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Home / Syllabus / B.Sc. (Data Science) / TY BSc Data Science / Semester 5 / Indian Knowledge Systems in Data Science

B.Sc. (Data Science) Indian Knowledge Systems in Data Science Syllabus - Mumbai University

This is the TY BSc Data Science syllabus under NEP 2020, in force from the academic year 2026-27. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.

Indian Knowledge Systems in Data Science Syllabus.pdf
Indian Knowledge Systems course · Semester 5 · TY BSc Data Science · 2 credits · 50 marks

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Syllabus for Indian Knowledge Systems in Data Science

Indian Knowledge Systems course · Semester 5 · TY BSc Data Science · 2 credits · 50 marks

Module I: Chapter 1: Foundations of Indian Knowledge Systems and Data Science

  • Concept and scope of Indian Knowledge Systems, Understanding the nature and interdisciplinary character of Indian Knowledge Systems and their integration of philosophy, mathematics, linguistics, medicine, and astronomy. Concept of knowledge, data, and observation in Indian traditions, Examining how observation, empirical recording, and interpretation of natural phenomena formed the basis of knowledge creation in ancient India. Sources of structured information in classical Indian literature, Studying sources such as Vedic literature, astronomical treatises, medical texts, and linguistic works that contain systematically organized knowledge. Relationship between Indian Knowledge Systems and modern data science Understanding how traditional methods of classification, inference, and prediction resemble modern data analysis and knowledge representation techniques. Relevance of traditional knowledge to contemporary data-driven research Exploring how insights from Indian knowledge traditions can contribute to modern data science methodologies and interdisciplinary studies. Chapter 2: Mathematical and Computational Foundations for Data Science in Ancient India Development of the decimal place-value number system, Understanding the origin and evolution of the decimal system and its importance in representing numerical data and performing complex calculations. Concept of zero and numerical computation, Examining the mathematical rules associated with zero and their role in enabling modern digital computation and data processing. Combinatorics and binary structures in Pingala’s Chandas Shastra, Studying how poetic meters were analyzed through combinatorial enumeration resembling binary number systems and recursive algorithms. Algorithmic procedures in classical Indian mathematics, Exploring computational methods used by mathematicians such as Aryabhata, Brahmagupta, and Bhaskara for solving equations and performing numerical calculations. Recursive and iterative computational techniques, Understanding mathematical procedures in ancient Indian texts that resemble modern algorithmic approaches used in data processing. Chapter 3: Data Classification and Knowledge Organization in Indian Traditions Ontological classification in Nyaya and Vaisheshika Studying structured categorization of objects, attributes, and relationships used to organize knowledge systematically. Taxonomy and classification in Ayurveda, Examining classification of diseases, medicinal plants, body constitutions (Doshas), and treatment systems in Ayurvedic texts. Hierarchical knowledge organization in classical Indian texts, Understanding layered knowledge structures used in philosophical, scientific, and linguistic works. Linguistic classification in Sanskrit grammar, Analyzing systematic classification of phonetics, morphology, and syntax in Panini’s grammatical framework. Relevance of traditional classification systems to modern data organization, Connecting ancient classification methods with modern data taxonomy, ontologies, and metadata systems used in data science.

Module II: Chapter 4: Pattern Recognition, Prediction, and Analytical Models in Indian

