yangxhcaf/adv-python-for-gis-and-rs

Ideas for a more advanced Python class for GIS and Remote Sensing, https://gbrunner.github.io/Advanced_Python_for_GIS_and_RS/

Advanced Python for Remote Sensing\GIS

Course Description

Have you ever wanted to learn how to publish, consume, and use geographic web services? Are in intrested in learning how typical desktop GIS and remote sensing workflows change when the data is shared as a web service? Do you want to learn how to create webmaps and webaps using Python, Javascript, and HTML? After completing this course, you will understand how to do all of these things! In Advanced Python for GIS and Remote Sensing, students will learn how to publish, consume, and analyze web services using Python. They will be introduced to more powerful, more advanced Python libraries such as Pandas, Numpy, ArcGIS, and Folium. They will also learn how to use the ArcGIS API for Javascript to create their first stand-alone web applications. This class builds on what students learned in GIS 4090\5090 and helps them develop knowledge and skills that they will use throughout their careers.

Course Objectives

• To teach students how to work with geogarphic web services.
• To introduce students to modern data science methods and tools that can be used to augment their research in geography and remote sensing.
• Encourage students to use web services, webmaps, and webapps for visualizing and contextualizing data.

Textbooks

Required

• Python Data Science Handbook. VanderPlas, Jake. 2016.
• Introducing ArcGIS API 4 for Javascript

Optional

• Mastering Geospatial Analysis with Python

Course Schedule

Week	Topics	Date
Week 1	Web Mapping with Leaflet and Github
Week 2	Creating GIS Applications with the ArcGIS Javascript API
Week 3	GIS Web Application and 3D Scenes
Week 4	Popups and Widgets in Webapps
Week 5	Jupyter Notebooks (IDE), Intro to the ArcGIS API for Python
Week 6	Github and Administering Your Organization
Week 7	ArcGIS API for Python: Publishing and Analysis
Week 8	ArcGIS API for Python: Data Science
Week 9	Intro to Pandas and ArcGIS Spatial DataFrames
Week 10	Numpy and Rasters & Working with Imagery Services
Week 11	Plotting with JS by @JWasilgeo: DataViz, D3, Charting
Week 12	IDEs, Jupyter Dashboards, Jupyter RISE
Week 13	Advanced Plotting with Matplotlib and Seaborn and Intro to Scikit-learn
Week 14	Review Final Projects and Multiprocessing

Assignments & Grading

Weight	Type
19%	Weekly Assignments
27%	Project 1 - Web Application Project
27%	Project 2 - ArcGIS Python API Project
27%	Project 3 - Data Science Notebook Project or Student Defined Project