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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/visualization-for-data-journalism
课程评论：没有评论
课程名称：数据新闻可视化 概述：数据讲故事是新闻实践的一个重要组成部分，正经历着一场复兴。曾被视为“艺术部门”的图形团队如今成为新闻机构的核心部分。在这一领域工作的人（通常有多种职称，如数据记者、新闻艺术家、图形记者、开发者等）被期望成为全面的记者，并与记者和编辑密切合作。该课程旨在教授数据的视觉呈现思维、作用及正确的方法。您将学习如何制作如《纽约时报》、Vox、Pew以及FiveThirtyEight等处的图表，最终能够在出版物、博客文章和网站上嵌入您制作的美丽图表。 本课程假设您具备基本的编程技能，最好是Python。然而，我们会在第一模块提供简要的Python复习，以便您刷新基础知识并进行简单的数据分析。 课程大纲： 1. 课程导向：熟悉课程内容、同学及学习环境。 2. 模块一：新闻机构中的可视化：总结数据可视化在新闻中的历史及新兴趋势，比较不同类型的图表，学习如何识别各种图形形式及其在故事中的应用情况。 3. 模块二：数据与视觉感知：学习成功的数据可视化案例，了解数据转化为信息的过程，探讨视觉感知理论及可视化概念，评估前注意属性的重要性，并进行数据整理的实践。 4. 模块三：叙事故事telling：学习将可视化融入叙事的框架和技术，研究互动和信息绘制的角色，开始使用Python创建图表。 5. 模块四：认知负载与色彩感知：探讨视觉中的认知和记忆概念，学习使用恰当的色彩及Gestalt原则以简化数据可视化，进行创建交互式地图的练习。 课程结论：对所学知识进行总结与回顾。
Name:Course Orientation
Description:In this module, you will become familiar with the course, your classmates, and the learning environment.
Name:Module 1: Visualization in Newsrooms
Description:This module starts with a summary of the history and emerging trends of data visualization in journalism. You will then explore various types of charts and compare their pros and cons. By doing so, you will be able to recognize a wide variety of graphical forms and evaluate their capabilities/shortcomings as well as what situations each chart type is typically used in storytelling. We will also go through the classic reading by Edward Tufte, The Visual Display of Quantitative Information, and learn how to locate and articulate errors and deception in data visualization.
Name:Module 2: Data and Visual Perception
Description:In this module, we will first look at some examples of successful data visualizations in journalism. We will then drill down on numbers, learning the process of transforming data into information. Next, we will explore theories in visual perception and concepts in visualization and familiarize ourselves with the visual channel ranking—a useful guideline in designing news visualizations. You will evaluate pre-attentive attributes and why they are important in visualizations. You will also have hands-on practice to learn how data wrangling helps us make informed decisions.
Name:Module 3: Narrative Storytelling
Description:In this module, we will learn about the frameworks and techniques that can be used to integrate visualizations into a narrative. You will examine the role messaging and interactions play in drawing readers into a story package that contains greater detail. For the hands-on exercise, you will start creating graphs in Python. You will apply design theories and concepts you previously learned to build line charts, bar charts, and scatter plots.
Name:Module 4: Cognitive Load and Color Perception
Description:In this final module, we will explore some related concepts of cognition and memory in visualization. You will examine the importance of using the “right” amount of color in the right place and apply Gestalt principles to de-clutter your data visualization. In the end, we will work on various exercises to create interactive maps with Python.
Name:Course Conclusion
Description:
While telling stories with data has been part of the news practice since its earliest days, it is in the midst of a renaissance. Graphics desks which used to be deemed as “the art department,” a subfield outside the work of newsrooms, are becoming a core part of newsrooms’ operation. Those people (they often have various titles: data journalists, news artists, graphic reporters, developers, etc.) who design news graphics are expected to be full-fledged journalists and work closely with reporters and editors. The purpose of this class is to learn how to think about the visual presentation of data, how and why it works, and how to doit the right way. We will learn how to make graphs like The New York Times, Vox, Pew, and FiveThirtyEight. In the end, you can share–embed your beautiful charts in publications, blog posts, and websites. This course assumes you understand basic coding skills, preferably Python. However, we also provide a brief review on Python in Module 1, in case you want to refresh yourself on the basics and perform simple data analysis.
