Certified Geospatial Analyst Program (Level 1) – GIS, Remote Sensing & Python Automation

This industry-aligned training program equips you with essential GIS skills, covering everything from foundational concepts to advanced spatial analysis. Gain hands-on expertise to launch or accelerate your career in geospatial technologies.

After the Course you'll be familiar with

ArcGIS Pro & QGIS
Web GIS & Cloud GIS
PostgreSQL/PostGIS

Python for GIS (ArcPy, GeoPandas, PyQGIS)
Google Earth Engine (GEE)
Remote Sensing & Spatial Data Analysis

Both online and offline(Bangalore and Thiruvananthapuram) mode available with customized class timings

Doubt clearing sessions with personalized assistance

Trainers with 5+ years of hands-on experience in various aspects of Geospatial Technology

Duration- 3 Months (12 Weeks)

Why Choose This Program ???

DALL·E 2025-02-18 15.09.21 - A highly realistic and visually appealing vector-style illust

Industry-Relevant Curriculum

Covers Latest GIS Desktop Software's, Programming & Database

DALL·E 2025-02-18 15.11.53 - A realistic and engaging vector-style illustration showcasing

Practical & Project-Based Learning

Every module includes hands-on exercises, mini-projects, and a Capstone Project that mirrors real-world GIS applications.

Focus on High-Demand Domains

Environmental Monitoring (Climate Change, Disaster Mapping)

Urban Planning (Smart Cities, Infrastructure Development)

Agriculture (Crop Health, Precision Farming)

Exclusive Skill Development

Master GIS Automation, Remote Sensing Analysis, Spatial Data Science, and Cloud GIS to stay ahead of the competition.

Career & Placement Assistance

Get guidance on resume building, job referrals, and GIS industry networking.

Course Tools and Exercises

Model Builder

Automating NDVI using Python

Dynamic NDVI Analysis and Deforestation Monitoring Using Landsat 8 Satellite Imagery using GEE

Image Classification

Training Modules Overview with Application Integration

📌 Module 1: Geospatial Fundamentals (Weeks 1-2)
Learn the core concepts of GIS, coordinate systems, spatial queries, and data classification.

Key Topics:
✔ Introduction to GIS: History, evolution, and applications across industries(Urban Planning: Smart City Development)
✔ Coordinate Systems & Projections: UTM, WGS 84, distortion handling (Environmental Monitoring: Climate Data Standardization)
✔ Spatial Data Handling: Vector & Raster data, conversion techniques (Agriculture: Crop Classification via Satellite Imagery)
✔ Attribute Data & SQL Queries: Joining tables, performing queries in QGIS (Urban Planning: Land Parcel Analysis & Zoning)
✔ Data Digitization: Tracing features, topology rules(Agriculture: Field Boundary Digitization)

💡 Mini Project: Land Use Mapping– Analyze land use trends using real-world datasets.

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📌 Module 2: GIS Software Proficiency (Weeks 3-5)
Master ArcGIS Pro & QGIS for spatial data processing, analysis, and modeling.

Key Topics:
✔ Geoprocessing Tools: Buffer, Clip, Merge, Intersect, Dissolve (Urban Planning: Zoning Adjustments)
✔ Raster Processing & Elevation Analysis: Hillshade, Slope, Contour Extraction (Environmental Monitoring: Flood Hazard Assessment)
✔ Spatial Analysis & GIS Modeling: Multi-Criteria Decision Analysis (MCDA), Spatial Interpolation, Time-Series Analysis (Agriculture: Soil Nutrient Mapping)
✔ GIS Automation: Model Builder in ArcGIS, Processing Toolbox in QGIS (Agriculture: Crop Growth Prediction)
✔ 3D Mapping & Visualization: Elevation models, 3D city models (Urban Planning: City Infrastructure Modeling)

💡 Mini Project: Advanced Spatial Processing – Conduct a GIS analysis for urban or environmental planning.

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📌 Module 3: Python for Geospatial Analysis (Weeks 6-8)
Automate GIS workflows using Python and geospatial libraries.

Key Topics:
✔ Python for GIS: Data structures, scripting basics (General GIS Automation)
✔ Vector & Raster Data Processing: GeoPandas, Shapely, ArcPy for spatial operations (Urban Planning: Automated Zoning Updates)
✔ Web Mapping & APIs: Fetching real-time data, visualizing spatial data with Folium (Agriculture: Weather & Climate Data Integration)
✔ Spatial Statistics & Hotspot Analysis: Moran’s I, Getis-Ord Gi, Kernel Density Estimation (Crime Mapping: Detecting High-Crime Areas)
✔ AI & Machine Learning for GIS: Predictive spatial analysis (Agriculture: Crop Yield Prediction)

💡 Mini Project: Python GIS Automation – Develop automation scripts for real-world GIS problems.

