Postgraduate Diploma in Geospatial Data Science

The Postgraduate Diploma in Geospatial Data Science (PGDipGDS) is a one-year programme that provides foundational knowledge and practical skills in GIS, remote sensing, geospatial analytics, data science, programming, databases, machine learning, and GeoAI. Through hands-on training, projects, and internship experience, students develop expertise in spatial data analysis, geospatial application development, and data-driven decision-making.

The programme prepares graduates for entry-level geospatial and data analytics careers, as well as further postgraduate study and research.

Highlights

  • Fundamentals of GIS and Spatial Analysis
  • Remote Sensing Basics and Digital Image Processing
  • LIDAR and Drone data processing
  • Google earth engine and R Programming
  • Web GIS
  • Introduction to Data Science
  • Python Programming
  • Database Management
  • Machine Learning, Deep Learning and GeoAI
  • Big Data and Geospatial Analytics

Course Curriculum

Sem 1
  1. Fundamentals of GIS and Spatial Analysis
  • Introduction to GIS
  • Coordinate Systems and Projections
  • Vector Data Models
  • Raster Data Models
  • GIS Data Collection Methods
  • Spatial analysis using vector data –Overlay Analysis, Hotspot Analysis
  • Geostatistical Analysis
  • 3D Analysis using DEM data
  • Hydrological Analysis
  • Remote Sensing Basics and Digital Image Processing
  • Principles of Remote Sensing
  • Satellite Platforms and Sensors
  • Image Interpretation and enhancement techniques
  • Digital Image Processing
  • Image Classification –Supervised,Unsupervised,Object Based
  • Accuracy assessment- User,Producer, Kappa Coefficient, AOC and ROC curve
  • Working with Synthetic Aperture Radar- Creation of Interferogram
  • Spectral Indices (NDVI, NDBI, NDWI)
  • LIDAR and Drone data processing
  • Introduction to LIDAR systems
  • LIDAR data processing, classification
  • DEM and contour generation from LIDAR data
  • Introduction to Drone Technology
  • Data Processing
  • Image Mosaic
  • DEM generation
  • Google earth engine and R Programming
  • Introduction to GEE platform
  • Image processing & visualization
  • Vegetation, water, snow & urban index generation Land Surface Temperature (LST) analysis
  • Digital Elevation Model (DEM)& terrain mapping
  • Land Use Land Cover (LULC) analysis
  • R Fundamentals & Spatial Data Basics
  • Vector Data Handling & Visualization
  • Raster Data Processing & Analysis
  • Advanced Mapping & Project Application
  • Web GIS
    • Web Mapping Concepts
    • Leaflet
    • GeoServer
    • OpenLayers

Web Map Creation

Sem 2
  1. Introduction to Data Science
  2. Data Science Fundamentals
  3. Data Analytics Process
  4. Types of Data
  5. Data Collection Techniques
  6. Statistical Concepts for Data Analysis
  7. Python Programming
  8. Python Basics
  9. Variables and Data Types
  10. Conditional Statements and Loops
  11. Functions and Modules
  12. File Handling
  13. Object-Oriented Programming
  14. NumPy
  15. Pandas
  16. Data Cleaning and Preprocessing
  17. Exploratory Data Analysis (EDA)
  18. Data Visualization using Matplotlib and Seaborn
  19. Database Management
  20. SQL Fundamentals
  21. PostgreSQL
  22. Database Design
  23. Queries and Joins
  24. Spatial Databases with PostGIS
  25. Machine Learning, Deep Learning and GeoAI
  26. Supervised Learning
  27. Unsupervised Learning
  28. Regression Techniques
  29. Classification Algorithms
  30. Clustering Methods
  31. Model Evaluation
  32. Introduction to AI
  33. Deep Learning Fundamentals
  34. Convolutional Neural Networks (CNN)
  35. Object Detection in Satellite Imagery
  36. Land Use/Land Cover Classification
  37. GeoAI Applications
  38. Big Data and Geospatial Analytics
  39. Big Data Concepts
  40. Hadoop and Spark Overview
  41. Geospatial Big Data

Fee

Rs. 55.,000/- (Pay In installments)