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The Spatial Data Science program at IIRS is structured over eight semesters, integrating foundational knowledge with specialized skills in geospatial analytics and data science. The curriculum is designed to provide a balanced blend of theoretical understanding and practical application, preparing students for careers in academia, industry, or research.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | MATH101 | Calculus and Linear Algebra | 4-0-0-4 | - |
| 1 | PHYS101 | Basic Physics | 3-0-0-3 | - |
| 1 | CS101 | Introduction to Programming | 2-0-2-4 | - |
| 1 | GEOS101 | Introduction to Geography | 3-0-0-3 | - |
| 1 | MATH102 | Probability and Statistics | 4-0-0-4 | MATH101 |
| 1 | LIT101 | English for Academic Purposes | 2-0-0-2 | - |
| 1 | LAB101 | Programming Lab | 0-0-2-2 | CS101 |
| 2 | MATH201 | Differential Equations | 3-0-0-3 | MATH101 |
| 2 | PHYS201 | Modern Physics | 3-0-0-3 | PHYS101 |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | GEOS201 | Physical Geography | 3-0-0-3 | GEOS101 |
| 2 | CS202 | Database Systems | 3-0-0-3 | CS101 |
| 2 | LAB201 | Database Lab | 0-0-2-2 | CS202 |
| 3 | MATH301 | Advanced Calculus | 4-0-0-4 | MATH102 |
| 3 | PHYS301 | Geophysics | 3-0-0-3 | PHYS201 |
| 3 | CS301 | Machine Learning Fundamentals | 3-0-0-3 | CS201, MATH102 |
| 3 | GEOS301 | Remote Sensing Principles | 3-0-0-3 | GEOS201 |
| 3 | CS302 | Web Development | 3-0-0-3 | CS201 |
| 3 | LAB301 | Machine Learning Lab | 0-0-2-2 | CS301 |
| 4 | MATH401 | Stochastic Processes | 3-0-0-3 | MATH301 |
| 4 | PHYS401 | Atmospheric Physics | 3-0-0-3 | PHYS301 |
| 4 | CS401 | Deep Learning | 3-0-0-3 | CS301 |
| 4 | GEOS401 | Satellite Image Processing | 3-0-0-3 | GEOS301 |
| 4 | CS402 | Mobile App Development | 3-0-0-3 | CS302 |
| 4 | LAB401 | Deep Learning Lab | 0-0-2-2 | CS401 |
| 5 | MATH501 | Advanced Statistics | 3-0-0-3 | MATH102 |
| 5 | PHYS501 | Oceanography | 3-0-0-3 | PHYS301 |
| 5 | CS501 | Data Mining | 3-0-0-3 | CS301 |
| 5 | GEOS501 | Spatial Data Modeling | 3-0-0-3 | GEOS401 |
| 5 | CS502 | Cybersecurity | 3-0-0-3 | CS201 |
| 5 | LAB501 | Data Mining Lab | 0-0-2-2 | CS501 |
| 6 | MATH601 | Time Series Analysis | 3-0-0-3 | MATH501 |
| 6 | PHYS601 | Climate Change | 3-0-0-3 | PHYS401 |
| 6 | CS601 | Cloud Computing | 3-0-0-3 | CS401 |
| 6 | GEOS601 | Urban Planning Using GIS | 3-0-0-3 | GEOS501 |
| 6 | CS602 | Artificial Intelligence | 3-0-0-3 | CS401 |
| 6 | LAB601 | Cloud Computing Lab | 0-0-2-2 | CS601 |
| 7 | MATH701 | Optimization Techniques | 3-0-0-3 | MATH401 |
| 7 | PHYS701 | Remote Sensing Applications | 3-0-0-3 | PHYS601 |
| 7 | CS701 | Research Methodology | 2-0-0-2 | - |
| 7 | GEOS701 | Spatial Econometrics | 3-0-0-3 | GEOS501 |
| 7 | CS702 | Big Data Analytics | 3-0-0-3 | CS601 |
| 7 | LAB701 | Research Lab | 0-0-2-2 | CS701 |
| 8 | MATH801 | Advanced Probability | 3-0-0-3 | MATH701 |
| 8 | PHYS801 | Environmental Monitoring | 3-0-0-3 | PHYS701 |
| 8 | CS801 | Capstone Project | 4-0-0-4 | CS702, GEOS701 |
| 8 | GEOS801 | Disaster Risk Management | 3-0-0-3 | GEOS601 |
| 8 | CS802 | Entrepreneurship in Tech | 2-0-0-2 | - |
| 8 | LAB801 | Capstone Project Lab | 0-0-4-4 | CS801 |
The program offers several advanced departmental electives that allow students to specialize in specific areas of spatial data science:
This course explores the application of machine learning techniques to satellite and aerial imagery analysis. Students learn to implement algorithms for object detection, classification, segmentation, and change detection using deep learning frameworks such as TensorFlow and PyTorch. The course emphasizes practical implementation through hands-on labs and real-world case studies.
This elective delves into the complexities of satellite image processing, including radiometric correction, atmospheric compensation, and geometric transformation. Students gain proficiency in handling multispectral, hyperspectral, and SAR data using industry-standard software like ENVI and ERDAS IMAGINE.
This course integrates geographic information systems with urban planning concepts to analyze land use patterns, population distribution, and infrastructure development. Students work on projects that involve creating spatial models for city planning, analyzing traffic flow, and designing sustainable urban environments.
Focusing on advanced techniques in spatial data analysis, this course covers geostatistics, spatial autocorrelation, and spatiotemporal modeling. Students learn to apply these methods using software packages like R, Python (GeoPandas), and specialized GIS tools.
This elective addresses the role of spatial data science in disaster preparedness and response. Topics include hazard mapping, vulnerability assessment, early warning systems, and post-disaster recovery planning using satellite imagery and geospatial tools.
This course focuses on monitoring environmental changes through satellite observations. Students learn to track deforestation, urban expansion, water quality, and climate indicators using various remote sensing techniques and data sources.
This advanced course combines economic theory with spatial data analysis to understand regional development patterns, market dynamics, and policy impacts. Students work with real datasets to model spatial relationships in economics and policy evaluation.
This course explores the challenges and opportunities in processing large volumes of geospatial data using big data technologies like Hadoop and Spark. Students learn to design scalable systems for spatial data storage, processing, and visualization.
Focusing on creating compelling visual narratives from complex spatial datasets, this course teaches students how to develop interactive maps, dashboards, and web-based applications using tools like D3.js, Leaflet, and Tableau.
This elective covers the use of geospatial technologies in national security and intelligence gathering. Students explore topics such as surveillance systems, threat analysis, and strategic spatial planning for defense purposes.
The program strongly emphasizes project-based learning to ensure students gain practical experience beyond theoretical knowledge. Projects are structured to simulate real-world scenarios and involve collaboration with industry partners or government agencies.
Mini-projects begin in the third year, where students work on smaller-scale challenges related to their area of interest. These projects are typically completed in teams and involve data collection, analysis, and presentation components.
The final-year capstone project is a comprehensive endeavor that integrates all learned concepts. Students select a topic relevant to current industry needs or research interests and conduct an extensive investigation under the guidance of faculty mentors.
Project selection involves a formal process where students submit proposals outlining their objectives, methodology, and expected outcomes. Faculty members review these proposals and assign mentors based on expertise alignment.
Evaluation criteria for projects include technical rigor, innovation, presentation quality, and contribution to existing knowledge or practical applications. Students are assessed through progress reports, peer reviews, and final presentations to ensure they meet the program's academic standards.
