SIT731 - Data Wrangling
2024 unit information
|Trimester 1: Burwood (Melbourne), Online
Trimester 3: Burwood (Melbourne), Online
Students will on average spend 150 hours over the trimester undertaking the teaching, learning and assessment activities for this unit.
This will include educator guided online learning activities within the unit site.
|Scheduled learning activities - campus
1 x 3 hour seminar per week, weekly meetings.
|Scheduled learning activities - online
Online independent and collaborative learning including 1 x 2 hour online seminar per week, weekly meetings.
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.
To be eligible to obtain a pass in this unit, students must complete Pass Tasks 1 and 2 by the end of Week 3, complete the remaining Pass Tasks by the end of week 11, and must achieve a mark of at least 50% in the online quiz.
Unit Fee Information
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