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.
55m
Total time
4
Competency levels
12
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.
1
Awareness
- 3mWhat Data Literacy Is and Why Every Role Needs ItKnowledge
- 3mTypes of Data, Populations and SamplesKnowledge
- 7mAwareness Check: Data BasicsAssessment
2
Knowledge
- 3mDescribing Data in Plain TermsKnowledge
- 3mCorrelation Is Not CausationKnowledge
- 8mKnowledge Check: Summaries and Causal ClaimsAssessment
3
Skill
- 3mAsking a Good Question and Checking Data QualityKnowledge
- 3mTurn a Vague Request into an Answerable Data QuestionAssignment
- 8mSkill Check: Questions and Data QualityAssessment
4
Mastery
- 3mHow Data Misleads, and Using It EthicallyKnowledge
- 3mAudit a Decision-Making Report and Brief Its OwnerAssignment
- 8mMastery Check: Judgement with DataAssessment
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