General information#
- A/Prof. Dan Andrews is your convener and lecturer.
- A second lecturer will be announced at a later date.
- Lectures twice a week: Monday, 11AM-13PM On-campus and Tuesday, 11AM-13PM Online live.
- The Monday lecture takes place at Llewellyn Hall.
- Method for joining the Tuesday live online lectures will be announced on Ed Discussions.
- Livestream lectures and recordings available on echo360 via Canvas.
- Lecture slides and other materials will be available in Ed Lessons.
Schedule#
Links to slides and code will be available on the lecturing dates on Ed Lessons.
| Week | Topics | Details |
|---|---|---|
| 1 | Introduction; Functional abstraction; Intro to AI & Large Language Models for programming; Data types | Intro to Python programming, including with LLMs and course administrative matters. Main Python datatypes and variables like integer, floating-point, string, bool |
| 2 | Functions; variables and expressions; Control flow: branching | Declaring and calling functions. Branching statement like if else and recursion. |
| 3 | Control flow: iteration; Sequence data type; Testing and debugging | Iteration statement like while and for. Sequence data types like string, list, tuple, the concept of slicing. Interpreting errors like syntax errors, runtime errors or semantic errors. Debugging and testing. |
| 4 | AI workflows for programming; Sequences Advanced | AI-assisted workflow for coding. Character encoding, string operations, iteration over sequence elements. More advanced topics like list comprehension, list of lists, objects by reference. |
| 5 | Code quality; Modules and Classes | Best practice about code quality like commenting, documenting, naming, organising code and code efficiency. Advanced features like namespace, scope, local vs global names, more recursions and classes. |
| 6 | Files; Dictionaries and sets | Concepts of Input/Output, files, directories, reading, writing text files. Simple data analysis like CSV file format, reading and storing tables. Dictionary (dict type), set type. |
| 7 | Numpy and Pandas | The concept of Numpy arrays, Numpy basics and vectorization. Pandas library for data analytics. |
| 8 | Problem decomposition and large programming example | Concept of breakdown of complex problems into smaller, manageable problems. Building small programs into larger programs to complete bigger tasks. |
| 9 | Computational complexity; Searching and sorting | Algorithm complexity, big-O notation, searching and sorting algorithms. Recursion vs. iterations. |
| 10 | Errors and exceptions; Command-line programs | Types of errors, raising and catching exceptions. Command-line programs and argument parsers. |
| 11 | AI impact in computational science | Special topics in computational science, AI and data science. |
| 12 | Exam revision | Final exam information, conditions and requirements, types of questions, Q&A session. |
- Monday Week 4 (17/08/2026) is the Mid-term test. TBA on Ed Discussions if this lecture will run.
- Monday Week 9 (5/10/2026) is Labor Day.
- Make up lecture details for week 4 and week 9 will be announced on Ed Discussions if applicable.