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 Exploration unit is a core elective in the full Master of Data Science to start in 2016, and core in the Graduate Diploma in Data Science (Monash Online) starting 2015.

February 2015: Admin: Updated Mode of offering to indicated this unit will be part of the Pearson online GradDip

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 required for the various teaching modes.

15/9/15 - Updated unit title and objectives to distinguish it from FIT5196 Data wrangling.

24/9/15 - Removed statement of knowledge of programming being required and changed it to FIT9131 or equivalent (to be consistent with other GDDS/MDS) units. Remove knowledge of Python and R being required, as they may not have done these in an pre-req units in the GDDS/MDS (or as entry requirement). As one of the objectives mentions Python, R and tools, the unit can be taught expecting the students to do a self-paced tutorial on Python and/or R early in the course if they have not used them previously.

24/9/15: Ann: Removed co-req of FIT5197, as the pre-req knowledge of statss is enough, and don't want to limit for Masters students (esp. those from MIT etc).

8/10/15: Kim Made it clear that students should have pre-req knowledge of R.

5/11/15: Removed reference to FIT5197 from prerequisite knowledge requirements.

30/11/15: Update to learning outcome 2., as per email discussion with CE, for compliance. Changed "demonstrate understanding of the role..." to "describe the role..." Also, numbered ULOs.

08/02/2018 - Admin: Updating workload requirements on behalf of the CE.

2/7/19 - updated learning outcomes to clarify that R is the main programming platform used

20/8/19 - updated to teach with workshop rather than lecture and use studio/lecture: clarified statistics requirements Effective Sem 1, 2020

28/11/2019 - add ETF5922 as a prohibition after discussion with CE (Kim Marriott) and Judy Sheard.

22/09/2020 - Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

10/06/2021 - Admin: Adding Reasons for Change - This unit has been evolving since it started in 2015. There are no existing masters units offering the same material as this unit. It is now run twice a year and attracts high enrolments. In order for it to become a MIT core elective to replace a unit that is not offered any more, the R programming requirement was updated to "recommended" over "required". This not only enables MIT students to be eligible, but reflects the growing number of students who are taking the unit from the new (in 2020) Advanced Data Science Pathway, who have been enrolling without prior R programming knowledge. We have updated the unit to include the Basics of R in Weeks 1-2 to account for this, however, prior experience is still recommended.

Updated delivery to 2 hour Workshop to reflect what is being timetabled. This 2 hour workshop is a mix of content and group activities.

Updated Labs to Tutorials as these have turned toward tutorial in style over the last few semesters, so it is just a request to update the terminology, in the handbook rather than a change in our delivery.

For Semester 2, 2021

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 FIT2079.

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

The unit covers a core area within Data Science, visualisation.

Upon successful completion of this course, students will be able to:

1. perform exploratory data analysis using a range of visualisation tools;
2. describe the role of data visualisation in data science and its limitations;
3. critically evaluate and interpret a data visualisation;
4. distinguish standard visualisations for qualitative, quantitative, temporal and spatial data;
5. choose an appropriate data visualisation;
6. implement static and interactive data visualisations using R and other tools.

029999

This unit introduces statistical and visualisation techniques for the exploratory analysis of data. It will cover the role of data visualisation in data science and its limitations. Visualisation of qualitative, quantitative, temporal and spatial data will be presented. What makes an effective data visualisation, interactive data visualisation, and creating data visualisations with R and other tools will also be presented.

Technological requirements

Access to computer facilities allowing programming in R, Python and D3. Monash Online: able to run the Respondus lockdown browser.

on-campus, Monash Online

Flipped classroom used in on-campus delivery with group activities

In-semester assessment: 100%

Monash Online

1. Quiz 1 - 20% - ULO: 1, 6
2. Project Proposal - 10% - ULO: 1, 2, 5
3. DataVis Design Exercise - 10% - ULOs: 3, 4, 5
4. Quiz 2 - 15% ULO: 1, 2, 3, 4, 5
5. Data Exploration and Visualisation Project - 45% - ULO: 1, 5, 6

On-campus

1. Programming Exercise 1: Tableau Public - 5% - ULO: 1, 6
2. Programming Exercise 2: R - 5% - ULO: 1, 6
3. Programming Exercise 3: D3 - 5% - ULO: 6
4. Data Exploration Project - 35% - ULO: 1
5. Visualisation Project - 40% - ULO: 5, 6
6. Online Quiz - 10% - ULO: 2, 3, 4, 5

Minimum total expected workload equals 144 hours per semester comprising:

1. Contact hours for on-campus students:
   • Two hour/week workshop.
   • Two hours/week tutorial.
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 9 hours per week of personal study (22 hours per week for Monash online students) for completing tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

Large studio lecture space for workshops

FIT

Guest lectures by academics in other faculties on the use of visual information in their own disciplines (Including staff from Science, Business and Economics and Arts).

Nil