The Malaysian Qualifications Agency (MQA) represents a statutory body in Malaysia set up under the Malaysian Qualifications Act 2007 to accredit academic programmes provided by educational institutions which are based in Malaysia. Accreditation exercises carried out by the MQA applies to both the undergraduate and postgraduate programs which are offered at Monash University, Malaysia campus. In February 2019, MQA published an addendum on the computing program standard (Link to the addendum: http://www2.mqa.gov.my/QAD/garispanduan/2019/Addendum%20for%20Computing.pdf). In this addendum, MQA recommends for Big Data to be covered as a body of knowledge by institutions which offer a computer science degree specializing in data science. Given that Monash Malaysia intends to start the Bachelor of Computer Science in Data Science programme in March of year 2020, the University is required to propose a level 3 big data unit for undergraduates specializing in this field. As such and apart from the aforementioned aims, this unit proposal is also prepared to fulfil the requirements as set by the MQA.

30/01/2020: Admin - adding Reasons for Change - learning outcomes updated to differentiate from FIT5148. Implementation Semester 1, 2020.

23/09/2020 Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

The advanced computer science and data science fields of study have attracted significant attention and interest both in the industry and in academia. This interest is in part due to a growing global demand to automate various platforms and to generate new analytical insights from processing big data in real-time.

While there is a broad consensus that the advanced computer science and data science fields require skills in computer science, mathematics and statistics as well as information systems, competency in big data management and processing is not covered in the current undergraduate course map. As such, this unit is proposed with an aim to cultivate competency in big data management and processing for the computer science undergraduates at Monash University. This competency would enable the Faculty?s graduates to pursue a professional career in the field of big data.

On successful completion of this unit, students should be able to:

1. identify big data concepts and technologies;
2. write and interpret parallel database processing algorithms and methods;
3. use big data processing frameworks and technologies;
4. describe and compare NoSQL technologies;
5. use big data streaming technologies.

020399

Data engineering is about developing the software (and hardware) infrastructure to support data science. This unit introduces software tools and techniques for data engineering, but not hardware. It will cover an introduction to big data processing, covering volume, variety, and velocity; large volume data processing using parallel technologies; variety data formats, including unstructured and semi-structured data, using NoSQL databases; and velocity data processing, covering data streaming.

Recommended resources

On-campus

Lectures and/or tutorials or problem classes: This teaching and learning approach provides facilitated learning and practical exploration

Examination (2 hours and 10 minutes): 60%; In-semester assessment: 40%

1. Take home test: - 5% - ULO: 1, 2, 3
2. Class Test: - 10% ULO: 1, 2, 3
3. Document and stream data processing: - 20% ULO: 4, 5
4. FLUX Participation: - 5% ULO: 1, 2, 3, 4, 5
5. Final Exam: - 60% ULO: 1, 2, 3, 4, 5

Minimum total expected workload equals 144 hours per semester comprising:

1. Contact hours for on-campus students:
   • Two hours/week lectures
   • Two hours/week laboratories
2. Additional requirements (all students):
   • A minimum of 8 hours per week of personal study for completing lab/tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

FIT

(FIT2094 or FIT3171) and FIT2004