Unit rationale, description and aim
Data is deemed as the world’s ‘new oil’ while data science is a new inter-disciplinary science of data that employs scientific methods, algorithms, tools and systems for uncovering insights, knowledge and value from massive data generated in different domains. Python, a general-purpose programming language, has gradually become the ‘engine’ of data and data science. In particular, many data scientists use Python because it provides a wealth of data science tools and libraries.
This unit will cover fundamental elements of Python programming language and its comprehensive use in the context of data science. This includes Python language basics, data structures, functions, files, tools, and various python data science libraries for data processing, analysis and visualisation. Data ethics and elementary statistics and probability in data science will also be introduced. The aim of the unit is for students to learn how Python can be used for building data science 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.
Articulate Python fundamental programming language...
Learning Outcome 01
Apply common Python data science libraries and too...
Learning Outcome 02
Experiment with Python data science libraries and ...
Learning Outcome 03
Examine data science ethical issues as they impact...
Learning Outcome 04
Content
Topics will include:
- Data science and Python introduction
- Data science environment setup:Jupyter notebooks
- Python and data science concepts and preliminaries
- Python ecosystem for data science
- Python language and tool basics
- Python data structures, functions, objects and files
- Python data science libraries NumPy and Pandas
- Python data plotting and visualisation library Matplotlib
- Data preparation with Python
- Data wrangling with Python
- Future Careers in IT
- Data analysis examples
- Data ethics and potential adverse impacts
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 consists of small to medium sized Python setup and programming tasks. The purpose is to assess students’ fundamental Python programming and data science skills for problem solving. The second assessment consists of data preparation tasks using key Python data science ecosystem/libraries. The purpose is to assess students’ use of Python data science libraries NumPy and Pandas and other related tools for collecting, cleaning and wrangling various types of data. The final assessment is a more comprehensive assignment involving data processing, analysis and visualisation. The purpose is to assess students’ Python programming and data science techniques from data processing to data visualisation on real-world datasets with consideration of data ethics. There are fortnightly lab sessions associated with the assessments including assessable lab participation/engagement.
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%.
Transition pedagogies support assessment success through staged tasks, in-class practice activities, early and ongoing feedback, discipline-specific skill development, and enhanced peer collaboration to promote progressive learning and student engagement throughout.
Overview of assessments
Assessment 1: Practical Programming tasks The fi...
Assessment 1: Practical Programming tasks
The first assessment item consists tasks of Python environment setup and solving basic Python programming and data science problems. The assessment requires students to demonstrate their understanding and use of fundamental Python programming and data science package skills.
Submission Type: Individual
Assessment Method: Practical Coding
30%
Assessment 2: Data preparation with NumPy and Pan...
Assessment 2: Data preparation with NumPy and Pandas
The second assessment item is a data preparation practical using key Python data science ecosystem/libraries. The assessment requires students to use libraries NumPy and Pandas and other related tools for collecting, cleaning and wrangling various types of data.
Submission Type: Individual
Assessment Method: Conceptual knowledge coding tasks
30%
Assessment 3: Data processing, analysis and visua...
Assessment 3: Data processing, analysis and visualisation assignment
The final assessment is a more comprehensive assignment involving data processing, analysis and visualisation. The assignment requires students to demonstrate Python data science techniques from data processing to data visualisation on real-world datasets with consideration of data ethics.
Submission Type: Individual
Assessment Method: Applying knowledge coding tasks
40%
Learning and teaching strategy and rationale
This unit is delivered in different modes using an integrated learning and teaching strategy designed to ensure equivalent learning outcomes and a comparable learning experience for all students, while supporting diverse learning needs and maximising access.
Learning activities are intentionally aligned to the unit learning outcomes and assessment tasks, and are underpinned by active learning, guided engagement with disciplinary knowledge, opportunities for peer interaction, and regular, timely feedback. While the mode of delivery shapes how students participate, the pedagogical intent, expectations and standards remain consistent.
In Attendance mode, students engage in weekly face-to-face Workshops, supported by preparatory activities and opportunities for consolidation following classes. Online learning platforms are used to complement synchronous teaching through additional resources and learning activities.
In Online mode, students engage with the same core content and learning outcomes through a combination of synchronous and asynchronous activities, including structured discussions and applied learning tasks that support learning in professional contexts.
Students should anticipate undertaking 150 hours of study for this unit, including class attendance, readings, online forum participation, independent study and assessment preparation.
To support students transitioning from the Diploma to second‑year undergraduate study, the unit uses targeted transition pedagogies that strengthen discipline foundations and essential study competencies through an active, engaging and supportive learning approach.