● 3.5 hours of on-demand video ● Full lifetime access ● Access on mobile and Laptop ● By Edwin Bomela
Welcome to the Creating Smart Maps with Python and Leaflet Windows Version course. We'll be building a python GIS application from scratch using a variety of open source technologies. The purpose of this course and many more to follow, is to learn to create geospatial analytics and convert it into a functional application.
In our use case we will be working with residential water consumption data and we will be applying data processing techniques to extract transform and load the data into our spatial database. Once we have processed and cleaned the data, we will use it as a data source for building our GeoDjango Web Map Application.
Some skills that you can expect to derive after completing the course are the following:
● You will learn how to build a Spatial Database using Postgresql and PostGIS.
● You will learn how to create charts with Chart.js.
● You will learn to build Web Maps with Leaflet.js.
● You will learn how to build REST API Endpoints.
● You will learn how to build Web Applications using the Django MVC framework.
WHAT YOU WILL LEARN
● Be able to create a Full-stack web GIS application from scratch
● Build a Full-stack Django Application
● Geodjango Application Development for Windows
● PostGIS for Windows
WHO THIS COURSE IS FOR
● Beginner and advanced developers
● The curious mind
2. Setup and Installations
3. Building a Spatial Database using PostgreSQL and PostGIS
4. Building a GeoDjango Application
5. Writing the GeoDjango Back-end Code
6. Building the Front-End using Leaflet.js
7. Adding the Data
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.