Subject Descriptions - Subject Information

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

Subject Information
Subject Code CSCI446
Subject Name Big Data Analytics
Credit Points 6
Pre-Requisites CSCI433 OR INFO433 OR CSCI435
Co-Requisites Nil
Restrictions None.
Equivalence None.
Assessment Labs or tutorials 10% Programming assignments 50% Final Exam 40%
General Subject No.
EFTSL (Non Weighted) 0.125
Non Weighted Student Contribution Amounts
Commonwealth Supported (HECS) Students Only
Pre-1997 Pre-2005 Post-2005 Post-2008 Post-2009 Post-2010
$ 1131  $ 1131  $ 1131  $ 1131  $ 1131  $ 1131 
Weighted Student Contribution Amounts
Commonwealth Supported (HECS) Students Only
1771-Bachelor of Laws (Honours) (Direct Entry)
1777-Bachelor of Laws (Direct Entry)
1827-Bachelor of International Studies - Bachelor of Laws
1845-Bachelor of Information Technology - Bachelor of Laws
1852-Bachelor of Business Information Systems - Bachelor of Laws
351-Bachelor of Laws (Honours)
760-Bachelor of Communication and Media Studies - Bachelor of Laws
770-Bachelor of Laws (Graduate Entry)
771-Bachelor of Arts - Bachelor of Laws
771H-Bachelor of Arts - Bachelor of Laws
772-Bachelor of Creative Arts - Bachelor of Laws
773-Bachelor of Commerce - Bachelor of Laws
774-Bachelor of Mathematics - Bachelor of Laws
775-Bachelor of Science - Bachelor of Laws
775H-Bachelor of Science - Bachelor of Laws
775M-Course information not Found
779-Bachelor of Engineering - Bachelor of Laws
858-Bachelor of Journalism - Bachelor of Laws
Work Experience No
Tutorial Enrolment Information Students should use the SMP OnLine Tutorial System (via SOLS) to enrol in Tutorial/laboratory groups for this subject. Once enrolments are open a link to the subject will appear in Tutorial Enrolments.

Subject Availability
Session Spring  (24-07-2017 to 16-11-2017)
Campus Wollongong
Delivery Method On Campus
Instance Name Class 1
Course Restrictions No restrictions
Contact Hours 2 hours lecture, 1 hour lab
Lecturer(s) and
Cons. times
Lei Wang
Coordinator(s) and
Cons. times
Lei Wang
Instance Comment  
Census Date 31-08-2017

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.

Textbook Information

Text book information is available via the UniShop website:

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