Web Scraping and Mapping Dam Levels in Python and Leaflet

Web Scraping and Mapping Dam Levels in Python and Leaflet

Web Scraping and Mapping Dam Levels in Python and Leaflet

Regular price Rs. 1,250.00 Sale price Rs. 599.00 Save 52%
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4.5 hours on-demand video
1 article
3 downloadable resources
Full lifetime access
Access on mobile and TV
Certificate of completion

Welcome to Web Scraping and Mapping Dam Levels in Python. In this course you will be exposed to multiple technologies, and topics such as: Web Scraping ETL, Python Django Programming, Web Mapping, and Data Visualization. You will combine each of these topics and technologies to create an end-to-end GIS web application.

● You will learn how to apply web scraping to collect dam level data from a website.
● You will then build a model, that you will use to plot the dam level data on a map using leaflet.
● You will learn how to use the Django Template Engine instead of Ajax, to pass data from the back-end to the front-end.
● You will learn how to build a spatial database using PostgreSQL.
● You will also learn how to build a small dashboard that floats over your map, which will contain graphs that will visualize your model.


● Web Scraping
● ETL and Data Transformation
● Web Mapping with Leaflet
● Web GIS Programming
● Python and Django
● Data Visualization


● Python Developers at any level
● GIS Developers at any level
● Developers at any level
● The curious mind

1. Introduction
2. Building a Spatial Database
3. Creating a Django Python Application
4. Writing the Django Server-Side Code
5. Web Scraping and ETL
6. Building the Django Front-End
7. Data Visualization
8. Project Sources Code

Edwin Bomela is a Big Data Engineer and Consultant, involved in multiple projects ranging from Business Intelligence, Software Engineering, IoT and Big data analytics. Expertise are in building data processing pipelines in the Hadoop and Cloud ecosystems and software development. He is currently a consulting at one of the top business intelligence consultancies helping clients build data warehouses, data lakes, cloud data processing pipelines and machine learning pipelines. The technologies he uses to accomplish client requirements range from Hadoop, Amazon S3, Python, Django, Apache Spark, MSBI, Microsoft Azure, SQL Server Data Tools, Talend and Elastic MapReduce.

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