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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/codio-visualizing-and-communicating-results-in-python-with-jupyter
课程评论：没有评论
课程名称：使用 Jupyter 在 Python 中可视化和交流结果 课程概述：在几分钟内编写并运行您的第一个 Python 程序，无需安装任何软件！本课程专为具有有限编码经验的学习者设计，旨在为使用 Jupyter Notebook 的数据呈现提供基础。本课程帮助学习者描述和推断数据，更好地交流和展示数据。 课程模块涵盖广泛的可视化技术，允许您说明和比较数据集的组成，确定数据集的分布，以及可视化复杂的数据（例如基于地理的数据）。建议在参加本课程之前完成《使用 pandas 和 matplotlib 在 Spyder 中的数据分析》课程。 为提供真正的动手自学体验，本课程不包含视频。作业包括简短的解释、图像和可运行的代码示例，并提供建议的修改，以便深入探索代码示例，增强理解能力。您将享受到多种评估项目的即时反馈，轻松地从快速理解检查（选择题、填空题和代码块重组）进阶到小型、可处理的编码练习，所需时间仅为几分钟而非数小时。最后，课程末尾的累积实验将为您提供将所有学到的概念应用于现实世界的机会。 课程大纲： 1. 创建比较和组成图表 2. 创建分布图表 3. 创建专业可视化 4. 使用 Jupyter Notebook 交流数据 5. 使用 Jupyter 可视化数据和交流结果 通过此课程，您将掌握如何有效地使用可视化工具，在数据分析中更好地传达您的发现。
Part: 1
Title:Creating Comparison and Composition Charts
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Part: 2
Title:Creating Distribution Charts
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Part: 3
Title:Creating Specialized Visualizations
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Part: 4
Title:Communicating Data Using Jupyter notebook
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Part: 5
Title:Visualizing Data and Communicating Results with Jupyter
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Code and run your first Python program in minutes without installing anything! This course is designed for learners with limited coding experience, providing a foundation for presenting data using visualization tools in Jupyter Notebook. This course helps learners describe and make inferences from data, and better communicate and present data. The modules in this course will cover a wide range of visualizations which allow you to illustrate and compare the composition of the dataset, determine the distribution of the dataset, and visualize complex data such as geographically-based data. Completion of Data Analysis in Python with pandas & matplotlib in Spyder before taking this course is recommended. To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours. Finally, an accumulative lab at the end of the course will provide you an opportunity to apply all learned concepts within a real-world context.
