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 Introduction to Data Science unit is being trialed as the starting unit for a full Master of Data Science to be proposed for start in 2016. By starting this unit in 2015, before the masters, we ease the burden of starting a whole new course, and also get to iron out the initial problems with the unit. It is a core unit in both Master of Data Science (on campus) and Graduate Diploma in Data Science (Monash Online).

July 2014: Add specified foundation units (FIT5131/9131 and FIT5132/9132) as prerequisites (after discussion with chief examiner).

16/9/2014: Added 'or equivalent' to prerequisite requirement, as approved at GPC4/14.

December 2014: CE modified assessment to 100% in semester.

February 2015: CE updated workload/contact hours to include both on-campus and online details. Admin added 'online' to Mode of offering.

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

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

4/3/2016: Amended pre-reqs. FIT9131+2 are overkill. Needed a less stringent pre-req to allow MDS students to do this concurrently with their foundation units. Install for S2 2016.

18/4/2016: Added in-semester assessment to reflect new decision about *not* needed identical assessment between online and on-campus. For S2 2016.

29/04/16: Modified pre-reqs and co-reqs based on discussion at GPC. Updated assessment entries likewise. 29/04/16: Corrected percentage split for assessment based on Ann's comments.

04/05/16: added wording on assessment for Margot

06/05/16: wording as per Judy Sheard

07/06/16: Mark Carman (lecturer, semester 2 2016) -- changed exam duration to 2 hours and percentage to 50% in order to better reflect time required and assessment importance. For S2 2016.

10/06/16: Mark Carman -- changed exam duration back to 3 hours for S2 2016 (to allow students more time to complete exam).

31/08/16: Wray Buntine -- modified pre-reqs and learning outcomes based on recent meetings

01/09/2016: Admin - numbered learning outcomes.

07/09/2016: Admin - updated learning outcomes as per GPC 4/16 (Item 7.1). Removed corequisites as they are now prerequisites.

22/03/17: Wray Buntine -- modification for new rules for assessment

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

21/06/17: Wray Buntine --changed on-campus percentages for better educational outcomes

29/05/18: Wray Buntine -- reading time standardised to 10 mins, requested to be done for S2 2018 as per faculty policy

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

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

29/10/2019: Updating the prerequisites; effective semester 1, 2020. Adding new foundation programming unit FIT9136.

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

10/02/2021: Guanliang Chen -- Changing the scheduled activities "Laboratories" to "Tutorials" given that these activities require tutors to perform quite some teaching activities instead of simply answering students' questions.

10/02/2021 - Admin: updating Workload as per above.

09/03/2021 - Lan Du: Removed FIT9132 from the pre-requisite list. The main reason: Based on the advice from the current CE and the past CE, the required database skills for FIT5145 are very lightweight, so FIT9132 can be removed. Moreover, the adjustment is also made to cope with the graduate certificate entry-level.

10/03/2021 - added ETC5510 to prohibitions list. FIT5145 is currently on the prohibitions list for ETC5510, but not the other way around. Discussed with Guanliang Chen (CE), Teresa Wang and Lan Du who all agree the units should be prohibited. Remove FIT3171 from prerequisites (FIT9132 has already been removed)

01/07/2021: Admin - adding Reasons for Change - Change in-semester assessment from 50% to 40%. Change scheduled final assessment from 50% to 60%

Assignment 4 is currently worth 20% of the unit, but it is overweighted because students often include their submissions for Assignment 2 as part of submission to Assignment 4. It is more practical to change it from 20% to 10% (in-semester assessment from 50% to 40%) and make the scheduled final assessment/exam 60%

This unit will be an elective unit in the existing masters courses offered through FIT in 2015. It is particular suited for MBIS and MIT, as it is not heavily analytic or coding oriented, and it is broadly targetting data science. In 2016, if the Master of Data Science goes ahead, it will be the starting core unit for the course.

There are no existing units offering the same material as this unit. Data Science, FIT3152, presents is more focused in presenting a range of analytic methods.

The unit plays the same role as "Introduction to Computer Science" does in the Bachelor of Computer Science. It lays the foundations on which all other courses can be built. It lets the student see the range of technologies and their typical use, before they embark on specialist units. It also covers many aspects of professional practice specific to data science such as legal and ethic issues.

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

1. Analyse the role of data in organisations, including curation and management issues
2. Apply basic tools for performing exploratory data analysis and visualisation
3. Apply basic tools for managing and processing big data
4. Apply basic predictive modeling and data analysis methods
5. Determine data storage and processing requirements for a data science project
6. Identify data resources and standards

020399

This unit looks at processes, case studies and simple tools to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed. Basic curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues.

Technological requirements

Access to a machine running Python with appropriate libraries. Jupyter or iPython workbooks may be required.

Python is available as a free download from https://www.python.org/downloads/ and Jupyter notebooks is free to download from https://jupyter.org/install

on-campus, Monash Online

Monash Online in-semester assessment: 100%.

1. Data science exercises - 20% - ULO: 2, 3
2. Business and data case study - 40% - ULO: 1, 2, 4, 5, 6
3. Video presentation - 10% - ULO: 1, 2, 3, 4, 5, 6
4. Quiz - 30% - ULO: 1, 2, 3, 4, 5, 6

On-campus examination (2 hours and 10 minutes): 50%; in-semester assessment: 50%.

Summer B:

1. Exploratory Data Analysis in Python - 20% - ULO: 2, 3, 4
2. Mid-semester in-class test - 10% - ULO: 1, 4, 5, 6
3. Critical Data Science Response - 20% - ULO: 2, 3
4. Examination - 50% - ULO: 1, 2, 3, 4, 5, 6

Semesters 1 & 2:

1. Data Science exercises I - 5% - ULO: 2, 3
2. Business and data case study: Plan - 5% - ULO: 1, 2, 3, 4, 5, 6
3. Data Science exercises II - 20% - ULO: 2, 3
4. Business and data case study: Report and Presentation - 10% - 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 tutorials.
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 tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

FIT

FIT9131 or FIT9133 or FIT9136

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