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AKTU Social Media Analytics and Data Analysis (BCAM061) Syllabus PDF – B.Tech 3rd Year
Social Media Analytics and Data Analysis BCAM061
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Official AKTU syllabus of Social Media Analytics and Data Analysis (BCAM061) for B.Tech 3rd Year – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Social Media Analytics and Data Analysis
Download PDFBCAM061 · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course, the student will be able to:
| CO 1 | Understand basic concepts and need of social media analysis |
| CO 2 | Understand the fundamental of graphs and matrices in social media analysis |
| CO 3 | Understand networking fundamentals of social media analysis |
| CO 4 | Understand social networking and modelling concepts and methods |
| CO 5 | Understand processing and visualizing social media data |
Course Content
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Introduction to Social Media Introduction to Social Media, Social Media Landscape, Social Media Analytics & its Need. SMA in Small and Large Organisations; Application of SMA in Different Social Media Platforms. Types of Social Networks, friend, user-generated content, affiliation, etc., sociograms, sociometric studies Basics of Social Media and Business Models, Basics of Web Search Engines and Digital Advertising, Application of SMA in different areas. | 08 |
| II | Graphs and Matrices The adjacency matrix, paths, connectivity, Distance and Breadth First Search, Network Datasets: An Overview Nodes, ties, and influencers, Making connections, Link analysis. Random graphs and network evolution. Weighted Networks, Hypergraphs | 08 |
| III | Network Fundamentals Network Structures, equivalence, homophile, clustering, snowball sampling, contact tracing and random walks, Ego-ceneterd networks, dominance hierarchies, Third Party Records, affiliation Network Citation Networks, Peer to Peer Networks, Recommender Networks, Biological Networks | 08 |
| IV | Social Network and Modelling Social Contexts: Affiliation and Identity, social capital, structural holes, structural balance, Predictive Modeling, Descriptive Modeling, community/ anomaly detection Facebook Analytics: Introduction, parameters, demographics, Analyzing page audience. Reach and Engagement analysis Google Analytics: Brief Implementation Technology, Limitations, Performance Concerns, Privacy Issues. Introduction and Working, Google Website Optimizer | 08 |
| V | Processing, Visualizing and Social Media Data Analytics Processing and Visualizing Data, Influence Maximization,Link Prediction, Collective Classification, Applications in Advertising and Game Analytics, collecting and visualizing social media data, visualization and exploration. Social Network and Web Data Analytics Methods, Clickstream analysis, A/B testing, online surveys, Web crawling and Indexing. Natural Language ProcessingTechniques for Micro-text Analysis, Trend, social influencers on judgements, opinion spread, judgement | 08 |
| Text and Reference Books: 1. Matthew Ganis, Avinash Kohirkar, Social Media Analytics: Techniques and Insights for Extracting Business Value Out of Social Media Pearson 2016 2. Jim Sterne, Social Media Metrics: How to Measure and Optimize Your Marketing Investment Wiley Latest edition 3. Brian Clifton, Advanced Web Metrics with Google Analytics, John Wiley & Sons; 3rd Edition edition (30 Mar 2012) 4. Ganis/Kohirka, SOCIAL MEDIA ANALYTICS Paperback – 29 September 2016 by Pearson. | ||
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