| Catalog Content |
What is Big Data; Big Data Features and Sources; Big Data Applications in Education; Tools and Techniques in Education: Accessing and recording data; Extraction, Cleaning and clarification; Integration, Aggregation and representation; Modeling and Analysis; Interpretation ; LMS and CMS data: Demographic data, User interaction data, Conditional data, Inferred student data; Machine Learning Models: Associative, Clustering, Classification , Association analysis; Analyzes on Sample Datasets. |
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