Geospatial APIs For Data Science Applications In Python

Data Science With Google Earth Engine (GEE) and Foursquare With Python Using Application Programming Interfaces (APIs)

4.58 (159 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Learn how to work with online Jupyter notebooks through

Gain robust grounding in working with geospatial APIs using Python

Apply data science methods on geospatial data

Deploy the Google Earth Engine (GEE) API within the Python ecosystem

Use GEE's datasets for visualisation and geospatial analysis

Course Gallery

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Comidoc Review

Our Verdict

This course aims to equip learners with data science techniques and geospatial API skills using Python. However, the pacing and unclear explanations of specific concepts might prove challenging for beginners. It is advisable that students have prior Python knowledge as they dive into this engaging yet at times inconsistent exploration of Google Earth Engine (GEE) and Foursquare APIs. Despite minor shortcomings, learners will appreciate the valuable, practical applications presented throughout the course.

What We Liked

The course provides a robust grounding in working with geospatial APIs using Python and covers data science methods on geospatial data.
It thoroughly explains the Google Earth Engine (GEE) API within the Python ecosystem and how to use GEE's datasets for visualization and geospatial analysis.
The course material boasts fascinating, clear explanations that are easy to follow, especially for those with a basic understanding of Python.
Learners appreciate engaging lessons on geographic visualization using Geopy and Folium packages.
Students find the introduction to Google Colab beneficial, enhancing their academic and professional map-making capabilities.

Potential Drawbacks

It appears that some students struggle with the course's rapid pace and unclear explanations of specific concepts or jargon.
There are reported inconsistencies in lecture continuity and several issues arise due to versioning and updates, causing difficulty following along.
A few learners noted missing output when code examples were presented, posing a challenge for those looking to replicate the results.
A number of students have expressed that certain machine learning sections felt incomplete or lacking detailed explanations.