Spatial Analysis and Geospatial Data Science With Python

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Abdishakur Awil Hassan

2:12:56

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1 - Introduction

1 - 11 Course Intro.mp4

02:30

2 - 1-Introduction.zip

2 - 12 Introduction.mp4

01:27

3 - 13 Jupyter Noteobok.mp4

05:35

4 - 14 Introduction to Python.mp4

06:31

5 - 15 Pandas Essentials Part 1.mp4

04:42

6 - 16 Pandas Essentials Part 2.mp4

04:59

2 - Introduction to Geopandas

7 - 2-Introduction-to-Geopandas.zip

7 - 21 Introduction.mp4

01:43

8 - 22 Reading Spatial Data.mp4

02:26

9 - 23 Read CSV File.mp4

04:24

10 - Read Subset data.mp4

03:31

11 - Geodataframe & Geoseries.mp4

02:56

12 - Coordinate Reference System CRS.mp4

04:40

13 - 27 Assignment.mp4

05:50

3 - Spatial Operations

14 - 3-Spatial-Operations.zip

14 - 31 Introduction.mp4

01:39

15 - 32 Spatial Join.mp4

05:40

16 - 33 Buffer Analysis.mp4

03:57

17 - 34 Overlay Analysis.mp4

03:47

18 - 35 Nearest Neighbourhood Analysis.mp4

05:35

19 - 36 Assignment.mp4

06:41

4 - Geospatial Data visualization Geopandas

20 - 4-Data-Vizualization.zip

20 - 41 Introduction.mp4

01:20

21 - 42 Geovisualization basics.mp4

04:14

22 - 43 Multi layer maps.mp4

01:53

23 - 44 Choropleth Map.mp4

03:15

24 - 45 Bubble Map.mp4

01:45

25 - 46 Assignment.mp4

03:38

5 - Interactive Geospatial Data visualization

26 - 5-Interactive-Visualization.zip

26 - 51 Introduction.mp4

01:42

27 - 52 Folium Part 1.mp4

03:43

28 - 53 Folium Part 2.mp4

04:10

29 - 54 IpyLeaflet.mp4

05:16

30 - 55 Plotly Express.mp4

04:09

31 - 56 Assignment.mp4

02:30

6 - Advanced Operations

32 - 6-miscellaneous.zip

32 - 61 Introduction.mp4

01:31

33 - 62 Geocoding.mp4

03:17

34 - 63 Reverse Geocoding.mp4

03:13

35 - 64 Retrieve Openstreetmap data.mp4

03:53

36 - 65 Tips to speed up Geospatial processing.mp4

04:54

Description

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.

Who is this for?

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.

What You Need to Know?

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

More details

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.
Who this course is for:
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.

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Abdishakur Hassan is a geographic information systems (GIS) expert and consultant with years of experience working with the United Nations. He holds an MSc in geoinformation science and earth observations. During his tenure as a GIS expert, Abdishakur has developed fully fledged GIS applications in the urban planning and land administration domains. He is interested in all things related to geospatial data science.

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