  • Knowledge Systems Pattern recognition in astronomical observations, Understanding how repeated observations of celestial movements enabled identification of patterns and periodic cycles. Predictive models in Indian astronomy and mathematics, Studying computational techniques used to predict eclipses, planetary motion, and calendrical calculations. Mathematical prediction techniques in the works of Aryabhata and Madhava, Exploring trigonometric tables, interpolation methods, and infinite series used for accurate prediction. Pattern recognition in linguistic and poetic structures, Examining structured patterns in Sanskrit phonetics, grammar, and poetic meters. Predictive reasoning in Indian philosophical traditions, Understanding inference-based prediction in Nyaya logic and philosophical reasoning systems. Chapter 5: Applications of Data Science in Studying Indian Knowledge Systems Text data and linguistic analytics in Sanskrit literature, Understanding how rule-based grammar and structured linguistic analysis support, computational text processing. Digital humanities and computational analysis of classical Exploring the use of digital tools to analyze large collections of manuscripts and traditional literature. Knowledge graphs and semantic representation of traditional knowledge, Understanding how semantic web technologies can represent relationships among concepts in Indian Knowledge Systems. Application of artificial intelligence and machine learning in IKS research, Using machine learning methods to analyze patterns in traditional knowledge sources and historical datasets. Ethical perspectives from Indian philosophy for responsible data science, Examining philosophical principles such as Dharma, balance with nature, and ethical responsibility in the development of modern data-driven technologies. 10 Text Books 1. The Wonder That Was India, Basham, A. L., Revised Ed., Picador / Rupa Publications, 2004. 2. A Critical Survey of Indian Philosophy, Sharma, C., Revised Ed., Motilal Banarsidass, 2016. 3. Classical Indian Philosophy, Mohanty, J. N., 1st Ed., Rowman & Littlefield, 2000. 11 Reference Books 1. Mahadevan, B., Bhat, Vinayak Rajat, and Nagendra Pavana R. Introduction to Indian Knowledge System: Concepts and Applications. PHI Learning, 2022. 2. Kapil Kapoor and Avadesh Kumar Singh. Indian Knowledge Systems. D.K. Printworld. 3. Kak, Subhash and Rao, T. R. N. Computing Science in Ancient India. Center for Advanced Computer Studies, University of Southwestern Louisiana, 1998. 4. Bhanu Murthy, T. S. A Modern Introduction to Ancient Indian Mathematics. New Age International, 1992. 5. Kim Plofker. Mathematics in India. Princeton University Press, 2009. 6. C. K. Raju. Cultural Foundations of Mathematics. Pearson Education. 7. Briggs, Rick. Knowledge Representation in Sanskrit and Artificial Intelligence. AI Magazine, 1985. 12 Internal Continuous Assessment: Semester End Examination: 60% 40% 13 Continuous Evaluation through: 30 marks Semester End Examination Case Study submission based on above topics = 05 Marks Class Assessment = 10 Marks Quiz / Assignment = 05 Marks 14 Format of Question Paper: (Semester End Examination: 30 Marks. Duration: 1 Hr ) Q1: Attempt any three (out of five/six) from Module 1 (15 Marks) Q2: Attempt any three (out of five/six) from Module 1 (15 Marks)

Text Books

  • 1 The Wonder That Was India, Basham, A. L., Revised Ed., Picador / Rupa Publications, 2004.
  • 2 A Critical Survey of Indian Philosophy, Sharma, C., Revised Ed., Motilal Banarsidass, 2016.
  • 3 Classical Indian Philosophy, Mohanty, J. N., 1st Ed., Rowman & Littlefield, 2000.
  • 1 Mahadevan, B., Bhat, Vinayak Rajat, and Nagendra Pavana R. Introduction to Indian Knowledge System: Concepts and Applications. PHI Learning, 2022.
  • 2 Kapil Kapoor and Avadesh Kumar Singh. Indian Knowledge Systems. D.K. Printworld.
  • 3 Kak, Subhash and Rao, T. R. N. Computing Science in Ancient India. Center for Advanced Computer Studies, University of Southwestern Louisiana, 1998.
  • 4 Bhanu Murthy, T. S. A Modern Introduction to Ancient Indian Mathematics. New Age International, 1992.
  • 5 Kim Plofker. Mathematics in India. Princeton University Press, 2009.
  • 6 C. K. Raju. Cultural Foundations of Mathematics. Pearson Education.
  • 7 Briggs, Rick. Knowledge Representation in Sanskrit and Artificial Intelligence. AI Magazine, 1985.

Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020, in force from the academic year 2026-27. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

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