This course explores cutting-edge techniques in scalable data analytics, with a focus on algorithm design and data management strategies for complex and large-scale data types. Students will delve into the development and optimization of computation algorithms that support big data workloads, with special attention to spatial, spatio-temporal, multi-dimensional, and network data. Key topics include indexing structures, vector search methods, and learning-based approaches for data analysis. Through lectures, hands-on projects, and case studies, students will gain practical experience and a deep theoretical understanding of modern data systems, preparing them to tackle challenges in real-world, data-intensive environments.

CILO-1: Apply various query processing methods for big data processing, such as network data, spatial data, spatio-temporal, to analyse and design computer systems with deep theoretical understanding. CILO-2: Evaluate and analyse current trends and emerging data engineering techniques in cloud computing and HPC environments with deep theoretical understanding and practical experience. CILO-3: Design and implement programs for big data applications using advanced data structures, algorithms, indexing techniques, and distributed processing frameworks with practical experience.