Spatial Analysis & Geospatial Data Science in Python
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 786 MB
Genre: eLearning Video | Duration: 24 lectures (1 hour, 32 mins) | Language: English
Learn how to process and visualize geospatial data and perform spatial analysis using Python
What you'll learn
The course introduces you to the most essential Geopython Libraries
Perform Spatial Data analysis with Python
Learn the essentials of Geopy,Descriptionly Library, the workhorse of Geospatial data science in Python.
Learn how to visualize Geospatial data in Python (static and interactive maps)
Learn how to pre-process geospatial data.
Perform Geocoding on Data
Requirements
No GIS knowledge is required. We will give breif theoretical explanation as well as its practical implementation
Description
Geospatial data science is a subset of data science that focuses on spatial data and its unique techniques. In this, we are going to perform spatial analysis and trying to find insights from spatial data. In this course, we lay the foundation for a career in Geospatial Data Science. You will get hands-on Geopy, Descriptionly etc.. the workhorse of Geospatial data science Python libraries.
The topics covered in this course widely touch on some of the most used spatial technique in Geospatial data science. We will be learning how to read spatial data , manipulate and process spatial data using Pandas , and perform some spatial operations. A large portion of the course deals with spatial Visuals like Choropleth, Geographical Scatter Description, Geographical Heatmap, Markers, Geographical HeatMap. Each video contains a summary of the topic and a walkthrough with code examples that will help you learn more effectively.
Who this course is for:
Students who want to become Data Scientist by show-case this Project on his/her Resume..
Students who like to take their first steps in the Geospatial data science career.
Python users who are interested in Spatial Data Science.
GIS users who are new to python and Jupyter notebooks for Geographic data analysis.
Who this course is for:
One who is curious about DataScience
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