SIT220 - Data Wrangling
Unit details
| Year | 2025 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Online Trimester 3: Burwood (Melbourne), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Unit Chair: | Trimester 1: Nayyar Zaidi Trimester 3: Kelvin Li |
| Prerequisite: | SIT103 |
| Corequisite: | SIT232 |
| Incompatible with: | SIT731 |
| Educator-facilitated (scheduled) learning activities - on-campus unit enrolment: | 1 x 3 hour seminar per week, weekly meetings. |
| Educator-facilitated (scheduled) learning activities - online unit enrolment: | Online independent and collaborative learning including 1 x 2 hour online seminar per week, weekly meetings. |
| Typical study commitment: | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. This will include educator guided online learning activities within 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 includes 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.
Learning outcomes
| ULO | These are the Unit Learning Outcomes (ULOs) for this unit. At the completion of this unit, successful students can: | Alignment to Deakin Graduate Learning Outcomes (GLOs) |
|---|---|---|
| ULO1 | Undertake data wrangling tasks by using appropriate programming and scripting languages to extract, clean, consolidate, and store data of different data types from a range of data sources. | GLO1 Discipline-specific knowledge and capabilities |
| ULO2 | Identify and apply data discovery and extraction methods and tools to handle extracting data based on project needs. | GLO3 Digital literacy |
| ULO3 | Design, implement, and explain the data model needed to achieve project goals, related security considerations, and the processes that can be used to convert data from data sources to both technical and non-technical audiences. | GLO1 Discipline-specific knowledge and capabilities |
| ULO4 | Use both statistical and machine learning techniques to perform exploratory analysis on data extracted, and communicate results to technical and non-technical audiences. | GLO1 Discipline-specific knowledge and capabilities |
| ULO5 | Apply and reflect on techniques for maintaining data privacy and exercising ethics in data handling. | GLO8 Global citizenship |
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Learning portfolio | Portfolio consisting of programming code, presentations, and reports | 100% | Week 12 |
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
Hurdle requirement
To be eligible to obtain a pass in this unit, students must meet certain milestones as part of the portfolio.
Learning resource
There is no prescribed text. Unit materials are provided via the unit site. This includes unit topic readings and references to further information.
The texts and reading list for SIT220 can be found via the University Library.
Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.
Unit Fee Information
Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study and their study discipline, and your study load.
Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current Students website.