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所在平台: Udemy |
课程主页: https://www.udemy.com/course/big-geospatial-data-analysis-in-python/
课程评论：没有评论
**课程名称：** Python地理空间数据分析入门 **课程概述：** 本课程是一门专为Python初学者设计的课程，旨在帮助您熟练运用Python进行数据分析和可视化。您将学习如何下载和访问Jupyter Notebook环境，并通过提供的示例Python脚本和数据进行GIS数据的操控练习。课程包含高清视频，全程指导您完成学习。 课程假定您没有任何Python基础，因此前两部分将讲解Python基础知识。如果您已有Python基础，这两部分也可作为复习。课程内容涵盖了安装conda以及地理空间数据分析所需的各类库，例如Basemap、Geopandas、Pandas、Matplotlib和Seaborn。同时，您还将学习使用流行的开源工具Jupyter Notebook。 您将掌握如何在Python代码中整合不同的空间库，并通过详细的步骤学习运用各种Python包来操控GIS数据，并通过可视化地理空间数据以获得更深入的洞察。所有课程演示中使用的数据都将提供给您。 **学习成果：** 完成本课程后，您将能够： * 成功下载并配置Jupyter Notebook。 * 安装conda及相关的地理空间数据分析库。 * 使用Python进行各种空间分析，包括GIS数据集的操控、聚合和可视化。
This Python for Beginner course will get you up and running using Python for data analysis and visualization. You will learn how to download and access a Jupyter Notebook environment. You will have sample Python scripts and example data so that you will get a chance to practice manipulating GIS data. Additionally, you will get HD videos to guide you throughout the course.The course assumes you have no prior knowledge of Python, so you also get to learn the basics of Python in the first two sections of the course. However, if you already know Python, the first two sections can serve as a refresher before you jump into the data analysis and visualization part. In the course, you will learn how to install conda and various libraries that are necessary for geospatial data analysis such as Basemap, Geopandas, Pandas, Matplotlib, and Seaborn. We will also use the popular open-source tool, the Jupyter Notebook.You will learn how to integrate different spatial libraries within your Python code. We will walk you step by step to apply various Python packages to manipulate GIS data and visualize geospatial data to get better insights. I will provide you with all the data that I demonstrate in the course. By the end of this course, you will be able to download Jupyter Notebook, install conda, and perform various spatial analyses including manipulating, aggregating, and visualizing GIS datasets using Python.
