Originally going to be an elective in Data Science courses but due to the uncertainty of numbers, it was shelved. Now with the healthy enrolment numbers, we would like to include the unit to complete the coverage of the MDS.

22/9/16: removed FIT9059 data structures from pre-reqs as GDDS students could not have this.

22/09/2016: removed reference to Level 2 units in prerequisites.

05/01/2017: Admin - adding RfC - updating unit content to include new programming environment and changing assessment for on-campus students in line with new University policy. Changes effective from TP4 and Semester 2, 2017.

27/3/2017 - Added reasons for change and semester of implementation on behalf of CE. The unit is predominantly programming based; including integration of various bigdata processing frameworks. As such it is not suited for a theoretical exam. Bigdata programming languages and frameworks are rapidly evolving, thus provisioning a per-configured lab, that can meet formal lab exam requirements, is at present infeasible. The change of assessment from 50% coursework + 50% exam to 100% coursework will thus ensure comprehensive assessment of the contents.

Semester of implementation: S2, 2017

06/04/2017 - Admin added reasons for change at the request of the CE. Information added on assessment strategies for ensuring the submissions being students' own work

23/07/2018 - Corrected pre-requisites. Effective Semester 2, 2018.

06/03/2019 - request to totally revamp the unit, due to the outdated contents

24/9/2019: Admin - adding 10 minutes reading time to the overall exam duration as per University requirements.

18/10/2019: Updating the prerequisites. As FIT9133 is no longer offered, adding FIT9136 for commencing students. Effective semester 1, 2020.

16/3/2020: Because we are merging FIT5148 and this unit (FIT5202), we need to change the unit objectives and synopsis

18/09/2020 - Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

Upon successful completion of this unit students should be able to:

1. identify and explain big data concepts and technologies;
2. write and interpret parallel database processing algorithms and methods;
3. apply common data analytics and machine learning algorithms in a big data environment;
4. use and evaluate streaming methods in big data processing;
5. use big data streaming technologies;

020399

This unit focuses on big data processing, including volume, complexity, and velocity using the latest big data technologies. In big data volume, it covers large volume data processing using parallel technologies. In large dimensionality (or complexity), it covers various data analytics methods for parallel processing. For the velocity, it covers data streaming processing.

Technological requirements

Students must have a working computer which can run large Docker (see https://www.docker.com/products/docker-desktop) containers effectively and efficiently.

On-campus, Monash Online

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

1. Assignment 1: Parallel data processing techniques with big data - 10% - ULO: ?
2. Assignment 2 Part A: Analysis and Evaluation of different Machine Learning Algorithms on big data 10% - ULO: ?
3. Assignment 2 Part B: Real-time stream processing on big data - 10% - ?ULO:
4. Flux Participation - 5% - ULO: ?
5. Lab Tasks - 5% - ULO: ?
6. Examination - 60% - ULO: ?

Monash Online In-semester assessment: 100%

1. Weekly Tasks - 10% - ULO: 1, 2, 4, 5
2. Big Data Group Project - 50% (Stage 1: 5%, Stage 2: 20%, Stage 3: 20, Stage 4: 5%) - ULO: 1, 2, 3, 4, 5
3. End-term Quiz - 40% - 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. Contact hours for Monash Online students:
   • Two hours/week online group sessions.
   • Online students generally do not attend lecture, tutorial and laboratory sessions, however should plan to spend equivalent time working through resources and participating in discussions.
3. Additional requirements:
   • A minimum of 8 hours per week of personal study (22 hours per week for Monash online students) for completing lab/tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

FIT

(FIT9131 or FIT9133 or FIT9136) and FIT9132