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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/fundamentals-of-data-visualization
课程评论：没有评论
课程名称：数据可视化基础 课程概述：数据无处不在。图表、图形及其他信息可视化工具帮助人们理解数据。本课程探讨信息可视化的设计、开发和评估，结合设计、计算机图形学、人机交互和数据科学的各个方面，将为您提供创建可视化、使用探索工具和构建数据叙事的实践经验。课程内容包括以用户为中心的设计、基于网络的可视化、数据认知与感知以及设计评估。 此课程可作为科罗拉多大学博尔德分校（CU Boulder）数据科学硕士（MS-DS）学位的一部分获得学术学分。MS-DS是一个跨学科的学位，将来自应用数学、计算机科学、信息科学等部门的教师汇聚在一起。采用基于表现的录取，不需要申请过程，非常适合具有计算机科学、信息科学、数学和统计学等广泛本科学历和/或专业经验的个人。更多关于MS-DS项目的信息，请访问 https://www.coursera.org/degrees/master-of-science-data-science-boulder。 课程大纲： 第一部分：设计基础 在这一模块中，您将学习可视化设计的基础知识，逐步了解可视化的关键组成部分，如何使用颜色、大小和位置等元素有效地表示数据，以及诚实而有效的可视化原则。您还将初步接触Altair，这是一个快速生成交互式可视化的Python库。每周将包括两篇阅读材料或一篇阅读和一个笔记本活动。 第二部分：用户需求 在这一模块中，您将学习如何为特定场景选择正确的可视化方案。您将了解人们在可视化中提出的不同问题，以及如何将设计与这些任务对齐。模块内容将涵盖任务分析基础、任务引导方法以及设计视觉感知的基本知识。每周将包括两篇外部阅读材料或一篇阅读和一个笔记本活动。 第三部分：评估 在这一模块中，您将学习如何评估可视化的有效性。您将学习定性和定量的评估方法，以及如何隔离关键元素进行评估和迭代。模块内容将涵盖基于洞察的评估、访谈研究以及实验设计与分析的基础知识。每周将包括两篇外部阅读材料或一篇阅读和一个笔记本活动。
Part: 1
Title:Basics of Design
Description:In this module, you will learn the foundations of visualization design. You will walk through the key components of a visualization, how we effectively represent data using channels like color, size, and position, and some ground rules for honest and effective visualization. You will also gain preliminary exposure to Altair, a Python library for rapidly generating interactive visualizations. Each week will also include either two readings or one reading and one notebook activity.
Part: 2
Title:User Needs
Description:In this module, you will learn how to choose the right visualization for a given scenario. You will learn how to reason about the different kinds of questions people ask with visualization and, how to align your design with that task. The module will cover basics of task analysis, methods for task elicitation, and foundational knowledge of visual perception for design. Each week will also include two external readings or one reading and one notebook activity.
Part: 3
Title:Evaluation
Description:In this module, you will learn how to assess the effectiveness of your visualization. You will learn both qualitative and quantitative approaches for evaluating visualizations as well as how to isolate key elements for assessment and iteration. The module will cover basics of insight-based evaluation, interview studies, and experimental design and analysis. Each week will also include two external readings or one reading and one notebook activity.
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
