Unit rationale, description and aim
Data science is an inter-disciplinary area that employs scientific methods, algorithms, tools and systems for extracting insights, knowledge and value from data. Machine learning, as a core part of data science and data analytics, and a subfield of artificial intelligence, is the scientific study of algorithms and mathematical models that computer systems use to make decisions or predictions. Machine learning algorithms and models are widely used in human’s digital life such as email client, search engine, social media, virtual personal assistant and recommendation system, although machine bias is an important ethical concern of which many people are unaware. Python is one of the most popular programming languages with comprehensive libraries and tools for putting data science and machine learning into practice in an efficient manner.
This unit will cover fundamental concepts and theories of data science and machine learning with focus on their practical use and implementations. The issue of machine bias in machine learning and how it may have an adverse impact on the common good will be examined. The aim of the unit is to learn both theoretical and practical data science and machine learning techniques to build real-world data science and machine learning solutions.
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.
Demonstrate comprehensive knowledge with data scie...
Learning Outcome 01
Demonstrate data science and machine learning prep...
Learning Outcome 02
Implement a data science and machine learning appl...
Learning Outcome 03
Explain the issue of machine bias and how it may a...
Learning Outcome 04
Content
Topics will include:
- Overview of data science and its implementation life cycle and tools
- Recap of data processing concepts and techniques
- Exploratory data analysis in data science
- Machine learning (ML) introduction
- ML projects and basic linear algebra
- Basic matrix analysis, dimensional reduction, and SVD, PCA
- Basic classification and evaluation metrics
- Regression (linear, polynomial), overfitting and regularization
- Clustering: k-means and mixture of Gaussians
- Better evaluation with k-fold cross validation and finetune model with grid search
- Neural networks and deep learning
- Machine bias in the real world and its impact on the common good
Assessment strategy and rationale
A range of assessment procedures will be used to meet the unit learning outcomes and develop graduate attributes consistent with University assessment requirements. The first assessment item consists of simple data and machine learning practical tasks. The purpose is to assess students’ practical data science and machine learning skills of Python data science and machine learning libraries and tools. The second assessment is a more specific image data exploration and machine learning preparation task that requires fundamental knowledge of data science and machine learning. The purpose is to assess students’ online engagement and understanding and practical skills in data preparation for machine learning algorithms and models. The final assessment is to conduct experiments with one machine learning algorithm e.g. classification. The purpose is to assess students’ machine learning practical skills and techniques with consideration of machine bias, building on the machine learning preparation task.
The assessments for this unit are designed to demonstrate the achievement of each learning outcome. To pass this unit, students are required to obtain an overall mark of at least 50%.
Overview of assessments
Assessment Task 1: Practical programming The firs...
Assessment Task 1: Practical programming
The first assessment consists of practicing simple Python data science and machine learning libraries. The assessment requires students to demonstrate their understanding and use of Python data science and machine learning libraries and tools.
Submission Type: Individual
Assessment Method: coding tasks
Artefact: Code
30%
Assessment Task 2: Image data exploration The se...
Assessment Task 2: Image data exploration
The second assessment consists of tasks to do online forum participation and image data exploration which requires fundamental knowledge of data science and machine learning. The purpose is to assess students’ online engagement and understanding and practical skills in the process of data preparation for machine learning models.
Submission Type: Individual
Assessment Method: Coding tasks + Online Viva
30%
Assessment Task 3: Machine learning assignment T...
Assessment Task 3: Machine learning assignment
The final assessment is a machine learning assignment focusing on classification. The assessment builds on the data prepared by the previous assessment and conducts experiments with machine learning models with consideration of machine bias. Students will use GenAI tools to evaluate and document their code. Online viva will be used to validate the antiunity of students’ learning.
Submission Type: Individual
Assessment Method: coding tasks + presentation (5 minutes)+ Online Viva
40%
Learning and teaching strategy and rationale
This unit is offered in two delivery modes—Attendance and Online—to support diverse learning needs and maximise access for isolated or marginalised groups.
Attendance Mode
Students attend weekly face-to-face classes at designated locations and engage directly with lecturers to support achievement of learning outcomes. The unit requires preparation before workshops (typically around one hour) and at least one hour of consolidation afterwards. Online learning platforms provide additional preparatory and practice activities to reinforce learning.
Online Mode
The online mode enables students to explore core disciplinary knowledge through both synchronous and asynchronous learning. Weekly discussion activities and active learning tasks encourage the application of theoretical concepts in professional contexts. Engagement with peers supports constructive learning, and students receive regular, timely feedback throughout the semester.
Students should anticipate undertaking 150 hours of study for this unit, including class attendance, readings, online engagement and forum participation and assessment preparation.