VCE Applied Computing Data Analytics study plan

Define data integrity, accuracy, validity, completeness and consistency, and classify five given data faults by which property each one breaks

Distinguish validation techniques (range, type, presence, consistency checks) from verification techniques, and match each to a realistic data-entry scenario

Map the stages of the data life cycle from acquisition through to disposal, and annotate where quality controls enter at each stage

Produce a one-page glossary of at least 20 domain-1 terms written in your own words with a worked example for each

Apply validation rules to a messy sample dataset and log every rejected or corrected record with a stated reason

Compare data cleaning, wrangling and transformation, and select the correct operation for ten described tasks

Write a step-by-step cleaning procedure for a dataset containing duplicate rows, inconsistent date formats and missing values

Justify when a missing value should be removed rather than imputed, using two contrasting examples

Classify ten data sources as primary or secondary and state one reliability strength and one weakness for each

Design a short survey or sensor-based collection plan that specifies sampling method, sample size rationale and collection frequency

Evaluate two secondary sources for currency, authority, accuracy and bias, and record the verdict in a comparison table

Identify three ways bias can enter a collection method and describe a mitigation for each

Select an appropriate chart type for eight described datasets and justify each choice in one sentence

Identify misleading visualisation practices such as truncated axes, inconsistent scales and cherry-picked ranges, and correct three examples

Draft a visualisation that follows accessibility principles including colour contrast, labelling and alt-text descriptions

Write a 150-word findings summary that separates what the data shows from what it suggests

Calculate and interpret mean, median, mode, range and outliers for a dataset, and explain which measure best suits a skewed distribution

Distinguish correlation from causation using two real-world examples and explain a confounding variable in each

Translate three data findings into concrete recommendations for a named decision-maker, stating the evidence behind each

Peer-review a classmate's or your own earlier visualisation and give three specific improvement notes

Summarise the obligations of relevant Australian privacy principles covering collection, use, disclosure and storage of personal data

Classify five scenarios as legal, ethical, both or neither, and justify each classification

Design a data security control set covering access control, encryption, backup and retention for a small analytics project

Write a plain-language consent and de-identification statement suitable for a survey participant

Build a project plan with scope, milestones, roles, dependencies and a risk log for a data analytics solution

Select a collaboration toolset and define version-control and file-naming conventions for a team of three

Run a structured review of a project plan against three success criteria and document the changes made

Draft a test plan that verifies the accuracy of at least three analytical outputs

Synthesise all domains into a single end-to-end analytics report from question to recommendation

Complete a timed set of mixed-domain practice questions and log every error by domain

Re-teach the three weakest sub-topics from memory without notes, then check against your reference sheets

Write a personal exam-day strategy covering question sequencing, time allocation and how to handle unfamiliar scenarios