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 Wrangling 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).

2/3/2015 - reworded pre-requisite for admin clarification.

28/7/2015 - Removed FIT5145 as a prerequisite as agreed at GDDS Steering Committee meeting 27/7/2015. Effective immediately.

07/08/2015 - Admin: submitted changes to synopsis made by CE. Also numbered learning outcomes.

10/08/2015 - Admin: update to format of Synopsis removing dot points and making it a paragraph as required for the handbook.

19/02/2016- Refined unit synopsis and objectives, heavy SQL material was removed because of inadequate teaching resources, for S2 and TP3

4/05/2016 - added justification for the 100% in-semester assessment.

31-Aug-2016- upgraded LOs and prerequisites according to the latest discussion on refreshing the unit

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

07/09/2016 - Admin: Update learning outcomes as per GPC 4/16 (Item 7.1).

09/01/2019- Lan: changed labs to tutorials

18/10/2019: Updating prerequisites to include FIT9136. Effective S1, 2020.

11/11/2019: FEC 5/19 approved prereq amendments entered 18/10/2019. Also approved was a prereq waiver clause for students studying C6007 Master of AI. Effective 2020.

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

This unit has no similar existing units. It is complementary to FIT5148 and FIT5149, and will be taught using Python as the programming environment.

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

1. Parse data in the required format
2. Assess the quality of data for problem identification
3. Resolve data quality issues ready for the data analysis process
4. Integrate data sources for data enrichment
5. Document the wrangling process for professional reporting
6. Write program scripts for data wrangling processes

020399

This unit introduces tools and techniques for data wrangling. It will cover the problems that prevent raw data from being effectively used in analysis and the data cleansing and pre-processing tasks that prepare it for analytics. These include, for example, the handling of bad and missing data, data integration and initial feature selection. It will also introduce text mining and web analytics. Python and the Pandas environment will be used for implementation.

Technological requirements)

Students will need Python 3 and Jupyter Notebook. Download both Python 3.7 and Jupyter Notebook from https://www.anaconda.com/distribution/

on-campus, Monash Online

In-semester assessment: 100%

1. Assessment 1: Parsing Raw Data and Text Preprocessing - 35% (part 1: 20%, part 2: 15%) - ULO: 1, 2, 3
2. Assessment 2: Data Cleansing and Integration - 35% - ULO: 2, 3, 4, 5, 6
3. Assessment 3: End-of-term Quiz - 30% - 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 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

FIT9133 or FIT9136 or equivalent; or entry into C6007.