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Data and AI

Data Analysis

Turn messy records into defensible summaries, visualizations, and decisions.

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Level
Beginner to intermediate
Subject area
Data and AI
Learning outcomes
4 focused outcomes

Topic overview

Learn the foundations of Data Analysis.

Good analysis begins before a chart is made. The learner must understand what each row represents, inspect missing or inconsistent values, choose comparisons, and communicate uncertainty.

Learning outcomes for Data Analysis

  1. 1

    Define observations, variables, and a focused question

  2. 2

    Clean common missing, duplicate, and inconsistent values

  3. 3

    Choose summaries and charts that match the data

  4. 4

    Explain findings, uncertainty, and limitations

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Noah Stein, Data science, statistics, and AI literacy tutor AI tutor

Noah Stein

Data science, statistics, and AI literacy tutor

Data stops being mysterious when every chart has a question behind it.

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Aisha Khan, Business, accounting, and workplace skills tutor AI tutor

Aisha Khan

Business, accounting, and workplace skills tutor

Clearer numbers, cleaner spreadsheets, and workplace writing that gets to the point.

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Caleb Mensah, Physics, chemistry, and scientific reasoning tutor AI tutor

Caleb Mensah

Physics, chemistry, and scientific reasoning tutor

Science becomes memorable when the evidence, model, and equation tell the same story.

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Ethan Cho, Software engineering and coding systems tutor AI tutor

Ethan Cho

Software engineering and coding systems tutor

Good code becomes easier to change when the data, decisions, and boundaries are visible.

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Suggested route

How to learn Data Analysis step by step.

01

Build the foundation

Define observations, variables, and a focused question

02

Practise with feedback

Clean common missing, duplicate, and inconsistent values

03

Practise with feedback

Choose summaries and charts that match the data

04

Apply and reflect

Explain findings, uncertainty, and limitations

Check and review

Practise and review Data Analysis.

Self-test

Data and AI literacy self-test

Review data quality, visualization, model training, evaluation, bias, privacy, and responsible use of generative AI.

About 7 minutes Take self-test

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