Difficulty:

Year Completed: Semester 1, 2026

Prerequisite: ETC1010

Master Elective for B6060 (Bachelor of Actuarial Science / Master of Actuarial Studies)

SETU Survey Results (Satisfaction Levels)

Very High: 54.29%

High: 31.43%

Medium: 5.71%

Low: 8.57%

Subject Content:

Lecture(s) and Tutorial(s):

Textbook(s):

Assessments:

ETC5512's focuses on handling open data; building the basis through highlight definitions, licensing and quality standards, before working through application in case studies with different data collection and access methods.

This includes web scraping, APIs, and working with various formats such as CSV. The case studies included topics such as NSW disaster data, the Australian census, Australian election data, and furthermore. Data ethics and privacy are also covered throughout the subject.

1 x 2 hour lecture

1 x 1 hour tutorial

Workshops build on and extend ideas introduced in the lecture, where the format varies week to week. Sometimes live coding demonstrations, sometimes open Q&A, sometimes supplementary topic coverage. Tutorials heavily cover coding and how to approach questions that may or may not be included in the assignments. Each week has their own website with different cases to look at. Workshops are recorded; tutorials are not.

N/A

100% assignment-based. 4 assignments, each worth 25%. Assignment 1 covers weeks 1–3 (due week 4), Assignment 2 covers weeks 4–6 (due week 7), Assignment 3 covers weeks 7–9 (due week 10), and Assignment 4 is cumulative across weeks 1–12 (due in the exam block). They're all individual assessments.

General Overview:

Lectures:

Tutorials:

Assessments/Other Assessments

Exam

Concluding Remarks

It was interesting applying data cleaning and exploration skills to scenarios that reflect real-world applications, such as working with the Australian census data. However, it's less about theory and more about actually doing data analysis with real, messy data. You will find it interesting if you like hands-on coding work.

Lectures weren't essential to watch to do well in the subject overall, but some provided useful base knowledge depending on the context; for example, explaining how voting works and how to identify relevant factors for data analysis. It's the kind of unit where you could probably manage by working through the recordings and materials at your own pace rather than needing to attend live. Mostly, lectures were a mix of theory and example-based content.

Tutorials were useful for getting hands-on help with the coding side of things, especially when applying concepts to your own project data. Attendance wasn't strictly compulsory, but showing up (or at least working through the tutorial exercises) helped reinforce the material and troubleshoot issues in real time rather than getting stuck on your own.

Assessments were fine overall, and marking was fairly lenient. The assignments were reasonable to complete, especially since we had open access to AI while working on them; as long as it was declared and used as a learning tool rather than to complete the work outright.

The final assessment is a data analysis project you pick your own dataset, clean it, analyse it, and present it in a reproducible report using R and Quarto. It's applied rather than exam-style, so you learn by building something real rather than memorising content.

What’s important to know about the assessments is that each line of code requires an explanation, so make sure to explain everything you do, rather than come up with a complicated solution coding-wise. Easy to screw-up so attend consultations to make sure you’re on the right track.

No Exam.

If you like coding and want practical data skills (not just formulas), this unit is worth it. Being comfortable with R going in helps, but you pick up a lot of the wrangling and visualisation skills as you go.