COMP 7003 Big Data
Credit Points 10
Legacy Code 301046
Coordinator Yi Guo Opens in new window
Description "Big data" is the label for the ever-increasing gigantic amount of data with which humanity has to cope. The availability of data and the development of cloud computing architectures to process and analyse these data have made data analytics a central tool in our endeavours. This subject will introduce students to the realm of "big data", covering the important principles and technologies of retrieving, processing and managing massive real-world data sets. It is designed to provide the basic techniques required by any discipline that needs to make sense out of the growing amount of data, and to equip students with the knowledge and key set of skills set to be competitive in the growing job market in the analytics field.
School Computer, Data & Math Sciences
Discipline Computer Science
Student Contribution Band HECS Band 2 10cp
Check your fees via the Fees page.
Level Postgraduate Coursework Level 7 subject
Assumed Knowledge
It is expected that students enrolled in this subject should have basic programming skills in any programming language and working knowledge in elementary probability and statistics, including the concepts of random variables, basic probability distributions, expectations, mean and variance.
Learning Outcomes
On successful completion of this subject, students should be able to:
- Explain the major trends in technology, business, and science behind big data
- Analyse and compare a selection of major big data management techniques in use today, including parallel databases, NoSQL, MapReduce, cloud services
- Evaluate the relative strengths and weaknesses of MapReduce and parallel database systems and apply the appropriate technique to tackle relevant big data problems
- Apply proper methods of data pre-processing and cleaning for big data analysis
Subject Content
1. Foundations and recent trends of big data
2. Parallel database management systems
3. Data parallelism and the MapReduce framework
4. NoSQL databases and cloud services
5. Data processing and manipulation for big data analysis
Assessment
The following table summarises the standard assessment tasks for this subject. Please note this is a guide only. Assessment tasks are regularly updated, where there is a difference your Learning Guide takes precedence.
| Type | Length | Percent | Threshold | Individual/Group Task |
|---|---|---|---|---|
| Practical | 2 hours per Practical (10 in total at 2% each) | 20 | N | Individual |
| Interview | 10-15 minutes | 40 | N | Individual |
| Final Exam | 90 minutes open book exam including reading time | 40 | Y | Individual |
Teaching Periods
Autumn (2024)
Parramatta - Victoria Rd
On-site
Subject Contact Yi Guo Opens in new window