Unit rationale, description and aim
To make data meaningful and informative, it must be transformed from raw and often messy formats into structured, reliable, and analysable forms.
This unit introduces the end-to-end process of data wrangling, which includes data discovery, cleaning, transformation, integration, and validation to prepare data for analysis and modelling. Students will also explore the fundamentals of machine learning, including how prepared data is used to build and evaluate simple predictive models. By combining data preparation with introductory modelling, students will learn to generate insights that support data-driven decision-making in real-world contexts across business and community sectors.
The aim of this unit is to equip students with the practical and conceptual skills to prepare, analyse, and model data for meaningful insights.
Campus offering
No unit offerings are currently available for this unit.Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
Explain the key stages and purpose of data wrangli...
Learning Outcome 01
Apply appropriate data wrangling and basic machine...
Learning Outcome 02
Implement data and process curation practices to e...
Learning Outcome 03
Apply and reflect on ethical and privacy principle...
Learning Outcome 04
Content
Topics will include:
- Principles of Data Wrangling
- Data Discovery and Loading
- Exploration and Descriptive Analysis
- Data Cleaning and Quality Assurance
- Data Transformation and Feature Preparation
- Data Integration and Visualization
- Introduction to Machine Learning Techniques
- Model Evaluation, Reporting and Ethics in Practice
Assessment strategy and rationale
Assessments are designed to develop and demonstrate students' ability to apply data wrangling concepts and techniques to real-world problems. Assessment 1 focuses on students' understanding of the stages of the data wrangling process and their impact on data quality, ethical handling and privacy. Assessment 2 provides an opportunity to implement these stages on authentic datasets using Python. Students will demonstrate data loading, cleaning, transformation, integration and visualization, and machine-learning model development and evaluation, and communicate their outcomes through a written report and a project presentation. This two-part structure scaffolds learning from conceptual understanding to practical application and ensures students achieve the required learning outcomes.
To pass the unit, students must demonstrate achievement of all learning outcome and obtain a minimum overall mark of 50%
Overview of assessments
Assessment Task 1: Computer Program and Written R...
Assessment Task 1: Computer Program and Written Report
Explain and critically analyse the stages of the data wrangling process, and their impact on data quality, reproducibility and ethical handling. Students will develop examples to illustrate each stage and support their discussion with evidence from academic and industry sources.
40%
Assessment Task 2: Applied Project Students will...
Assessment Task 2: Applied Project
Students will implement the stages of the dataset wrangling pipeline on a real-world dataset to extract, clean, transform, integrate and visualise data, and to build and evaluate the machine learning models. Students will present their project outcomes and findings through a scientific report and a scheduled online presentation with Q&A.
60%
Learning and teaching strategy and rationale
The teaching approach in this unit is designed to place students at the centre of their learning experience. Core concepts and foundational knowledge are delivered through asynchronous online materials, enabling students to engage flexibly with readings, media, activities, and interactive content in the LMS. These resources support students in building essential understanding at their own pace.
Learning is further supported through structured opportunities for applied practice, problem-solving, and optional peer interaction. These activities encourage students to extend their understanding, apply concepts to real-world scenarios, and develop practical skills relevant to computer science and data-focused disciplines.
This approach ensures that students can achieve the learning outcomes through the online materials alone, while still benefiting from optional engagement and opportunities to deepen their learning.