VCE Data Analytics U3O1 Word Scramble: Artificial intelligence trends in data analytics and Testing databases, spreadsheets and visualisations (Modules 130–148)
Name:
Date:
Class:
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Word scramble
Unscramble the letters and write each key term on the line.
1
TNILU-BI IA ASFREEUT
Smart tools inside everyday software, like Copilot in Excel, that suggest formulas and charts, answer plain-English questions and find insights automatically.
2
OIUSTAISVNALI MPPROT
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.
3
GFNIINER A ORPMTP
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.
4
ECMINHA RGLNNAEI
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).
5
TACITASSTIL LMODE
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.
6
LEDOM
A ______ uses data to predict what will happen, support decisions and recommend options to users, such as suggested products.
7
BLTYELIIIAR
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.
8
LVUIAS TNEINCPSIO
Checking the appearance of a visualisation, such as layout, alignment, readability, colours and labels, often with a checklist to spot problems.
9
ATRCH IAICOFNVIERT
Comparing a chart's plotted values, category labels, totals and axis scale with the source data to confirm the chart shows the data accurately.
Answer Key
VCE Data Analytics U3O1 · Modules 130–148
1TNILU-BI IA ASFREEUT → built-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.
2OIUSTAISVNALI MPPROT → visualisation 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.
3GFNIINER A ORPMTP → Refining 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.
4ECMINHA RGLNNAEI → machine 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).
5TACITASSTIL LMODE → statistical 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.
6LEDOM → model — A model uses data to predict what will happen, support decisions and recommend options to users, such as suggested products.
7BLTYELIIIAR → reliability — 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.
8LVUIAS TNEINCPSIO → visual inspection — Checking the appearance of a visualisation, such as layout, alignment, readability, colours and labels, often with a checklist to spot problems.
9ATRCH IAICOFNVIERT → Chart 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.