Unit rationale, description and aim
Data is widely considered the world’s ‘new oil’, and data science is a powerful discipline that enables organisations to extract actionable insights from large volumes of data. This unit introduces students to the core concepts and practices of data science using Python — a widely adopted programming language known for its clarity, flexibility, and strong support for data analysis.
Students will first build a solid foundation in Python programming, including data types, data structures, control structures, functions, and file operations. They will then progress to using Python’s data science libraries — such as NumPy, Pandas, and Matplotlib — for tasks such as data wrangling, exploration, and visualisation. Real-world datasets from areas such as health, business, sustainability, and community services will be used to provide practical and inclusive context, ensuring representation of diverse perspectives and experiences.
The unit emphasises ethical data handling, reproducibility, and responsible analysis, while fostering critical thinking and hands-on experience. It also aligns with other units in the MINFTN program, providing foundational programming and data analysis skills that support more advanced study in areas such as machine learning, data mining, and business intelligence. The aim of this unit is to equip students with foundational knowledge and practical skills in Python and data science, enabling them to contribute to societal benefit and the common good.
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.
Apply Python and data science tools to analyse dat...
Learning Outcome 01
Apply Python libraries to collect, clean, and tran...
Learning Outcome 02
Conduct exploratory analysis and data-driven model...
Learning Outcome 03
Critically evaluate and reflect on ethical, social...
Learning Outcome 04
Content
Topics will include:
- Introduction to Data science
- Advanced Python & Reproducible Coding Practices
- Python Ecosystem for Data Science: Pipelines, Environments, and Git
- Data Science environment setup: Jupyter notebooks and Google Colab
- Python syntax, data types, data structures, control structures, functions, and file handling
- Introduction to object-oriented programming in Python
- Advanced Data Wrangling and Exploratory Data Analysis (EDA) with Pandas and Seaborn
- Data cleaning, transformation, and Outlier Detection with NumPy and Pandas
- Data processing on data aggregation and group operations
- Data visualisation with Matplotlib and Plotly
- Working with APIs, JSON, Databases and web scraping for data extraction
- Communicating Results: Dashboards, Reports and Storytelling
- Case study applications in health, business, and climate
- Ethics of Data Science: Bias, Transparency, Human Dignity
Assessment strategy and rationale
The unit uses a scaffolded assessment strategy to progressively build students’ skills and confidence in Python-based data science.
- Assessment 1 introduces foundational programming concepts through coding tasks focused on Python syntax and setup, helping students gain early confidence.
- Assessment 2 applies Python libraries (e.g., NumPy, Pandas) to real datasets for data preparation and exploration, reinforcing hands-on skills.
- Assessment 3 is data science project involving full-cycle data analysis and visualisation, integrating prior learning with a focus on ethical data practices.
Assessments are designed for both multimode and online students, ensuring consistent access to tools and resources. All tasks are aligned with authentic industry workflows to enhance graduate employability. Students must achieve at least 50% overall and meet all learning outcomes.
Use of Generative AI: Use of AI tools is permitted only where explicitly stated in the assessment guide, and any usage must be declared. Undisclosed AI use may breach academic integrity.
Overview of assessments
Assessment 1: Practical programming tasks The fi...
Assessment 1: Practical programming tasks
The first assessment item consists of tasks involving Python environment setup and solving Python programming and data science problems. The assessment involves a variety of skills-building task that sets the foundation for later assessments, and requires students to demonstrate their ability to understand and use fundamental Python programming and data science concepts and tools.
Submission Type: Individual
Assessment Method:
Computer Code + Presentation + Online Viva
Artefact: Code + Live/Recorded Presentation with Face overlay (5 minutes) + Online Viva
30%
Assessment 2: Data preparation tasks The second ...
Assessment 2: Data preparation tasks
The second assessment item is a practical task focused on data preparation and wrangling using key Python data science libraries. Building on the foundational programming skills developed in Assessment 1, this task increases the complexity of data handling and requires students to apply libraries such as NumPy and Pandas to collect, clean, and transform varied datasets. Students will also be expected to script reproducible workflows, reinforcing best practices in data science and demonstrating a deeper level of competence
Submission Type: Individual
Assessment Method:
Computer Code + Presentation + Online Viva
Artefact: Code + Live/Recorded Presentation with Face overlay (5 minutes) + Online Viva
30%
Assessment 3: Data processing and exploration pro...
Assessment 3: Data processing and exploration project
The final assessment is an individual data exploration project. It requires students to apply Python and data analysis skills to solve real-world problems with consideration of data ethics. Students will practice skills on data preparation, exploratory analysis and visualisation and improve their ability to gain insights from the data.
Submission Type: Individual
Assessment Method:
Computer Code + Presentation + Online Viva
Artefact: Code + Live/Recorded Presentation with Face overlay (8 minute
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.
Across both modes, students should expect to commit approximately 150 hours to the unit, including class activities, readings, online participation and assessment preparation.