Data Science is a rapidly expanding field in industry and many leading universities in the USA and the UK are starting data science degrees and units. Monash FIT trialed a data science unit for the undergraduate business IT course, FIT3152, in 2013, which is continuing, mostly focusing on data analysis. This Data Engineering unit will start in 2016. It is a core elective unit in Master of Data Science (on-campus) and a core unit in Graduate Diploma in Data Science (Monash Online).

23/2/2015: Amended wording in following fields for administrative clarification: Reasons For Introduction; Teaching/Mode; Assessment/Summary; Workload/Contact Hours; Prerequisites/PreReq Units; Location Of Offering

27/2/2015: Amended Workload Requirements following discussion with Chair of GPC to clarify the workload required for the various teaching modes.

6/7/2015: Amended unit title from 'Data engineering' to 'Distributed and big data processing' as revised unit is based on FIT5043 Distributed and big data processing. As FIT5148 unit will replace FIT5043 from 2016, FIT5043 has been added as a prohibition to FIT5148.

2/11/15 - Amended pre-requisites to accommodate changes made to pre-2016 versions of the MNS & MIT course structures ie. replacing FIT5043 with FIT5148 for MIT and adding FIT5148 to MNS. Effective Semester 1, 2016.

4/11/15 - Amended Outcomes to remove 'data mining' as this is not the focus of this unit. Also FIT5043/5148 does not require students to understand data mining. With the learning outcome of "implementing data mining and machine learning solutions" would need to assume understanding of data mining and machine learning algorithm. In the MIT the unit that covers introduction to machine learning and data mining is FIT5047 and it is not compulsory. We can't put FIT5047 in the pre-requisite as it will cause limited choice for MIT students to study. As a reasonable substitute, we revised the outcome 6 as "demonstrate how to handle and analyse big data;"

4/11/15 - Amended Synopsis to match the changes to the outcomes. This involved removing the last bullet and revising the fourth bullet as "introduction to big data and its handling and processing;"

4/11/15 - Amended textbooks by adding the main book that first six lectures are based on it.

4/11/15 - Amended background knowledge by adding 'some programming background' because the first assignment requires programming skills, and removed operating systems knowledge as it is not required in any of the assignments or related to any lectures.

19/11/2015 - Amended Assessment - Added the validation details of the in-semester assessments

22/04/2016 - Amended the unit name - to follow a review of data science curriculum where the name was overlapping with another new unit name

22/04/2016 - Amended in the outcomes (minor change) - only few words were changed to better reflect the unit contents and also match the updated name of the unit

12/06/2017: Admin - updating location of offering to reflect actual campus offerings at the ADE's request.

28/11/2017: request to totally revamp the unit

13/06/2018: Admin - FEC 2/18 (Item 6.2) requested that the Textbooks be removed from the Monatar entry.

5/10/2018: Update to prerequisites to allow Software engineering students to enrol as an approved elective. Effective 2019.

12/12/2018: Amended the On-Campus Assessment weight from 50%-50% (Exam-InSemester) to 60%-40% (Exam-InSemester), to allow more structured assessment (exam) (Effective Semester 1/2019).

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

29/10/2019: Updating the prerequisites for 2020 intake. Adding FIT9136 to choice of programming foundation units.

21/09/2020 - Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

19/11/2020: Admin - this unit was disestablished at FEC 5/20 on 12/11/2020 as it is no longer required.

This unit has no similar existing units, though it will have some overlap with FIT5043, which is a modern take on databases included distributed databases and big data.

On successful completion of this unit a student should be able to:

• identify and assess big data concepts and technologies;
• write and interpret parallel database processing algorithms and methods;
• use big data processing frameworks and technologies;
• describe and compare NoSQL technologies;
• use and evaluate streaming methods in big data processing;
• 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:

• 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;
• velocity data processing, covering data streaming;

on-campus, Monash Online

Monash Online In-semester assessment: 100%

On-campus 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 (individual and group assessment task) - 20% - ULO: 4, 5, 6
4. FLUX Participation (online quizzes) - 5% - ULO: 1, 2, 3, 4, 5, 6
5. Examination - 60% - ULO: 1, 2, 3, 4, 5, 6

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)

For students enrolled in E3001, E3002,E3005, E3010, E3011, E3007 completing the Software Engineering specialisation: FIT2099 and FIT3171