Data Literacy Fundamentals

Practical data literacy for industrial, energy and office roles: data types, populations and samples, descriptive statistics in plain language, correlation versus causation, data quality and the common ways numbers mislead.

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55m

Total time

Competency levels

Lessons

What you'll learn

Explain what data literacy is and why every role, technical or not, now depends on it
Classify data as categorical or numerical, discrete or continuous, and distinguish a population from a sample
Interpret means, medians and measures of spread, and recognise when an average misleads
Challenge causal claims made from correlated data using confounders and alternative explanations
Assess data against the accuracy, completeness, timeliness and consistency quality dimensions
Frame a precise question before requesting data, and handle personal data ethically and lawfully

Curriculum

Progress through each competency level in order.

Awareness

What Data Literacy Is and Why Every Role Needs It
Knowledge
3m
Types of Data, Populations and Samples
Knowledge
3m
Awareness Check: Data Basics
Assessment
7m

Knowledge

Describing Data in Plain Terms
Knowledge
3m
Correlation Is Not Causation
Knowledge
3m
Knowledge Check: Summaries and Causal Claims
Assessment
8m

Skill

Asking a Good Question and Checking Data Quality
Knowledge
3m
Turn a Vague Request into an Answerable Data Question
Assignment
3m
Skill Check: Questions and Data Quality
Assessment
8m

Mastery

How Data Misleads, and Using It Ethically
Knowledge
3m
Audit a Decision-Making Report and Brief Its Owner
Assignment
3m
Mastery Check: Judgement with Data
Assessment
8m

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