MS4252 - Big Data Analytics | ||||||||||
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| * The offering term is subject to change without prior notice | ||||||||||
Course Aims | ||||||||||
This course delves into the essential concepts and techniques of big data analytics, social network analysis, and advanced data mining within the framework of business administration. Students will explore the issues and success factors in big data analytics, understanding both structured and unstructured data management, including tools like MapReduce and Hadoop. The curriculum covers information retrieval, web search, vector space and statistical language models, and singular value decomposition. Social network analysis topics include network measures, graph theory, centrality, prestige, and network propagation models. The course also examines recommendation systems, advanced data mining techniques such as Na?ˉve Bayes, Support Vector Machines, and Random Forests, and text mining methods like NLP, TF-IDF, and sentiment analysis. Students will gain hands-on experience with professional software packages (e.g., SAS/DIS, SAS/EM, Python, R), enhancing their ability to solve real-world business problems. Additionally, the course prepares students to excel in interpersonal communication and teamwork. | ||||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||||
Continuous Assessment: 60% | ||||||||||
Examination: 40% | ||||||||||
Examination Duration: 3 hours | ||||||||||
Examination The final examination will assess students' comprehensive professional knowledge and ability to apply big data techniques to solve business problems. This exam will provide evidence of studentsa?? understanding and application of all the concepts covered in the course (CILO 1, CILO 2, CILO 3, CILO 4). | ||||||||||
Detailed Course Information | ||||||||||
| MS4252.pdf | ||||||||||
