B.Sc. (Data Science) Social Media Analytics 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.
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Syllabus for Social Media Analytics
Module I
- 1. Introduction: Social Media Mining, Challenges for Mining 2. Network Measures: Centrality, Transitivity and Reciprocity, Balance and Status, Similarity 3. Network Models: Properties of Real-World Networks, Random graphs, Small- World Model, Preferential Attachment Model 4. Data Mining Essentials: Data, Data Processing, Data Mining Algorithms, Supervised Learning, Unsupervised Learning 5. Community Analysis: Community Detection, Community Evolution, Community Evaluation
Module II
- 6. Information Diffusion in Social Media: Herd Behaviour, Information Cascades, Diffusion of Innovations, Epidemics 7. Influence and Homophily: Measuring Assortativity, Influence, Distinguishing Influence and Homophily 8. Recommendation in Social Media: Challenges, Classical Recommendation Algorithms, Recommendation Using Social Context 9. Behaviour Analytics: Individual Behaviour, Collective Behaviour 10 Text Books and Reference Books: and 3. Liu, H., Abbasi, M. A., & Zafarani, R. (2014). Social media mining: An 11 introduction. Cambridge University Press. 4. Easley, D., & Kleinberg, J. (2017). Networks, crowds, and markets: Reasoning about a highly connected world (3rd ed.). Cambridge University Press. 5. Hansen, D., Shneiderman, B., Smith, M., & Himelboim, I. (2019). Analysing social media networks with NodeXL (2nd ed.). Morgan Kaufmann. 6. Choudhury, M. D., & De Choudhury, M. (2018). Social media analysis: Techniques and applications. Springer. 7. Barabási, A.-L. (2016). Network science. Cambridge University Press. 8. Aggarwal, C. C. (2016). Social network data analytics. Springer. 9. Feldman, R., & Sanger, J. (2007). The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press. 12 Internal Continuous Assessment: Semester End Examination: 60% 40% 13 Continuous Evaluation through: 30 marks Semester End Examination Class test of 1 of 15 marks Class test of 2 of 15 marks Average of the two: 15 marks Quizzes/ Presentations/ Assignments: 5 marks Total: 20 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 and Reference Books
- and 3. Liu, H., Abbasi, M. A., & Zafarani, R. (2014). Social media mining: An
- 11 introduction. Cambridge University Press.
- 4 Easley, D., & Kleinberg, J. (2017). Networks, crowds, and markets: Reasoning about a highly connected world (3rd ed.). Cambridge University Press.
- 5 Hansen, D., Shneiderman, B., Smith, M., & Himelboim, I. (2019). Analysing social media networks with NodeXL (2nd ed.). Morgan Kaufmann.
- 6 Choudhury, M. D., & De Choudhury, M. (2018). Social media analysis: Techniques and applications. Springer.
- 7 Barabási, A.-L. (2016). Network science. Cambridge University Press.
- 8 Aggarwal, C. C. (2016). Social network data analytics. Springer.
- 9 Feldman, R., & Sanger, J. (2007). The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press.
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.