IDTS4103 - Big Data Programming
Overview
This course provides comprehensive knowledge and practical skills in programming techniques for processing and analysing large-scale datasets within modern big data ecosystems. The course focuses on scalable software development approaches using distributed computing frameworks, parallel processing systems, cloud infrastructures, and real-time data pipelines.
Students will learn how to design and implement big data programming solutions using Python, Hadoop, Apache Spark, Spark SQL, MLlib, APIs, streaming technologies, and cloud-based platforms. The curriculum emphasizes efficient programming methodologies, scalable data pipelines, distributed storage systems, workflow automation, and optimization techniques for handling high-volume and high-velocity data.
In addition, the course integrates cybersecurity awareness, responsible data engineering practices, privacy considerations, governance frameworks, and sustainability principles relevant to digital banking and fintech industries. Students will explore how scalable data systems support intelligent decision-making, fraud detection, customer analytics, financial monitoring, and operational efficiency.
Through laboratory practice, collaborative projects, and real-world case studies, students will gain the ability to build, evaluate, optimize, and communicate scalable big data programming solutions that address practical industrial challenges.
Pre-requisites:
Learning Hours Composition:
- 42 hours face to face, consisting of 14 sessions of lecturing, tutorial or workshop.
- 28 hours of Structured Learning and Guided Group Discussions
- 65 hours of Independent, Project-Based Learning
Managing Faculty:
Credit Point:
3 Credit(s)
Learning Hours:
45 hours x 3 CPs = 135 hours
