Geospatial APIs For Data Science Applications In Python

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

3.93 (33 reviews)
Udemy
platform
English
language
Data Science
category
instructor
227
students
5.5 hours
content
Oct 2021
last update
$84.99
regular price

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

Description

ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT OBTAINING AND WORKING WITH WITH FREE GEOSPATIAL DATA OBTAINED VIA APPLICATION PROGRAMMING INTERFACES (APIs) USING DATA SCIENCE TECHNIQUES.

  • Are you currently enrolled in any of my GIS and remote sensing related courses?

  • Or perhaps you have prior experiences in GIS or tools like R and QGIS?

  • You want to quickly analyse large amounts of geospatial data

  • Implement machine learning models on remote sensing data

  • You don't want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis?

  • You want to have access  to a multi-petabyte catalogue of satellite imagery and geospatial datasets with planetary-scale analysis capabilities

The next step for you is to gain proficiency in obtaining free geospatial datasets from a variety of sources, from Foursquare to Google Earth Engine via their Python-friendly APIs and analyse these using data science techniques

MY COURSE IS A HANDS-ON TRAINING WITH REAL REMOTE SENSING AND GIS DATA ANALYSIS WITH GOOGLE EARTH ENGINE- A planetary-scale platform for Earth science data & analysis; including implementing machine learning models on imagery data, powered by Google's cloud infrastructure. !

My course provides a foundation to carry out PRACTICAL, real-life remote sensing and GIS analysis tasks in this powerful cloud-supported platform. By taking this course, you are taking an important step forward in your GIS journey to become an expert in geospatial analysis.

Why Should You Take My Course?

I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real-life spatial geospatial data from different sources and producing publications for international peer-reviewed journals.

In this course, actual geospatial data obtained via Foursquare and GEE APIs will be used to give you hands-on experience of applying data science and machine learning techniques to these data to answer real-life questions such as identifying the best locations for a restaurant or changes in socio-economic dynamics of a territory.

This course will ensure you learn & put geospatial data analysis into practice today and increase your proficiency in using APIs for obtaining these data and deriving valuable insights from them.

This is a fairly comprehensive course, i.e. we will focus on learning the most essential and widely encountered data science techniques applied to geospatial data

In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!

ENROLL NOW :)

Screenshots

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Content

Welcome to the Course

What Is This Course About?
Data and Code
Python Installation
What Is Google CoLab?
Google Colabs and GPU
Google Colab Packages
Introduction To Basic Spatial Data Concepts

Introduction to Geospatial APIs (and Other Sources of GIS Data)

What Are APIs
Singapore MRT
Basic Geocoding
Geocode A Dataframe of Cities
Introduction To The Foursquare API
Get Started With the Foursquare API
Obtain Venues and Their Details Around a Particular Location
Visualise the Foursquare Venues
Retrieve Venues On the Basis of Lat Long Coordinates
Retrieve the Venues Corresponding To Mumbai's Neighbourhoods

Other Source of Geospatial Data

Access Open Street Data
Obtain World Bank Data

Introduction To Google Earth Engine (GE)

What is GEE?
Sign Up For GEE
Datasets Within GEE

Obtaining GEE Data Via API To Use With Python

Accessing GEE API Within Python
Introduction To Geemap
Start Exploring Feature Collections
Filter and Visualise Shapefiles
Identify the Biggest Country
Filter Based on Numerical Attributes
Grouping Feature Collections By Attributes
Create a GeoJSON Bounding Box
Clip Image To Shapefile Extent
Upload External Data On GEE

Working With GEE's Imagery Data

Access Image Collections Within Google Colab
See Images Side By Side
Topographic Computations
Clip Image Collection To Shapefile Extent
Improve Your Clipped Image
Time Series Visualization
What Are Multispectral Data?
Using Multispectral Data: Case of Tonle Sap
Flood Mapping
Why Do We Need Radar Data
Obtaining Sentinel-1 Data From GEE
Visualise Sentinel-1 Data
Obtain Time Series Landsat Data From GEE

Getting a Sense of Our Data

What Are Pandas?
Principles of Data Visualisation
Some Theoretical Principles Behind Data Visualisation
Visualise Time Series Geospatial Data With Pandas
Where Are Singapore's MRT Stations Located?
Let's Colour Code These Stations-Part 1
Let's Colour Code These Stations- Part 2

Machine Learning

What is Machine Learning (ML)?
Training Data
Unsupervised Learning:Theory
k-means
Clustering Landcovers in Cambodia-Part1
Clustering Landcovers in Cambodia-Part 2
Supervised Classification
Random Forest
Basic Supervised Classification With MODIS For Training Samples
How Good Are My Results?
Accuracy
Spectral Unmixing
Supervised Classification With Geolocations: Introduction (Part 1)
Supervised Classification: Geolocation Training Data
Classify The Image
Combine EO Data From Different Sensors-Problem
Supervised Classification: Sentinel-1 and Sentinel-2
Supervised Classification: Sentinel VIs
Visualise the Classification results

Reviews

David
July 13, 2022
The structure of the course should have been better elaborated, many times the instructor just read the code.
Rajesh
June 20, 2022
The course contains valuable information about the use of Geospatial APIs for data Science Applications.
Marks
June 10, 2022
A lot of topics and I learnt a lot about GEE in Python, Instructor is knowledgeable and extraordinary
Mozafar
May 13, 2022
Very useful course. I learnt a lot of new stuff in this course. However, some of the lecturers were duplicated and some of the skipped codes were not working
Raj
May 3, 2022
The course contains wealth of valuable information which has immense potential for practical applications. The instructor has total command over the subject.
Rashmi
April 6, 2022
The course is beyond my expectations and will definitely enhance my knowledge base as well as quality of my work.
Ramakrishna
February 21, 2022
The course contains useful and vital information regarding use of geospatial APIs for data science applications in Python. The instructor has deep knowledge of the subject and her delivery is engaging.
Sungi
January 13, 2022
Very useful course explaining the utility and importance of APIs for data science applications in Python. The course will certainly add value to my work.
Rony
December 19, 2021
The course contains useful information with immense possibilities for practical applications. The instructor is impressive in her presentation.
Amit
December 12, 2021
Geospatial APIs for data science applications in Python is a course well-suited for my requirements. It contains valuable information and will certainly enrich my knowledge of the subject.
Raveena
November 25, 2021
Data science applications with Python using APIs is a concept which has immense potential for practical applications. The course is most useful and relevant for my work. The instructor is superb in her teaching methodology and her knowledge of the subject is par excellent.

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4228472
udemy ID
8/7/2021
course created date
10/23/2021
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