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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-data-visualization-using-python/
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课程名称：使用Python进行数据可视化 课程概述： Python是一种简单强大且易于学习的编程语言，其优雅的语法和动态类型特性，使其非常适合数据可视化。本课程旨在帮助希望进入数据可视化领域或提升数据可视化技能的Python用户，通过易于理解和动手实践的方式教授数据可视化的基本概念以及可用的技术和库。 课程内容包括： 1. 数据可视化入门：介绍数据可视化的基本概念及其重要性。 2. 使用Python库进行数据可视化：学习利用如Matplotlib、bqplot和Bokeh等库进行可视化。 3. 创建交互式数据可视化：使用bqplot库编写引人注目的交互式可视化，了解如何编程创建交互式网络图。 4. 构建数据的交互式Web可视化：根据用户的输入生成可视化图表，使得数据表达更加生动。 课程分为两部分： 第一部分“学习Python数据可视化”专注于基本的可视化概念，并通过Matplotlib等库帮助学生分析不同规模的数据集。内容涵盖数据导入与导出、现实世界数据集介绍、可视化类型与技术、以及高级可视化技巧等。 第二部分“Python中的数据可视化项目”则侧重于实际应用，使用bqplot创建交互式可视化，学习如何使用Bokeh库程序化地可视化数据，并最终构建用户可互动的数据Web可视化。 课程讲师介绍： 本课程由Benjamin Keller博士和Harish Garg主讲。Benjamin Keller是海德堡大学的博士后研究员，专注于天文数字建模等研究；Harish Garg拥有17年的软件行业经验，是数据科学家和软件开发负责人，擅长使用R和Python进行数据可视化。 学习本课程，您将能够使用Matplotlib、bqplot、NetworkX、Bokeh和Dash等工具，展示引人入胜的真实数据集，实现有效的数据可视化，提升您的数据沟通能力。
Python is a straightforward, powerful, easy programing language. Python's elegant syntax and dynamic typing, along with its interpreted nature, makes it a perfect language for data visualization that may be a wise investment for your future big-data needs.If you are a Python user who desires to enter the field of data visualization or enhance your data visualization skills to become more effective visual communicator, then this learning path is for you.With this easy to follow, hands-on course you will initially begin with introduction to data visualization, and the techniques and libraries which can be leveraged with the Python language. Ten you will learn to program stunning & interactive Data Visualizations using bqplot, an open source Python library developed by Bloomberg. Furthermore, you will gain knowledge on how to programmatically create interactive network graphs and visualizations & then visualize data with the interactive Python visualization library, Bokeh. Finally, you will build interactive web visualizations of data using Python: you will choose a number of inputs your users can control, then use any Python graphing library to create plots based on those inputs.By the end of this course you will be able to demonstrate visualizations with interesting, real-world data sets. Also you'll be able to create effective visualizations for your data sets using tools: matplotlib, bqplot, NetworkX, Bokeh, and Dash in Python.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Learning Python Data Visualization begins with visualization concepts so viewers can analyze large and small sets of data using libraries such as Matplotlib, IPython, and so on. This course primarily employs the IPython environment and matplotlib, with the following structure: Introduce key data visualization libraries (matplotlib and so on.) and cover data importing/exporting (CSV, Excel, JSON and so on), Introduce real-world data sets (to be visualized in the video), Visualization types/techniques (bar chart, histogram, scatter plot, geospatial, and so on); demonstrate how to customize visualizations. Introduce intermediate topics to create more advanced visualizations and using complex techniques, such as real-time data visualization. By the end of the course, you will be able to demonstrate visualizations with interesting, real-world data sets.In the second course, Data Visualization Projects in Python you will start by programming stunning interactive Data Visualizations using bqplot, an open source Python library developed by Bloomberg. Then you will learn how to programmatically create interactive network graphs and visualizations. You will then programmatically visualize data with the interactive Python visualization library, Bokeh. Finally, you will build interactive web visualizations of data using Python: you will choose a number of inputs your users can control, then use any Python graphing library to create plots based on those inputs.About the AuthorsBenjamin Keller is a postdoctoral researcher in the MUSTANG group at Universität Heidelberg's Astronomisches Rechen-Institut. He obtained his PhD at McMaster University and got his BSc in Physics with a minor in Computer Science from the University of Calgary in 2011. His current research involves numerical modeling of the interstellar medium over cosmological timescales. As an undergraduate at the U of C, he worked with Dr. Jeroen Stil on stacking radio polarization to examine faint extragalactic sources. He also worked in POSSUM Working Group 2 to determine the requirements for stacking applications for the Australian SKA Pathfinder (ASKAP) radio telescope. At McMaster, he worked with Dr. James Wadsley in the Physics & Astronomy department. His current research is focused on understanding how the energy released from supernovae explosions regulate the flow of gas through galaxies, and how that gas is converted into stars.Harish Garg is a Data Scientist and a Lead Software Developer with 17 years' software industry experience. He worked for McAfee/Intel for 11+ years before starting his own software consultancy. He is an expert in creating data visualizations using R, Python, and web-based visualization libraries.
