VCE Data Analytics U3O1 Definitions Worksheet: Artificial intelligence trends in data analytics and Testing databases, spreadsheets and visualisations (Modules 130–148)
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Key terms
Write what each term means, in your own words.
1
built-in AI features
2
visualisation prompt
3
Refining a prompt
4
Check against source data
5
machine learning
6
statistical model
7
=FORECAST(x, known_ys, known_xs)
8
model
9
reliability
10
visual inspection
11
Chart verification
Answer Key
VCE Data Analytics U3O1 · Modules 130–148 · Student wording will vary for definitions
1built-in AI features — Smart tools inside everyday software, like Copilot in Excel, that suggest formulas and charts, answer plain-English questions and find insights automatically.
2visualisation prompt — An instruction to an AI tool that names the data fields, chart type, title, axis labels, colours and audience so it can build a clear, suitable chart.
3Refining a prompt — Reviewing the AI's chart, then sending a follow-up prompt that names exactly what to change, such as labels, chart type or detail, and repeating until it is right.
4Check against source data — Compare every value, label and axis scale in an AI-generated chart with the original data, and look for invented figures, before you trust or use the chart.
5machine learning — Training a model on past data so it learns patterns and can predict outcomes for new data, such as a category (classification) or a number (numeric prediction).
6statistical model — A rule built from past data, like a regression line, that uses the relationship between variables to estimate new values. A strong r makes the estimates more reliable.
7=FORECAST(x, known_ys, known_xs) — Estimates a new y value for a given x by extending the trend of existing data, e.g. predicting next year's sales. It is less reliable the further x is beyond the known data.
8model — A model uses data to predict what will happen, support decisions and recommend options to users, such as suggested products.
9reliability — How much you can trust a prediction. It drops with small samples, outliers and predicting far beyond your data, so always state how uncertain the prediction is.
10visual inspection — Checking the appearance of a visualisation, such as layout, alignment, readability, colours and labels, often with a checklist to spot problems.
11Chart verification — Comparing a chart's plotted values, category labels, totals and axis scale with the source data to confirm the chart shows the data accurately.