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📌 Module 4: Web GIS & Cloud Platforms (Weeks 9-10)
Explore Web GIS, Google Earth Engine (GEE), and cloud-based geospatial analysis.

Key Topics:
✔ ArcGIS Online & QGIS Cloud: Publishing interactive web maps (Environmental Monitoring: Tracking Air Pollution)
✔ Google Earth Engine (GEE): Satellite data analysis, remote sensing techniques (Agriculture: NDVI-based Crop Health Monitoring)
✔ Cloud GIS Integration: Combining GEE, PostGIS, and Web GIS (Urban Planning: Urban Growth Analysis with Time-Series Data)
✔ Interactive GIS Dashboards: Building real-time data visualization dashboards (Disaster Management: Flood Risk Mapping)

💡 Mini Project: Develop a Web GIS Application – Create and deploy an interactive GIS dashboard.

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📌 Module 5: Spatial Databases with PostGIS (Weeks 11-12)
Work with spatial databases for large-scale GIS data management.

Key Topics:
✔ Introduction to PostgreSQL & PostGIS: Setting up spatial databases (Urban Planning: Managing City Zoning Data)
✔ Spatial Queries & Indexing: Performing advanced spatial analysis using SQL (Environmental Monitoring: Identifying Air Pollution Hotspots)
✔ PostGIS for Web GIS: Integrating databases with web-based GIS applications (Agriculture: Creating a Real-Time Farm Monitoring Dashboard)

💡 Mini Project: Build a GIS Database – Store and analyze real-world geospatial data in PostGIS.

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📌 Capstone Project (Weeks 13-14)
Apply your knowledge to a real-world GIS challenge in:

✔ Urban Planning – Optimize road networks, land parcel management.
✔ Environmental Monitoring – Climate change analysis, air pollution mapping.
✔ Agriculture – Precision farming, crop yield monitoring.

Deliverables:
✅ Geospatial Analysis Report
✅ GIS Maps & Visualizations
✅ Python Scripts / PostGIS Queries
✅ Web GIS Dashboard (if applicable)

💡 Final Presentation – Showcase your project to industry professionals and potential employers.

📌 MODULE 1: Geospatial Fundamentals (2 Weeks)

Week 1: GIS Basics & Data Handling:

Week 2: GIS Data Sources & Processing

📌 MODULE 2: GIS Software Proficiency (3 Weeks)

Week 3: ArcGIS Pro & QGIS - Data Processing

Week 4: Spatial Analysis & GIS Modeling

Week 5: Thematic Mapping & Advanced GIS Tools

📌 MODULE 3: Python for Geospatial Analysis (3 Weeks)

Libraries Used: Geopandas, Folium, Shapely, Pandas, Requests (for APIs)

Week 6: Python Basics & GIS Automation

Week 7: Geospatial Data Processing in Python

Week 8: Advanced Spatial Data Analysis

📌 MODULE 4: Web GIS & Cloud Platforms (2 Weeks)

Week 9: Web GIS Fundamentals & ArcGIS Online

Week 10: Google Earth Engine (GEE) & Cloud GIS

📌 MODULE 5: Spatial Databases with PostGIS (2 Weeks)

Week 11: Introduction to Spatial Databases & SQL Queries

Week 12: Advanced PostGIS & Web GIS Integration

📌 CAPSTONE PROJECT (2 Weeks)

Project Overview:

The capstone project serves as the culmination of the course, allowing students to apply the knowledge and skills acquired in GIS, Remote Sensing, Python Automation, Web GIS, and Spatial Databases to solve real-world problems in Environmental Monitoring, Urban Planning, or Agriculture.

Week 13: Project Planning & Data Acquisition

Week 14: Project Execution & Final Presentation

Capstone Deliverables:

Geospatial Analysis Report: Including methodology, tools used, and conclusions.
GIS Maps & Visualizations: High-quality thematic maps for the project.
Python Scripts/PostGIS Queries: If applicable, automation workflows must be submitted.
Web GIS Dashboard (if applicable): Fully deployed interactive map for visualization.

Evaluation Criteria:

Technical Execution (40%): GIS techniques, data accuracy, automation applied.
Innovation & Problem Solving (30%): Uniqueness and effectiveness of the approach.
Presentation & Documentation (20%): Clarity, professionalism, and visual storytelling.
Industry Relevance (10%): Alignment with real-world industry needs.

This Capstone Project will serve as a portfolio piece for students to showcase their GIS expertise to potential employers. 🚀 (Expanded with project details, dataset integration, and final reports)