Geospatial Data Science with Python- GeoPandas

Geospatial Data Science with Python: GeoPandas, available at $59.99, has an average rating of 4.58, with 51 lectures, based on 424 reviews, and has 3158 subscribers.

You will learn about How to analyze geospatial data using the python data science ecosystem Using Jupyter notebooks to provide complete documentation of your workflow and interactive code examples The basics of the python data science ecosystem: NumPy, Matplotlib, Pandas, etc. Geospatial extensions to the Python data science ecosystem: Fiona, Shapely, GDAL, and most importantly GeoPandas Perform common vector analysis tasks with GeoPandas This course is ideal for individuals who are GIS analysts who want to increase their understanding of data science or Data scientists who want to increase their understanding of geospatial analysis It is particularly useful for GIS analysts who want to increase their understanding of data science or Data scientists who want to increase their understanding of geospatial analysis.

Enroll now: Geospatial Data Science with Python: GeoPandas

Summary

Title: Geospatial Data Science with Python: GeoPandas

Price: $59.99

Average Rating: 4.58

Number of Lectures: 51

Number of Published Lectures: 51

Number of Curriculum Items: 51

Number of Published Curriculum Objects: 51

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

How to analyze geospatial data using the python data science ecosystem

Using Jupyter notebooks to provide complete documentation of your workflow and interactive code examples

The basics of the python data science ecosystem: NumPy, Matplotlib, Pandas, etc.

Geospatial extensions to the Python data science ecosystem: Fiona, Shapely, GDAL, and most importantly GeoPandas

Perform common vector analysis tasks with GeoPandas

Who Should Attend

GIS analysts who want to increase their understanding of data science

Data scientists who want to increase their understanding of geospatial analysis

Target Audiences

GIS analysts who want to increase their understanding of data science

Data scientists who want to increase their understanding of geospatial analysis

Learn why the Geospatial Data Science tools are becoming so popular in the Geospatial sector.  The combination of Jupyter Notebooks with Python and GeoPanda’s allows you to analyze vector data quickly, repeatably, and with full documentation of every step along the way so your entire analysis can be repeated at the touch of a button in a notebook format that can be shared with colleagues.

If you ever get asked to explain your analysis, either for a scientific paper, to defend your results in a court, or simply to share what you’ve done with others so they can follow your steps than you will be glad that you conducted your analysis in Jupyter notebooks with GeoPanda’s rather than in a traditional desktop GIS system.

If you ever get frustrated with limitations in desktop GIS software, some of which is still 32 bit, single core software that uses decades old technology under the hood then you will appreciate the performance that can be achieved with this approach.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Differences between data science and GIS

Lecture 3: Advantages of the data science approach

Lecture 4: The python data science ecosystem for non-spatial data

Lecture 5: The python data science ecosystem for spatial data

Lecture 6: Introduction to Jupyter notebooks

Chapter 2: Installation and setup

Lecture 1: Installation overview

Lecture 2: Installation on MacOS

Lecture 3: Installation on Windows

Chapter 3: Getting started with GeoPandas

Lecture 1: Reading data into GeoPandas – Shapefiles

Lecture 2: Reading data into GeoPandas – other file types

Lecture 3: Reading data into GeoPandas – from PostGIS

Lecture 4: Advanced techniques for reading data into GeoPandas

Lecture 5: Reading non-spatial (tabular) data

Lecture 6: Reading data from an HTML table on the web

Lecture 7: Writing a GeoDataFrame to disk file or database

Lecture 8: Dataframes 101 – Part 1

Lecture 9: DataFrames 101 – Part 2

Lecture 10: DataFrames 101 – Part 3

Lecture 11: DataFrames 101 – part 4

Chapter 4: Spatial functions in GeoPandas

Lecture 1: Measurements

Lecture 2: Functions that create geometry

Lecture 3: The GeoPandas apply function

Lecture 4: The Geopandas map and replace functions

Lecture 5: Low level Intersection functions

Lecture 6: The overlay function

Lecture 7: More spatial functions for the toolkit

Chapter 5: Summarizing your data

Lecture 1: The groupby method

Lecture 2: The pivot_table method

Lecture 3: The apply method using more than one column

Chapter 6: Combining data from multiple dataframes

Lecture 1: Appending dataframes (Concatenation)

Lecture 2: Attribute joins (GeoPandas merge)

Lecture 3: Spatial joins

Chapter 7: Other GIS operations

Lecture 1: Dissolving geometries

Lecture 2: Clipping geometries

Lecture 3: Renaming columns

Lecture 4: Advanced geometry calculations

Lecture 5: The GeoPandas collect method

Lecture 6: Exploratory data analysis

Chapter 8: Extra content from Survey of Python for GIS appications

Lecture 1: What is a package

Lecture 2: Working with third party packages

Lecture 3: Python virtual environments – Part 1

Lecture 4: Python virtual environments – Part 2

Lecture 5: Intro to Jupyter noteboks

Lecture 6: Intro to NumPy

Lecture 7: Intro to Matplotlib

Lecture 8: Intro to Pandas

Lecture 9: Intro to GDAL and OGR

Lecture 10: Intro to Fiona and Shapely

Lecture 11: Intro to GeoPandas – 1

Lecture 12: Intro to GeoPandas – 2

Instructors

Michael Miller
GIS Programming

Rating Distribution

1 stars: 8 votes

2 stars: 12 votes

3 stars: 47 votes

4 stars: 144 votes

5 stars: 213 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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