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

Общее время

Уровни компетенций

Уроки

Чему вы научитесь

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

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Awareness

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

Knowledge

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

Skill

Asking a Good Question and Checking Data Quality
Знания
3m
Turn a Vague Request into an Answerable Data Question
Задание
3m
Skill Check: Questions and Data Quality
Оценка
8m

Mastery

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

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