Subject Descriptions - Subject Information

Calendar: 2017 Postgraduate
Faculty: Faculty of Engineering and Information Sciences
Department: School of Computing and Information Technology

Subject Information
Subject Code CSCI946
Subject Name Big Data Analytics
Credit Points 6
Pre-Requisites CSCI933 OR INFO933
Co-Requisites Nil
Restrictions None.
Equivalence None.
Assessment Labs or tutorials 10% Programming assignments 50% Final Exam 40%
General Subject No.

Subject Description
This subject covers the principles, techniques and applications of processing and analysing big data. The first part of this subject introduces the fundamental concepts, platforms, systems, and tools for big data analysis, to give students a basic understanding of this field. The second part introduces the main analytics algorithms that are used to uncover the underlying patterns and rules in big data; including classification, regression, clustering, recommendation and retrieval algorithms. The third part then focuses on case studies and the practical application of big data analytics to help students gain a better understanding of these algorithms. This subject will equip students with the fundamental knowledge on big data analytics and the skills to appropriately choose and apply algorithms to resolve practical analytics problems.

Subject Learning Outcomes
On successful completion of this subject, students will be able to:
1. Understand the basic concepts of big data analytics.
2. Correctly apply the related core algorithm(s).
3. Students are able to choose appropriate algorithms for, and design, and build systems to complete a specific big data analysis task.
4. Integrate the knowledge and skills learned in this subject o resolve practical issues.