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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-data-visualization-with-python-and-matplotlib-3/
课程评论：没有评论
课程名称：用Python和Matplotlib 3掌握数据可视化 概述：Matplotlib是一个跨平台的数据可视化工具，能够创建高级和互动的数据可视化，以展示数据集中的洞见。它与多种操作系统和图形后端良好兼容，帮助用户轻松定制数据图表、构建3D图形并处理真实世界数据。Python优雅的语法、动态类型以及解释型特性，使其成为数据可视化的理想语言。本课程特别适合Python开发者和数据科学家，旨在使用Matplotlib 3创建展示数据洞见的高级级别数据可视化。该课程采取逐步指导的方式，带你进入Python和Matplotlib 3的数据可视化世界。 课程内容包括四个部分： 1. **Matplotlib基础**：介绍Matplotlib的基本概念和数据可视化的基础。学习如何制作各种基本图形，如线图、散点图、条形图和直方图，以及如何控制图形的轴、刻度、字体和颜色。 2. **高级图表开发**：探索Matplotlib的高级绘图和功能，包括特定用途的图表、3D图表以及非笛卡尔和向量图的构建，掌握如何添加文本、线条和注释等。 3. **数据可视化实用配方**：提供创建互动数据可视化的实用技巧，重点在于减少复杂性，快速高效地做到需要的可视化任务，基于真实数据的实例进行演示。 4. **Matplotlib 3精通**：深入了解Matplotlib的最新版本，通过逐步指导掌握高级概念，能够轻松应对复杂的数据可视化项目。 经过本课程的学习，学员将成为Matplotlib 3的数据可视化专家，掌握有效且实用的数据可视化配方。 **讲师简介**： - Benjamin Keller：麦克马斯特大学博士候选人，研究涉及星系演化的数值建模。 - Harish Garg：软件专业人士，拥有18年以上软件行业经验，专注于数据分析和数据科学。 - Amaya Nayak：数据科学顾问，拥有超过10年Python编程、数据分析和可视化的经验。 本课程将为您提供全面的Matplotlib 3培训，帮助您掌握数据可视化的核心技能。
Matplotlib is a multi-platform data visualization tool for creating advanced-level and interactive data visualizations that showcase insights from your datasets. One of Matplotlib's most important features is its ability to work well with many operating systems and graphics backends. Matplotlib helps in customizing your data plots, building 3D plots and tackling real-world data with ease. Python's elegant syntax and dynamic typing, along with its interpreted nature, make it a perfect language for data visualization. If you're a Python Developer or a data scientist looking to create advanced-level Data Visualizations that showcase insights from your datasets with Matplotlib 3, then this Course is perfect for you!This comprehensive 4-in-1 course follows a step-by-step approach to entering the world of data Visualization with Python and Matplotlib 3. To begin with, you'll use various aspects of data visualization with Matplotlib to construct different types of plot such as lines and scatters, bar plots, and histograms. You'll use Matplotlib 3's animation and interactive capabilities to spice up your data visualizations with a real-world dataset of stocks. Finally, you'll master Matplotlib by exploring the advanced features and making complex data visualization concepts seem very easy.By the end of the course, you'll become a data visualizations expert with Matplotlib 3 by learning effective and practical data visualization recipes.Contents and OverviewThis training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Matplotlib for Python Developers, covers understanding the basic fundamentals of plotting and data visualization using Matplotlib. In this course, we hit the ground running and quickly learn how to make beautiful, illuminating figures with Matplotlib and a handful of other Python tools. We understand data dimensionality and set up an environment by beginning with basic plots. We enter into the exciting world of data visualization and plotting. You'll work with line and scatter plots and construct bar plots and histograms. You'll also explore images, contours, and histograms in depth. Plot scaffolding is a very interesting topic wherein you'll be taken through axes and figures to help you design excellent plots. You'll learn how to control axes and ticks, and change fonts and colors. You'll work on backends and transformations. Then lastly you'll explore the most important companions for Matplotlib, Pandas, and Jupyter used widely for data manipulation, analysis, and visualization. By the end of this course, you'll be able to construct effective and beautiful data plots using the Matplotlib library for the Python programming language.The second course, Developing Advanced Plots with Matplotlib, covers exploring advanced plots and functions with Matplotlib. In this video course, you'll get hands-on with customizing your data plots with the help of Matplotlib. You'll start with customizing plots, making a handful of special-purpose plots, and building 3D plots. You'll explore non-trivial layouts, Pylab customization, and more on tile configuration. You'll be able to add text, put lines in plots, and also handle polygons, shapes, and annotations. Non-Cartesian and vector plots are exciting to construct, and you'll explore them further in this tutorial. You'll delve into niche plots and visualizing ordinal and tabular data. In this video, you'll be exploring 3D plotting, one of the best features when it comes to 3D data visualization, along with Jupyter Notebook, widgets, and creating movies for enhanced data representation. Geospatial plotting will be also be explored. Finally, you'll learn how to create interactive plots with the help of Jupyter. By the end of this video tutorial, you'll be able to construct advanced plots with additional customization techniques and 3D plot types.The third course, Data Visualization Recipes with Python and Matplotlib 3, covers practical recipes for creating interactive data visualizations easily with Matplotlib 3. This course cuts down all the complexities and unnecessary details. It boils it down to the things you really need to get those visualizations going quickly and efficiently. The course gives you practical recipes to do what exactly needs to be done in the minimum amount of time. All the examples are based on real-world data with practical visualization solutions. By the end of the course, you'll be able to get the most out of data visualizations where Matplotlib 3 is concerned.The fourth course, Mastering Matplotlib 3, covers mastering the power of data visualization with Matplotlib 3. This course will help you delve into the latest version of Matplotlib, 3, in a step-by-step and engaging manner. Through this course, you will master advanced Matplotlib concepts and will be able to tackle any Data Visualization project with ease and with increasing complexity. By the end of the course, you will have honed your expertise and mastered data visualization using the full potential of Matplotlib 3.By the end of the course, you'll become a data visualizations expert with Matplotlib 3 by learning effective and practical data visualization recipes.About the AuthorsBenjamin Keller is currently a Ph.D. candidate at McMaster University and achieved his BSc in Physics with a minor in Computer Science from the University of Calgary in 2011. His current research involves numerical modeling of galaxy evolution over cosmological timescales. As an undergraduate at the U of C, he worked on stacking radio polarization to examine faint extragalactic sources. He also worked in the POSSUM Working Group 2 to determine the requirements for stacking applications for the Australian SKA Pathfinder (ASKAP) radio telescope. His current research is focused on developing and improving subgrid models used in simulations of galaxy formation and evolution. He is particularly interested in questions involving stellar feedback (supernovae, stellar winds, and so on) and its impact on galaxies and their surrounding intergalactic medium.Harish Garg is a co-founder and software professional with more than 18 years of software industry experience. He currently runs a software consultancy that specializes in the data analytics and data science domain. He has been programming in Python for more than 12 years and has been using Python for data analytics and data science for 6 years. He has developed numerous courses in the data science domain and has also published a book involving data science with Python, including Matplotlib.Amaya Nayak is a Data Science Domain consultant with BignumWorks Software LLP. She has more than 10 years' experience in the fields of Python programming, data analysis, and visualization using Python and JavaScript, using tools such as D3.js, Matplotlib, ggplot, and more. With over 5 years' experience as a data scientist, she works on various data analysis tasks such as statistical data, data munging, data extraction, data visualization, and data validation.
