建一个可视化时，都在创建数据的抽象模型。作为设计师，我们必须确保数据尽可能真实和客观地被呈现，因为数据可有误导、欺骗或混淆的能力。在此模块中，我们将研究一些有问题的可视化，以及如何避免数据的错误表示。您还将应用所学知识，在Tableau中执行多变量可视化方法。
Part: 1
Title:Multivariate Visualization Methods
Description:For all but the simplest datasets, complex analytics requires a multivariate understanding of the data being studied. Visual interactivity with the data is a key component of multivariate analytics and makes finding higher dimensional relationships in complex datasets more intuitive. In this module, we’ll take a look at various chart types and visualizations used to express comparisons. You will also have the opportunity to practice correlations in Tableau.
Part: 2
Title:Statistical Relationships and Distributions
Description:How important do you think interaction is for general audiences to gain an understanding of complex data sets? In this module, we’ll explore how visual interactivity with the data makes higher dimensional relationships in complex datasets more intuitive and debate whether interactivity in visualization is a hindrance or help for larger audiences.
Part: 3
Title:Storytelling with Data
Description:Why does storytelling matter when delivering data to your audiences? What are the principles of storytelling that you should implement into your visuals? In this module, we’ll examine aspects of storytelling and how to structure your story to effectively communicate the right insights to your key stakeholders.
Part: 4
Title:Ethics, Deception, and Fallacies
Description:Any time we create a visualization, we create an abstract model of the data. As designers, we must ensure that the data is represented as truthfully and objectively as possible because they have the ability to mislead, deceive, or confuse. In this module, we’ll take a look at some problematic visualizations and how to avoid misrepresenting data. You’ll also put your knowledge to use and perform multivariate visualization methods in Tableau.
This course will cover the more complex concepts that become involved when working beyond simple datasets. Exploring the connection between visual aspects and data understanding, we will examine how those concepts work together through data storytelling. After reviewing key points on how to avoid problematic visualizations and data misrepresentation, you will continue working in Tableau performing multivariate descriptive analysis of the S&P 500 stock sectors.
