SIT731 - Data Wrangling

Year:

2023 unit information

Enrolment modes: Trimester 1: Burwood (Melbourne), Cloud (online)
Trimester 3: Burwood (Melbourne), Cloud (online)
Credit point(s): 1
EFTSL value: 0.125
Prerequisite:

SIT774.
For students enrolled in S506, S507, S508, S535, S536, S538, S576, S677, S735, S737, S739, S770, S776, S778, S779: Nil

Corequisite:

Nil

Incompatible with:

SIT220

Study commitment

Students will on average spend 150 hours over the trimester undertaking the teaching, learning and assessment activities for this unit.

Scheduled learning activities - campus

1 x 3-hour active class per week

Scheduled learning activities - cloud (online)

Online independent and collaborative learning including optional scheduled activities as detailed in the unit site.

Content

Data Science (DS) and Artificial Intelligence (AI) are popular fields in making sense of data that have been collected in large quantities from various sources. Performing accurate exploration and modelling using DS and AI heavily rely on appropriately prepared data. Data wrangling is the process of preparing the raw data appropriately for modelling purposes. The aim of this unit is to learn various data wrangling methodologies and programming techniques to perform them. This include programming in Python for performing various data wrangling tasks, learning data extraction methods  from different sources, working with different types of data, storing and retrieving them, applying sampling techniques and inspecting them, cleaning them by identifying outliers/anomalies, handling missing data, transforming, selecting and extracting features, performing exploratory analysis, visualisation using various tools, summarising data appropriately, performing basic statistical analysis and modelling using basic machine learning. Further, techniques for maintaining data privacy and exercising ethics in data manipulation will be covered in this unit.

Hurdle requirement

To be eligible to obtain a pass in this unit, students must meet certain milestones as part of the portfolio, and must achieve a mark of at least 50% in the online quiz.

Unit Fee Information

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