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.

2026 10

Campus offering

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  • Term Mode
  • Semester 1Campus Attendance
  • Semester 2Campus Attendance
  • Term Mode
  • ACU Term 2Online Unscheduled

Prerequisites

Nil

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 and data science tools to analyse data for evidence-based decision-making.
Relevant Graduate Capabilities: GC1, GC10

Apply Python libraries to collect, clean, and tran...

Learning Outcome 02

Apply Python libraries to collect, clean, and transform data, and develop reproducible workflows.
Relevant Graduate Capabilities: GC2, GC10

Conduct exploratory analysis and data-driven model...

Learning Outcome 03

Conduct exploratory analysis and data-driven modelling on industry datasets to inform decision-making.
Relevant Graduate Capabilities: GC2, GC8

Critically evaluate ethical, social and profession...

Learning Outcome 04

Critically evaluate ethical, social and professional issues in Data Science, including their impact on human dignity.
Relevant Graduate Capabilities: GC6, GC7

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 low-stakes quizzes or 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 a capstone 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

Multimode

Assessment 1: Practical programming tasks The fir...

Assessment 1: Practical programming tasks

The first assessment item consists of tasks involving Python environment setup and solving simple 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: Content knowledge coding tasks

Artefact: Code

Weighting

30%

Learning Outcomes LO1, LO2
Graduate Capabilities GC1, GC2, GC10

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: Conceptual knowledge coding tasks

Artefact: Code

Weighting

30%

Learning Outcomes LO2
Graduate Capabilities GC2, GC8, GC10

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: Projects of applying skills

Artefact: Code and Report

Weighting

40%

Learning Outcomes LO3, LO4
Graduate Capabilities GC2, GC6, GC7

Online

Assessment 1: Programming tasks The first assessm...

Assessment 1: Programming tasks

The first assessment item consists of a series of tasks  including Python environment setup and solving Python programming and data science problems. The assessment requires students to demonstrate their ability to understand and use advanced Python programming and data science concepts and tools, including data types/structures, control flow, functions, and file input/output operations.

Submission Type: Individual

Assessment Method: Content knowledge coding tasks

Artefact: Code

Weighting

30%

Learning Outcomes LO1, LO2
Graduate Capabilities GC1, GC2, GC10

Assessment 2: Data preparation tasks The second a...

Assessment 2: Data preparation tasks

The second assessment item is a data preparation practical using key Python data science ecosystem libraries.

The assessment requires students to use libraries such as NumPy and Pandas, along with visualisation tools like Matplotlib and Seaborn to load, clean, transform and explore data.

Students will work with real-world datasets in CSV or JSON formats and apply best practices in exploratory data analysis, including handling missing values, detecting outliers, and generating descriptive statistics and visualisations.

Submission Type: Individual

Assessment Method: Conceptual knowledge coding tasks

Artefact: Code and Summary Report

Weighting

30%

Learning Outcomes LO2
Graduate Capabilities GC2, GC8, GC10

Assessment 3: Data exploration project The final...

Assessment 3: Data exploration project

The final assessment is an individual data exploration project. Students will complete a full data science workflow using real-world datasets in a relevant filed (e.g., healthcare, business, climate). accessed through APIs or web scraping. This includes data acquisition, cleaning, exploratory analysis, visualisation, reporting, and ethical reflection.

Submission Type: Individual

Assessment Method: Projects of applying skills

Artefact: Code and Report and Presentation (8-10 minutes)

Weighting

40%

Learning Outcomes LO3, LO4
Graduate Capabilities GC2, GC6, GC7, GC10

Learning and teaching strategy and rationale

This unit is delivered in multimode over a twelve-week semester or equivalent study period. It adopts a scaffolded and experiential learning approach designed to progressively build students’ understanding of data science concepts and develop their practical skills using Python. All core learning materials and assessments are accessible online, ensuring flexibility and continuity of learning beyond the classroom.

Students will participate in weekly two-hour workshops and fortnightly two-hour laboratory sessions. Workshops are structured around active learning, where students engage in guided problem-solving, peer discussion, and real-world case analysis to strengthen conceptual understanding. Lab sessions provide experiential, hands-on opportunities to apply programming techniques and data analysis methods to authentic datasets. This structure reflects best practices in computing and data science education, where repeated exposure to practical challenges supports skill acquisition and long-term retention. The blended format fosters collaboration, immediate feedback, and ongoing engagement—key factors for success in this discipline.

ACU Online

In the online mode, this unit uses a student-centred, active learning strategy tailored for flexible, remote learners. It integrates scaffolded content delivery with regular opportunities for engagement and self-directed learning. Students explore key concepts through interactive modules and are encouraged to participate in asynchronous weekly discussions that promote critical reflection and peer-to-peer learning.

Practical exercises, real-world case studies, and coding labs are embedded within the online environment to simulate workplace-relevant tasks. These activities emphasise authentic learning and provide opportunities for students to apply new skills to problems that mirror those encountered in industry. Regular and timely feedback—both automated and instructor-led—supports students’ progress and confidence. This pedagogical model aligns with the needs of diverse online learners, offering flexibility, interactivity, and strong support mechanisms to enhance both engagement and outcomes.

Representative texts and references

Representative texts and references

Wes McKinney, 2022. Python for Data Analysis, 3rd Edition O'Reilly Media, Inc.

Peckham, T. (2024). Practical Python Data Projects: Real-World Data Science Applications Using Pandas, Matplotlib, and Scikit-learn. Apress.

Joel Grus, 2019. Data Science from Scratch, 2nd Edition, O'Reilly Media, Inc.

Eric Matthes, 2019. Python Crash Course: A Hands-On, Project-Based Introduction to Programming, 2nd Edition No Starch Press.

Peter Bruce et al, 2020. Practical Statistics for Data Scientists, 2nd Edition O'Reilly Media, Inc.

Kubben et al (Eds), 2019. Fundamentals of Clinical Data Science, Springer – open access freely available from https://www.springer.com/gp/book/9783319997124

Celi et al (Eds), 2020. Leveraging Data Sciences for Global Health, Springer – open access freely available from https://link.springer.com/book/10.1007%2F978-3-030-47994-7

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