Program Overview

Introduction to Data Science and Machine Learning

The unit ITEC203 - Introduction to Data Science and Machine Learning is designed to provide students with a comprehensive understanding of data science and machine learning concepts, techniques, and tools. 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.

Unit Rationale, Description, and Aim

This unit will cover fundamental concepts and theories of data science and machine learning with a 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, students will be able to demonstrate the following learning outcomes:

• Demonstrate comprehensive knowledge with data science libraries and tools for data processing and analysis
• Demonstrate data science and machine learning preparation skills, via key techniques learnt and the use of relevant tools
• Implement a data science and machine learning application with an appropriate choice of data science and machine learning techniques
• Explain the issue of machine bias and how it may affect the common good

Unit 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 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%.

Assessments

• Assessment Task 1: Practical Programming - The first assessment consists of practicing simple Python data science and machine learning libraries.
• 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.
• 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.

Learning and Teaching Strategy and Rationale

This unit is offered in two delivery modes—Attendance and Online—to support diverse learning needs and maximize access for isolated or marginalized groups.

Representative Texts and References

• Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly Media.
• Bruce, P., Bruce, A., & Gedeck, P. (2020). Practical statistics for data scientists: 50+ essential concepts using R and Python (2nd ed.). O'Reilly Media.
• Jean, H. (2020). Essential math for data science. O'Reilly Media.

Locations and Credit Points

• Location: North Sydney
• Credit Points: 10

Year

2026