Introduction to Geospatial Data Analysis in Python
What you'll learn
- Download Python and the Jupyter Notebook using Anaconda
- Create Interactive Maps with Leaflet
- Import data in to Python for spatial analysis and visualization
- Organize data inside a pandas data frame
- Query data from Python pandas data frame
- Apply various spatial data visualization including time series plots, heatmaps, and base maps
- This course has no requirements.
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.
Who this course is for:
- Anyone who wants to leverage the power of the Python programming language for handling geospatial data.
- Anyone who needs to learn Python in a Jupyter Notebook environment.
- Anyone who wants to manipulate spatial data using Python.
Spatial eLearning provides online courses teaching remote sensing, GIS, machine learning, cloud computing, and spatial data science skills. Our mission is to make highly valuable geospatial data science skills accessible and affordable to anyone and anywhere around the world. We teach 20,000 plus students in over 170 countries around the world. Spatial eLearning’s valuable learning resources include webinars, books, free tutorials, and online courses.
I am a geospatial data scientist with 15 plus years of experience. I am a former NASA Earth and Space Science fellow. My research interests include remote sensing, big data and environmental change. More specifically, I am interested in applying big geospatial data, cloud computing and machine learning to solve complex environmental problems, especially land cover change, climate change, water resource, agriculture, and public health.