Computer Science in Statistical Information Report (SIR): With Special Reference to Chhattisgarh

The Statistical Information Report (SIR) is an essential tool for the collection and analysis of socio-economic and demographic data required for effective governance, planning, and policy formulation. Traditional SIR systems were manual, time-consuming, and prone to error. The integration of Computer Science technologies — including digital data collection, database management, real-time analytics, Geographic Information Systems (GIS), and cybersecurity frameworks — has transformed SIR processes into efficient, transparent, and reliable systems. This paper explores the role of Computer Science in enhancing SIR systems with special reference to Chhattisgarh. Primary data were collected through interviews and surveys, and secondary data were acquired from official government publications and academic sources. Findings indicate that computerized SIR systems significantly improve data quality, accessibility, and administrative efficiency. The study concludes with recommendations for further technological integration and capacity building.

Statistical Information Report, Computer Science, Chhattisgarh, Data Analytics

1. Yadav, N., & Singh, V. B. (2012). E-Governance: Past, Present and Future in India. International Journal of Computer Applications, 53(7), 35–47. DOI: 10.5120/8437-2220 [Google Scholar] [Crossref]

2. Kumar, P., Dharminder Kumar, & Narendra Kumar. (2013). ICT in Local Self Governance: A Study of Rural India. International Journal of Computer Applications, 83(6), 31–36. DOI: 10.5120/14453-2714 [Google Scholar] [Crossref]

3. Kumar, P., Dharminder Kumar, & Narendra Kumar. (2014). E-Governance in India: Definitions, Challenges and Solutions. International Journal of Computer Applications, 101(16), 6–8. DOI: 10.5120/17769-7408 [Google Scholar] [Crossref]

4. Saluja, H., Asthana, P., Mishra, S., Kumar, S., & Hazela, B. (2018). Big Data in E-Governance Management. International Journal of Computer Sciences and Engineering, 6(12), 321–325 [Google Scholar] [Crossref]

5. Sharma, A. (2022). Promoting Greater Efficiency and Better Governance Through ICT Application. Indian Journal of Public Administration, 68(3), 426–435 [Google Scholar] [Crossref]

6. Nanda, S. (2022). India’s E-Governance Journey: Looking Through Common Service Centres. Indian Journal of Public Administration, 68(4), 599–609 [Google Scholar] [Crossref]

7. Gupta, B. M., & Dhawan, S. M. (2005). Computer Science Research in India: A Scientometric Analysis. DESIDOC Journal of Library & Information Technology, 25(1), 1–12 [Google Scholar] [Crossref]

8. Bala, M., & Verma, D. (2018). Governance to Good Governance Through E-Governance – A Critical Review. International Journal of Management, IT & Engineering, 8(10), 224–269 [Google Scholar] [Crossref]

9. Census of India. (2021). Census Tables and Population Data 2011. Office of the Registrar General & Census Commissioner, India Government of India. (2017). Sample Registration System (SRS) Statistical Report – India 2014. Office of the Registrar General & Census Commissioner Directorate of Economics & Statistics, Chhattisgarh. (2021). Chhattisgarh Statistical Abstract 2021–22. Government Publication Directorate of Economics & Statistics, Chhattisgarh. (2023). Chhattisgarh at a Glance – 2023. Government Publication Gupta, M. P. (2010). Tracking the Evolution of E-Governance in India. International Journal of Electronic Government Research, 6(1), 46–58 World Bank. (2022). Data-Driven Development Policies. The World Bank Group Organisation for Economic Co-operation and Development (OECD). (2020). [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet