Spatial Analysis And Geospatial Data Science With Python

Learn how to process and visualize geospatial data and perform spatial analysis using Python.

What you'll learn
The course introduces you to the most essential Geopython Libraries
Learn how to visualize Geospatial data in Python (static and interactive maps)
Perform Spatial Data analysis with Python
Learn how to pre-process geospatial data.
Learn the essentials of Geopandas Library, the workhorse of Geospatial data science in Python.
Perform Geocoding and Reverse Geocoding using free Open source Solutions.
Unleash the power of Locational analytics in Data science.
Reinforce your knowledge with Geospatial data science Excercises and Projects.
Requirements
Basic Understanding of Python
No GIS knowledge is required. We will give breif theoretical explanation.
You’ll need to install Anaconda and GeoPython libraries. We will provide a Guide on installation and Jupyter Notebooks
Description
Geospatial data science is a subset of data science that focuses on spatial data and its unique techniques. It is beyond creating maps and merely focusing on where things happen but instead incorporates spatial analysis and insights derived from spatial data. In this course, we lay the foundation for a career in Geospatial Data Science. You will get introduced with Geopandas, the workhorse of Geospatial data science Python libraries.The topics covered in this course widely touch on some of the most used spatial technique in Geospatial data science. We will be learning how to read spatial data effectively, manipulate and process spatial data, and carry out spatial operations. A large portion of the course deals with spatial operations like Buffer analysis, Spatial joins and Nearest Neighbourhood analysis. Each video contains a brief overview of the topic and a walkthrough with code examples. We conclude each section Geospatial data science assignment and project, that will help you learn more effectively.We will also cover spatial data visualization using both Geopandasa and other interactive libraries like Folium, IpyLeaflet and Plotly Express. We cover how to make stunning Geo visualization for the most widely used map types.The final section covers some advance features including Geocoding, reverse geocoding, accessing OpenStreetMap data in Python and some advanced tips and tricks to process large Geospatial datasets.At the end of this course, you will be able to perform most of Geospatial data science operations in Python and also build a strong foundational knowledge in Geospatial Python.

Overview

Section 1: Introduction

Lecture 1 1.1 Course Intro

Lecture 2 1.2 Introduction

Lecture 3 1.3 - Jupyter Noteobok

Lecture 4 1.4 Introduction to Python

Lecture 5 1.5 Pandas Essentials - Part 1

Lecture 6 1.6 Pandas Essentials - Part 2

Section 2: Introduction to Geopandas

Lecture 7 2.1 Introduction

Lecture 8 2.2 - Reading Spatial Data

Lecture 9 2.3 - Read CSV File

Lecture 10 Read Subset data

Lecture 11 Geodataframe & Geoseries

Lecture 12 Coordinate Reference System (CRS)

Lecture 13 2.7 - Assignment

Section 3: Spatial Operations

Lecture 14 3.1 - Introduction

Lecture 15 3.2 - Spatial Join

Lecture 16 3.3 - Buffer Analysis

Lecture 17 3.4 - Overlay Analysis

Lecture 18 3.5 - Nearest Neighbourhood Analysis

Lecture 19 3.6 - Assignment

Section 4: Geospatial Data visualization (Geopandas)

Lecture 20 4.1 - Introduction

Lecture 21 4.2 Geovisualization basics

Lecture 22 4.3 Multi layer maps

Lecture 23 4.4 Choropleth Map

Lecture 24 4.5 Bubble Map

Lecture 25 4.6 Assignment

Section 5: Interactive Geospatial Data visualization

Lecture 26 5.1 - Introduction

Lecture 27 5.2 - Folium Part 1

Lecture 28 5.3 - Folium Part 2

Lecture 29 5.4 - IpyLeaflet

Lecture 30 5.5 - Plotly Express

Lecture 31 5.6 - Assignment

Section 6: Advanced Operations

Lecture 32 6.1 Introduction

Lecture 33 6.2 - Geocoding

Lecture 34 6.3 - Reverse Geocoding

Lecture 35 6.4 - Retrieve Openstreetmap data

Lecture 36 6.5 - Tips to speed up Geospatial processing

Students who want to learn Python for Geospatial Data Science.,Students who like to take their first steps in the Geospatial data science career.,Python users who are interested in Spatial Data Science.,GIS users who are new to python and Jupyter notebooks for Geographic data analysis.