Learn how to build webGIS applications with Python and Django

Welcome to the Smart Map In Python Tutorial Series. In this tutorial series 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 take 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. We will be powering our application with a PostgreSQL and PostGIS database. For the front-end, we'll use Bootstrap, JavaScript, Leaflet, and Ajax. On the server side, we'll be using Python and Django combined with the use of scientific libraries like pandas, for our data transformation and conversion operations. The operating system that we will be working on is Ubuntu Linux LTS. (course update: January 2024)
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Course curriculum

  1. 01
  2. 02

    • Project Source Code

    • Lesson 2: Installing PostgreSQL and PostGIS Part 1

    • Lesson 3: Installing PostgreSQL and PostGIS Part 2

    • Lesson 4: Creating the Spatial Database
  3. 03

    • Lesson 5: Creating a Python Virtual Environment

    • Lesson 6: Installing and Configuring GeoDjango

    • Lesson 7: Installing Visual Studio Code IDE

    • Lesson 8: Creating the Django Base Application

    • Lesson 9: Testing the Django Installation
  4. 04

    • Lesson 10: Adding the Spatial Database to our Django Backend

    • Lesson 11: Creating a Django Admin User

    • Lesson 12: Creating the Model

    • Lesson 13: Downloading the Sample Dataset

    • Lesson 14: Creating the GeoAdmin and Testing the Spatial Back-End
  5. 05

    • Lesson 15: Adding the Leaflet Config Code

    • Lesson 16: Adding the Static Files

    • Lesson 17: Creating the Layout Page Part 1

    • Lesson 18: Creating the Layout Page Part 2

    • Lesson 19: Creating the Index Page

    • Lesson 20: Creating the Index View

    • Lesson 21: Modifying and Styling the Index Page
  6. 06

    • Lesson 22: Creating the Dataset API Endpoints

    • Lesson 23: Displaying Data on the Map

    • Lesson 24: Creating the Legend

    • Lesson 25: Creating the Bar Chart Legend

    • Lesson 26: Creating the Bar Chart

What will you learn?

  • How to build a Spatial Database using PostgreSQL and PostGIS.

  • How to create charts with Chart.js.

  • How to build Web Maps with Leaflet.js.

  • How to build REST API Endpoints.

  • Some JavaScript programming.

  • How to build Web Applications using the Django MVC framework.

Any prerequisites?

  • Basic knowledge of GIS and Python

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Some more information

  • Certificates of Completion

    After you successfully finish the course, you can claim your Certificate of Completion with NO extra cost! You can add it to your CV, LinkedIn profile etc

  • Available at any time! Study at your best time

    We know hard it is to acquire new skills. All our courses are self paced.

  • Online and always accessible

    Even when you finish the course and you get your certificate, you will still have access to course contents! Every time an Instructor makes an update you will be notified and be able to watch it for FREE

About your Instructor

Data Engineer and business intelligence consultant with an academic background in Bsc computer science and around 5 years of experience in IT. 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. My career started as an embedded software engineer writing firmware for integrated microchips, then moved on as an ERDAS APOLLO developer at geo data design a hexagon geospatial partner. Am now a consultant 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 I use to accomplish client requirements range from Hadoop, Amazon S3, Python, Django, Apache Spark, MSBI, Microsoft Azure, SQL Server Data Tools, Talend and Elastic MapReduce.

Edwin Bomela

Data Engineer and business intelligence consultant

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