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 Applied Data Analysis unit is core in the Master of Data Science (on-campus) starting 2016 and in the Graduate Diploma of Data Science (Monash Online) starting 2015, with this unit scheduled to start mid 2016 online.

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

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

19/02/2016: modified Prerequisites/PreReqUnits, FIT5197 is adequate, and because FIT5149 is only available one semester a year, we need to provide more entry paths. This for S2 and TP6.

18/04/2016: modified unit synopsis and learning outcomes. We have recently started developing this unit. The modification is according to our new discussion on what this unit should cover.

5/05/2016: added justification for 100% in-semester assessment

22/06/2016: Amended the unit synopsis, The synopsis shown in the system does not match the one updated on 18/04. Changed the prescribed reading material

09/09/2016: Removed FIT5145 from pre-reqs based on Lan Du's student experience.

02/02/2018: Admin - As advised by the MDataSci Course Director in consultation with the CE, adding ETC5252 to the prerequisites will streamline take-up of the unit by BusEco students. ETC5252 is not equivalent to FIT5197 so these students also require experience with programming in R.

23/04/2018: Amended the unit synopsis by removing the software not used in the unit, introduced the final exam for the on-campus version, starting from S2 2018

30/04/2018: changed the exam from 60% to 50% as suggested by GPC

09/01/2019: changed labs to tutorials. We have been running the session as tutorials. Slightly tweaked the synopsis. The change is subtle.

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

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

This unit will be a core elective unit for the Master of Data Science starting 2016 and the online Graduate Diploma of Data Science starting 2015. It is also a graduate variant of the undergraduate FIT3152.

There are no existing masters units offering the same material as this unit.

The unit covers a core area within Data Science.

Upon successful completion of this unit, it is expected that students will be able to:

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This unit aims to provide students with the necessary analytical and data modeling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyze them with appropriate machine learning and statistical algorithms. Those algorithms include regression, classification, clustering and so on. The unit focuses on understanding the analytical problems, machine learning models, and the basic modeling theory. Students will need to interpret the results and the suitability of the algorithms.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with Applications in R. Springer.

Zhao, Y (2012). R and Data Mining: Examples and Case Studies. Academic Press.

Technological requirements

Students will need R/Python, which is freely available software that can be downloaded.

On-campus and Monash Online.

Monash Online In-semester assessment: 100%

1. Regression analysis and Classification - 20% - ULO: 1, 2, 3
2. Data Analysis Challenge - 40% - ULO: 1, 3, 4, 5
3. Quiz (Modules 1-6) - 40% - ULO: 2, 3, 4

On-campus Examination (2 hours and 10 minutes): 50%, In-semester assessment: 50%

1. Mining Knowledge from Data (Individual assessment task) - 15% - ULO: 1, 3, 5
2. Data Analysis Challenge (Group assessment task) - 35% - ULO: 1, 2, 3, 4, 5
3. End of semester examination - 50% - ULO: 1, 2, 3, 4

Minimum total expected workload equals 144 hours per semester comprising:

1. Contact hours for on-campus students:
   • Two hours/week lectures.
   • Two hours/week tutorials.
2. Contact hours for Monash Online students:
   • Two hours/week online group sessions.
   • Online students generally do not attend the 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

FIT5197 or (ETC5252 plus experience with programming in R)