Units / SCI1022
SCI1022 · Introduction to scientific coding
2026 Handbook6 credit pointsLevel 1School of Mathematics
Last checked: 23 Aug 2026 UTCOverview
This unit will teach the fundamental principles of the writing and development of scientific computing code through the completion of practical modules. You will initially complete a compulsory introductory module in which you will learn coding skills using Python. You will then complete a module in other languages such as R, MATLAB, Mathematica or Julia. All the modules will be taught in applied sessions using practical examples from various disciplines. All modules will cover fundamental control structures, common file systems, input and output of data, two- and three-dimensional data plotting and the interpretation of these plots.
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Clayton | First semester | Teaching activities are on-campus and in a block period (ON-BLK) |
| Clayton | Second semester | Teaching activities are on-campus and in a block period (ON-BLK) |
Assessment
The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | Homework assignment (Group and individual assignments) | Exercise | 50% | — |
| 2 | Pre-class test | Quiz / Test | 10% | — |
| 3 | In-class assignment (Weekly computer laboratory work) | Exercise | 40% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
The Handbook lists no prerequisite, corequisite or prohibition for this unit.
Learning outcomes
- Discuss the importance of data and simulations in science;
- Identify and describe common online and local computer file systems and file types for interacting with external systems;
- Demonstrate how basic control and data structures function in a range of programming languages;
- Access and navigate online repositories, download source and data files from repositories;
- Construct basic computer codes for data analysis and scientific modelling;
- Use a variety of plotting routines to manipulate and critically assess scientific data for mathematical, physical, environmental, chemical and biological applications;
- Communicate practical findings on data analysis and scientific modelling across a range of application's to different audiences.
Workload
• Four hours of seminars over one week; • Two 2-hour applied sessions per week (in weeks 2-12) and • Six hours of independent study
| Activity | Duration |
|---|---|
| Applied sessions | 48 hours |
| Seminars | 4 hours |
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