Birla Institute of Technology & Science, Pilani via Coursera
This comprehensive course explores the intersection of social media platforms and network science, providing students with essential skills for analysing digital social interactions. Beginning with graph theory fundamentals, students learn to model social media data as networks and apply mathematical frameworks to extract meaningful insights.
The curriculum progresses through advanced network analysis, centrality measures, and community detection algorithms. Students master key concepts, including degree centrality, betweenness analysis, PageRank algorithms, and information diffusion models. Practical applications focus on influencer identification, recommendation systems, viral marketing strategies, and community leader detection.
Advanced modules cover machine learning techniques for social media, including language analysis, fake news detection, and behavioural prediction. Students explore ethical considerations in social media research, privacy preservation, and responsible AI applications. The course emphasises hands-on implementation using NetworkX, real-world case studies, and industry-relevant projects.
By completion, students will be equipped to analyse social media networks professionally, develop recommendation algorithms, design viral marketing campaigns, and conduct ethical social media research. This course is ideal for data scientists, marketing professionals, researchers, and anyone seeking to understand the mathematical foundations of social media analytics.
In this module, the learners will be introduced to the course and its syllabus, setting the foundation for their learning journey. The course's introductory video will provide them with insights into the valuable skills and knowledge they can expect to gain throughout the duration of this course. A…
This foundational module introduces students to the intersection of social media platforms and network science. You will explore how social media ecosystems function as complex networks and master fundamental graph theory concepts essential for social media analytics. Key concepts include social me…
This module explores advanced graph types, including bipartite, weighted, temporal, and scale-free networks common in social media platforms. Students implement fundamental graph algorithms like DFS, BFS, and Dijkstra's algorithm for network exploration and shortest path analysis. The module covers…
This module focuses on measuring node importance and identifying influential users in social networks. Students master fundamental centrality measures including degree, betweenness, closeness, and PageRank algorithms to analyse user roles and network positions. The module covers local node properti…
This module examines methods for identifying and analysing groups within social networks. Students explore community detection approaches, including modularity-based methods, the Louvain algorithm, and spectral clustering techniques. The module covers overlapping communities, dynamic community evol…
This module studies how information and behaviours spread through social media networks. Students explore diffusion models, including independent cascade and linear threshold mechanisms, along with influence maximisation techniques. The module covers collective behaviours such as herd mentality, ec…
This module describes the design of recommender systems in the modelling of social media. The application domains for the recommendation models and systems are summarised. The Internet-scale algorithms using rule-based and parameter-based techniques are given. Further optimisation based on recent a…
This module provides the characterisation of big data generated on social media platforms. It provides an introduction to the adaptations of the data analytics tasks for processing big data. Complex graph analysis is explained in terms of dynamic networks formed on social media datasets. The corres…
This module introduces robust and privacy-aware algorithm design for social media systems operating under adversarial conditions. It covers polarization mitigation through network interventions and adversarial perturbations, misinformation detection, encryption and anonymization techniques, reinfor…
This module examines advanced content-based and personalised recommendation frameworks in social media ecosystems. It introduces language modelling, topic modelling, and novelty-detection filters for analysing multilingual and multimedia content. Knowledge representation through semantic web archit…
This module examines algorithmic approaches to detecting and countering malicious content and adversarial behaviour in digital ecosystems. It covers fake news characterization, misinformation propagation patterns, feature engineering, and graph-based detection models. Image forensics and deepfake g…
End-Term Examination
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Birla Institute of Technology and Science, Pilani is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (84/100).
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performa…
Instructor: Prof. Erik Demaine, Dr. Jason Ku, Prof. Justin Solomon View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY This course is an introduction to mathematical modeling of computational problems, as…
This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow;…
View the complete course: http://ocw.mit.edu/6-046JS15 Instructors: Erik Demaine, Srinivas Devadas, Nancy Ann Lynch 6.046 introduces students to the design of computer algorithms, as well as analysis of sophisticated algorithms. License: Creative Commons BY-NC-SA More information at http://ocw.mit.…
View the complete course: http://ocw.mit.edu/6-851S12 Instructor: Erik Demaine Videos of 6.851 Lectures Spring 2012 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Harvard University · edX
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
