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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/python-plotting
课程评论：没有评论
课程名称：Python中的应用绘图、图表和数据表示 课程概述：本课程将向学习者介绍信息可视化的基础知识，重点是在使用matplotlib库进行报告和绘图。课程将从设计和信息素养的角度入手，探讨什么是好的和坏的可视化，以及统计测量如何转化为视觉效果。第二周将重点介绍用于在Python中创建可视化的技术matplotlib，并向用户介绍创建基本图表的最佳实践以及如何在框架内实现设计决策。第三周将介绍matplotlib的功能，并演示多种基本统计图表，帮助学习者识别特定方法在特定问题上的适用性。课程最后将讨论其他形式的数据结构化和可视化。 此课程建议在完成《Python中的数据科学导论》后学习，并在继续其他应用数据科学与Python课程之前修读，例如《Python中的应用机器学习》、《Python中的应用文本挖掘》和《Python中的应用社会网络分析》。 课程大纲： - 模块1：信息可视化的原则 描述：本模块介绍信息可视化的原则，提供有关设计的思考工具和创建有效可视化的图形启发式方法。本模块包括课程的评分、先修要求和期望的相关信息。 - 模块2：基本绘图 描述：本模块深入基本绘图，学习者将使用真实的CSV天气数据，操纵数据以显示特定日期范围内的最低和最高温度，并使用matplotlib创建折线图。此外，还将通过叠加某一年创纪录数据的散点图来演示复合图表的过程。 - 模块3：绘图基础 描述：本模块探讨绘图基础，学习者将在本周的作业中基于学术研究实施新的可视化技术。作业灵活，可以选择从简单的静态图像到交互式图表，允许用户设置使用的数值范围。 - 模块4：应用可视化 描述：在本模块中，所有内容将开始结合。最终作业为“成为数据科学家”，要求学习者识别来自相同区域的至少两个可公开获取且在有意义维度上一致的数据集。学习者需要提出一个可以通过这些数据集回答的研究问题，并使用matplotlib创建一个视觉图形来解决所述研究问题，然后解释该视觉图形如何满足研究问题的要求。
Name:Module 1: Principles of Information Visualization
Description:In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module.
Name:Module 2: Basic Charting
Description:In this module, you will delve into basic charting. For this week’s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year.
Name:Module 3: Charting Fundamentals
Description:In this module you will explore charting fundamentals. For this week’s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used.
Name:Module 4: Applied Visualizations
Description:In this module, then everything starts to come together. Your final assignment is entitled “Becoming a Data Scientist.” This assignment requires that you identify at least two publicly accessible datasets from the same region that are consistent across a meaningful dimension. You will state a research question that can be answered using these data sets and then create a visual using matplotlib that addresses your stated research question. You will then be asked to justify how your visual addresses your research question.
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
