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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/python-data-visualization
课程评论：没有评论
课程名称：顶点项目：使用Python进行数据检索、处理和可视化 课程概述：在本课程的顶点项目中，学生将构建一系列应用程序，以使用Python检索、处理和可视化数据。项目将涉及专业化的所有元素。在顶点项目的第一部分，学生将进行一些可视化练习，以熟悉所使用的技术，然后追求自己的项目，以可视化他们拥有或可以找到的其他数据。本课程以《Python for Everybody》中第15和第16章为基础，主要涵盖Python 3的内容。 课程大纲： 1. 欢迎进入顶点项目 - 描述：恭喜大家走到这一步。在开始之前，请观看介绍视频并阅读顶点项目概述。课程资源部分包含可能在未来几周参考的附加课程材料。 2. 构建搜索引擎 - 描述：本周我们将下载并运行一个简单版本的Google PageRank算法，并练习爬取一些内容。此作业为同行评审，并且是课程中三个可选荣誉作业中的第一个。这是专业化课程4中所覆盖材料的延续，基于教材第16章。 3. 数据源探索（项目） - 描述：可选的顶点项目是你选择、处理和可视化你所选择的数据的机会，并获得来自同伴的反馈。该项目不计分，过程可以简单或复杂。本周的作业是确定一个数据源，并在讨论论坛发布简短帖子，描述该数据源并概述可以对此进行的分析。你不必使用此处提供的数据源进行实际分析。 4. 爬取和建模电子邮件数据 - 描述：在我们的第二个可选荣誉作业中，我们将从Sakai开源项目中检索和处理电子邮件数据。视频讲座将指导你完成数据检索、清理和建模的过程。 5. 访问新数据源（项目） - 描述：本周的任务是发表一个讨论主题帖，反映你在检索和清理数据源方面所取得的进展，以便进行分析。鼓励其他学生提供反馈，以帮助你完善过程。 6. 可视化电子邮件数据 - 描述：在最后一个可选荣誉作业中，我们将对你所检索和处理的电子邮件数据进行两次可视化：通过词云可视化频率分布，并通过时间线显示数据随时间的变化。 7. 可视化新数据源（项目） - 描述：本周你将向班级讨论你的数据分析。虽然许多项目将导致数据可视化，但分析数据的其他结果同样受到重视，因此请使用最适合你所选择数据集的分析和展示形式。
Name:Welcome to the Capstone
Description:Congratulations to everyone for making it this far. Before you begin, please view the Introduction video and read the Capstone Overview. The Course Resources section contains additional course-wide material that you may want to refer to in future weeks.
Name:Building a Search Engine
Description:This week we will download and run a simple version of the Google PageRank Algorithm and practice spidering some content. The assignment is peer-graded, and the first of three optional Honors assignments in the course. This a continuation of the material covered in Course 4 of the specialization, and is based on Chapter 16 of the textbook.
Name:Exploring Data Sources (Project)
Description:The optional Capstone project is your opportunity to select, process, and visualize the data of your choice, and receive feedback from your peers. The project is not graded, and can be as simple or complex as you like. This week's assignment is to identify a data source and make a short discussion forum post describing the data source and outlining some possible analysis that could be done with it. You will not be required to use the data source presented here for your actual analysis.
Name:Spidering and Modeling Email Data
Description:In our second optional Honors assignment, we will retrieve and process email data from the Sakai open source project. Video lectures will walk you through the process of retrieving, cleaning up, and modeling the data.
Name:Accessing New Data Sources (Project)
Description:The task for this week is to make a discussion thread post that reflects the progress you have made to date in retrieving and cleaning up your data source so can perform your analysis. Feedback from other students is encouraged to help you refine the process.
Name:Visualizing Email Data
Description:In the final optional Honors assignment, we will do two visualizations of the email data you have retrieved and processed: a word cloud to visualize the frequency distribution and a timeline to show how the data is changing over time.
Name:Visualizing new Data Sources (Project)
Description:This week you will discuss the analysis of your data to the class. While many of the projects will result in a visualization of the data, any other results of analyzing the data are equally valued, so use whatever form of analysis and display is most appropriate to the data set you have selected.
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
