Year
2024Credit points
10Campus offering
Prerequisites
Nil
Teaching organisation
150 hours of focussed learning
Unit rationale, description and aim
The Python programming language is currently one of the world’s most popular programming languages. In the health sciences, Python is used by healthcare data analysts, clinical data analysts, epidemiologists, biostatisticians, data scientists and software engineers. This programming language has become an essential tool for addressing challenges in a variety of health fields, such as medical diagnostics, genomic sequencing, hospital management, biomarker detection, drug delivery and health informatics.
In this unit, students will learn to create increasingly complex algorithms that will develop their skills and knowledge of programming syntax, functions, data, and file management. In addition to this, students will also learn to utilise common library packages such as Numpy, Matplotlib and Pandas for describing, analysing, interpreting, and visualising data, and engage in machine learning activities that will require them to write code to solve problems in a variety of health-related contexts. This unit is an introduction to Python programming, and therefore while having prior experience with programming is beneficial, such familiarity is not required to engage with and succeed in this unit. This unit aims to help students understand and apply the fundamental concepts of the Python programming language.
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.
| Learning Outcome Number | Learning Outcome Description | Relevant Graduate Capabilities |
|---|---|---|
| LO1 | Demonstrate knowledge and understanding of fundamental programming concepts commonly used in health data science | GC1, GC7, GC8, GC10 |
| LO2 | Apply common data processing library packages and tools for describing, analysing, interpreting, and visualising data | GC1, GC2, GC7, GC8, GC10 |
| LO3 | Solve problems in a variety of health-related contexts using the Python programming language | GC1, GC2, GC7, GC8, GC10 |
| LO4 | Evaluate coding algorithms based on their quality, efficiency, and relevance | GC1, GC7, GC8, GC10 |
Content
Topics will include:
- Installing Python and Integrated Development Environments (IDEs)
- Library Packages: Numpy, Matplotlib and Pandas
- Variables and Data Types
- Data Structures
- Conditions and Loops
- Functions
- Classes and Objects
- Files and Exceptions
- Machine Learning
Learning and teaching strategy and rationale
Becoming a proficient programmer requires practice and application, and therefore this unit takes an active approach to learning. Interactive online workshop classes provide an opportunity for students to engage with and learn key programming concepts and to undertake activities that necessitate firm critical thinking and problem-solving skills. Online computer lab classes will permit students to practice coding programs through carefully paced modules and interactive exercises. Students will be supported in their learning via synchronous and asynchronous sessions, discussion forums and other resources made available to them through ACU’s Learning Management System (LMS).
Assessment strategy and rationale
Educational literature shows that assessment drives learning. For this reason, assessments in this unit are designed to be engaging and interactive to encourage the active learner. The first assessment task is designed to ensure students have a solid understanding of the weekly content taught in the interactive workshops. Students will then engage with a variety of test questions that will assess their knowledge and understanding of fundamental programming concepts. The second assessment task will assess the student’s ability to use library packages to manage and present data. The final assessment task will require students to plan and develop their own machine learning code to solve problems in health-related contexts.
Overview of assessments
| Brief Description of Kind and Purpose of Assessment Tasks | Weighting | Learning Outcomes |
|---|---|---|
Assessment Task 1: Coding Tasks Students are required to demonstrate ability to read/write Python syntax and develop simple coding algorithms to solve problems in a variety of health-related contexts. | 30% | LO1, LO2 |
Assessment Task 2: Data Analysis and Presentation Students are required to demonstrate proficiency in using common Python library packages such as Numpy, Matplotlib and Pandas to analyse and present data to an industry-related standard. | 30% | LO2, LO3, LO4 |
Assessment Task 3: Machine Learning Solutions Students are required to apply their understanding of machine learning and develop sound code to address common issues in health data science. | 40% | LO1, LO2, LO3, LO4 |
Representative texts and references
Downey, A., Loukides, M. K., Blanchette, M., Romano, R., & Demarest, R. (2012). Think Python (1st ed.). Sebastopol, CA: O’Reilly.
Kubben, P., Dumontier, M., & Dekker, A. (2019). Fundamentals of Clinical Data Science (1st ed.). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-99713-1
Lutz, M. (2013). Learning python: Powerful object-oriented programming (5th ed.). O'Reilly Media, Inc.
Matthes, E. (2019). Python crash course a hands-on, project-based introduction to programming (2nd ed.). San Francisco: No Starch Press.
McKinney, W. (2018). Python for data analysis : data wrangling with pandas, NumPy, and IPython (2nd ed.). Beijing: O’Reilly.
Shaw, Z. A. (2017). Learn python 3 the hard way: A very simple introduction to the terrifyingly beautiful world of computers and code. Addison-Wesley Professional.
Swaroop, C. H. (2013). A byte of python. Swaroop, C. H. https://open.umn.edu/opentextbooks/textbooks/581