Offered: Fall 2025 (current)
"Big data" describes the massive and complex datasets, whether structured or unstructured, that organizations encounter on a daily basis; however, the real worth is not in the quantity itself, but in the ways companies analyze and utilize this data to extract insights for improved decision-making and strategic business planning. This course presents the essential algorithms, frameworks, and programming models for big data analysis. The course content includes big data characteristics, big-data ecosystem, big-data mining, big-data programming models, big-data clustering, and dimensionality reduction on big data, big-data classification algorithms, big-data regression algorithms, mining data streams, big-data graph analytics, big-data query languages, and big-data recommendation systems.
The core objectives of this course are to:
Recognize big-data characteristics and describe both the theoretical and practical aspects of big-data management ecosystem
Analyze big-data learning problems and can understand and select appropriate large-scale machine learning and deep learning algorithms for big-data modelling
Become familiar with the big-data programming models and can apply those programming models to solve complex engineering problems associated with massive and complex datasets.
Get an exposure to advanced concepts of big-data mining, graph analytics, big-data query languages and recommender systems
Design and implementation of advanced big-data applications for real-life problem solving.
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