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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-visualization-with-python-and-matplotlib/
课程评论：没有评论
课程名称：使用Python和Matplotlib进行数据可视化 课程概述：越来越多的人意识到分析大数据的巨大好处和用途。然而，大多数人缺乏理解原始数据所需的技能和时间。这就是数据可视化的用武之地：通过创建易于阅读、易于理解的图表、图形以及其他数据的可视化表示。Python 3和Matplotlib是可轻松访问和高效使用的工具，可以实现这一目标。 课程内容： - 学习如何处理大数据，利用Python进行可视化。 - 可视化多种形式的2D和3D图形，包括折线图、散点图、条形图等。 - 从各种来源加载和组织数据进行可视化。 - 创建和自定义实时图表。 - 增加细节和风格，使图表更具视觉吸引力。 此课程提供58节课时，累计6小时内容，涵盖了几乎所有Matplotlib能够提供的主要图表。适合已有基础Python知识的学生，按照循序渐进的方式创建各种图表，如折线图、散点图、堆叠图、饼图、条形图、3D图形等。此外，还涵盖了数据导入（包括CSV和NumPy），以及更高级的功能，如自定义坐标轴、样式、注释、平均值和指标，地理制图与Basemap及高级线框图。 完成课程后，学员将深入了解数据可视化的可选方式，并掌握制作设计良好、视觉吸引人的图表的技能。 使用的工具： - Python 3：一种通用编程语言，注重可读性和简洁性，非常适合新手学习。 - Matplotlib：与Python配合使用的绘图库，用户可以将图嵌入到应用程序中。 - IDLE：Python的集成开发环境，推荐在本课程中使用。 总之，完成此课程后，您将能够在Python中高效地进行数据可视化，并创造出美观的图表。
More and more people are realising the vast benefits and uses of analysing big data. However, the majority of people lack the skills and the time needed to understand this data in its original form. That's where data visualisation comes in; creating easy to read, simple to understand graphs, charts and other visual representations of data. Python 3 and Matplotlib are the most easily accessible and efficient to use programs to do just this. Learn Big Data PythonVisualise multiple forms of 2D and 3D graphs; line graphs, scatter plots, bar charts, etc.Load and organise data from various sources for visualisationCreate and customise live graphsAdd finesse and style to make your graphs visually appeallingPython Data Visualisation made EasyWith over 58 lectures and 6 hours of content, this course covers almost every major chart that Matplotlib is capable of providing. Intended for students who already have a basic understanding of Python, you'll take a step-by-step approach to create line graphs, scatter plots, stack plots, pie charts, bar charts, 3D lines, 3D wire frames, 3D bar charts, 3D scatter plots, geographic maps, live updating graphs, and virtually anything else you can think of!Starting with basic functions like labels, titles, window buttons and legends, you'll then move onto each of the most popular types of graph, covering how to import data from both a CSV and NumPy. You'll then move on to more advanced features like customised spines, styles, annotations, averages and indicators, geographical plotting with Basemap and advanced wireframes. This course has been specially designed for students who want to learn a variety of ways to visually display python data. On completion of this course, you will not only have gained a deep understanding of the options available for visualising data, but you'll have the know-how to create well presented, visually appealing graphs too. Tools UsedPython 3: Python is a general purpose programming language which a focus on readability and concise code, making it a great language for new coders to learn. Learning Python gives a solid foundation for learning more advanced coding languages, and allows for a wide variety of applications.Matplotlib: Matplotlib is a plotting library that works with the Python programming language and its numerical mathematics extension 'NumPy'. It allows the user to embed plots into applications using various general purpose toolkits (essentially, it's what turns the data into the graph).IDLE: IDLE is an Integrated Development Environment for Python; i.e where you turn the data into the graph. Although you can use any other IDE to do so, we recommend the use of IDLE for this particular course.
