Geospatial Analysis Using Python

 

Geospatial Analysis Using Python

[3 Months Training  + 2 Months Internship]

Master in essential Python libraries for geospatial analysis with Industry-Relevant Skills like, :

  • GeoPandas for vector data processing

  • Shapely for geometric operations

  • Fiona for reading and writing vector data

  • Rasterio for raster data processing

  • Folium for interactive mapping

  • Plotly for advanced data visualization

  • ArcPy for integration with ArcGIS

  • AI & ML in GIS



Course Link- https://www.agsrt.com/geospatial-analysis-using-python-geopandas-shapely-fiona-rasterio-folium-plotly-arcpy-agsrt

Course Content:

Month 1: Python Programming and Basics of Geospatial Data

Week 1: Introduction to Python Programming

  • Day 1-2: Setting Up the Environment​

  • Day 3-4: Basic Syntax and Data Types​

  • Day 5: Control Structures​

Assignment:

  • Write a Python script to perform basic arithmetic operations.

  • Create a program that uses loops and conditionals to solve a simple problem.

​

Week 2: Functions, Modules, and File I/O

  • Day 1-2: Writing Functions

  • Day 3: Python Modules

  • Day 4-5: File I/O

Assignment:

  • Create functions for common mathematical operations.

  • Write a program to read data from a file, process it, and write the output to another file.

​

Week 3: Data Structures, Collections, and Introduction to Pandas

  • Day 1-2: Lists and Tuples

  • Day 3-4: Dictionaries and Sets

  • Day 5: Introduction to Pandas

Assignment:

  • Implement a program using lists, tuples, and dictionaries to manage a collection of data.

  • Load a dataset using `pandas`, clean it, and perform basic analysis.

​

Month 2: Geospatial Data, Libraries, and ArcPy

Week 4: Introduction to Geospatial Data and Libraries

  • Day 1: Overview of Geospatial Data

  • Day 2-3: Introduction to Geospatial Libraries

  • Day 4-5: Reading and Writing Vector Data

Assignment:

  • Read a shapefile using `geopandas` and plot it.

  • Perform basic operations on a geodataframe (e.g., filtering, selecting columns).

​

Week 5: Working with Vector Data and Visualization

  • Day 1-2: Advanced Vector Data Operations

  • Day 3-4: Vector Data Visualization

  • Day 5: Practical Exercise

Assignment:

  • Perform a spatial join between two geodata frames.

  • Create a custom map plot with different styles and layers.

​

Week 6: Introduction to ArcPy

  • Day 1-2: Setting Up ArcPy

  • Day 3-4: Basic ArcPy Operations

  • Day 5: Spatial Analysis with ArcPy

Assignment:

  • Perform basic geoprocessing tasks with ArcPy.

  • Conduct a spatial analysis using ArcPy.

Month 3: Advanced Geospatial Analysis and Visualization

Week 7: Working with Raster Data

  • Day 1-2: Introduction to Raster Data

  • Day 3-4: Raster Data Processing

  • Day 5: Raster Data Visualization

Assignment:

  • Read a raster file using 'Rasterio' and perform basic analysis.

  • Visualize raster data using 'matplotlib' and create an interactive map with 'folium'.

​

Week 8: Advanced Spatial Analysis

  • Day 1-2: Spatial Clustering and Interpolation

  • Day 3-4: Surface Analysis

  • Day 5: Network Analysis

Assignment:

Conduct a clustering analysis using spatial data.

  • Perform interpolation on a raster dataset and analyze elevation data.

​

Week 9: Geospatial Visualization and Web Mapping

  • Day 1-2: Interactive Mapping with Folium

  • Day 3-4: Advanced Visualization with Plotly

  • Day 5: Practical Exercise

Assignment:

Create an interactive map with multiple layers using 'folium'

  • Build an interactive dashboard using 'plotly' for geospatial data.

​

Week 10: Time-Series Analysis and Machine Learning

  • Day 1-2: Time-Series Analysis

  • Day 3-4: Introduction to Machine Learning

  • Day 5: Machine Learning with Geospatial Data

Assignment:

Load and analyze time-series geospatial data.

  • Implement a basic machine learning model for geospatial data.

​

Week 11: Advanced Machine Learning for Geospatial Data

  • Day 1-2: Supervised Learning Techniques

  • Day 3-4: Unsupervised Learning Techniques

  • Day 5: Practical Exercise

Assignment:

Implement and evaluate a classification model for geospatial data.

  • Perform clustering analysis using unsupervised learning techniques.

​

Week 12: Final Project Preparation and Presentation

  • Day 1-2: Project Planning and Data Collection

  • Day 3-4: Analysis and Visualization

  • Day 5: Project Report and Presentation

Final Project:

  • Complete a comprehensive geospatial analysis project.

  • Present the project to the class, highlighting key findings and methodologies.

Internship

This 2 months internship will provide students with hands-on experience in real-world geospatial projects. Students will apply the skills and knowledge gained during the course to practical tasks, working on actual datasets and contributing to meaningful projects. Each intern will be assigned a mentor to guide them through the projects.


Prerequisites

  • Students and academics in geography, environmental science, and related fields.

  • Professionals in urban planning, environmental science, civil engineering, and data science.

  • GIS enthusiasts and hobbyists looking to deepen their skills.

  • Career changers aspiring to enter the GIS domain**.**

For More enquiry or for enrollment of the course click bellow to chat with us: https://wa.me/9337538414

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